Improving Accuracy in Arrhenius Models of Cell Death: Adding a Temperature-Dependent Time Delay.

PubMed

Pearce, John A

2015-12-01

The Arrhenius formulation for single-step irreversible unimolecular reactions has been used for many decades to describe the thermal damage and cell death processes. Arrhenius predictions are acceptably accurate for structural proteins, for some cell death assays, and for cell death at higher temperatures in most cell lines, above about 55 °C. However, in many cases--and particularly at hyperthermic temperatures, between about 43 and 55 °C--the particular intrinsic cell death or damage process under study exhibits a significant "shoulder" region that constant-rate Arrhenius models are unable to represent with acceptable accuracy. The primary limitation is that Arrhenius calculations always overestimate the cell death fraction, which leads to severely overoptimistic predictions of heating effectiveness in tumor treatment. Several more sophisticated mathematical model approaches have been suggested and show much-improved performance. But simpler models that have adequate accuracy would provide useful and practical alternatives to intricate biochemical analyses. Typical transient intrinsic cell death processes at hyperthermic temperatures consist of a slowly developing shoulder region followed by an essentially constant-rate region. The shoulder regions have been demonstrated to arise chiefly from complex functional protein signaling cascades that generate delays in the onset of the constant-rate region, but may involve heat shock protein activity as well. This paper shows that acceptably accurate and much-improved predictions in the simpler Arrhenius models can be obtained by adding a temperature-dependent time delay. Kinetic coefficients and the appropriate time delay are obtained from the constant-rate regions of the measured survival curves. The resulting predictions are seen to provide acceptably accurate results while not overestimating cell death. The method can be relatively easily incorporated into numerical models. Additionally, evidence is presented to support the application of compensation law behavior to the cell death processes--that is, the strong correlation between the kinetic coefficients, ln{A} and E(a), is confirmed.

Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

PubMed Central

Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2012-01-01

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0. PMID:23300419

Improved methods for predicting peptide binding affinity to MHC class II molecules.

PubMed

Jensen, Kamilla Kjaergaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

2018-07-01

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2. © 2018 John Wiley & Sons Ltd.

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions.

PubMed

Cheung, C Y Maurice; Williams, Thomas C R; Poolman, Mark G; Fell, David A; Ratcliffe, R George; Sweetlove, Lee J

2013-09-01

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance. © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

Annealing characteristics of irradiated hydrogenated amorphous silicon solar cells

NASA Technical Reports Server (NTRS)

Payson, J. S.; Abdulaziz, S.; Li, Y.; Woodyard, J. R.

1991-01-01

It was shown that 1 MeV proton irradiation with fluences of 1.25E14 and 1.25E15/sq cm reduces the normalized I(sub SC) of a-Si:H solar cell. Solar cells recently fabricated showed superior radiation tolerance compared with cells fabricated four years ago; the improvement is probably due to the fact that the new cells are thinner and fabricated from improved materials. Room temperature annealing was observed for the first time in both new and old cells. New cells anneal at a faster rate than old cells for the same fluence. From the annealing work it is apparent that there are at least two types of defects and/or annealing mechanisms. One cell had improved I-V characteristics following irradiation as compared to the virgin cell. The work shows that the photothermal deflection spectroscopy (PDS) and annealing measurements may be used to predict the qualitative behavior of a-Si:H solar cells. It was anticipated that the modeling work will quantitatively link thin film measurements with solar cell properties. Quantitative predictions of the operation of a-Si:H solar cells in a space environment will require a knowledge of the defect creation mechanisms, defect structures, role of defects on degradation, and defect passivation and annealing mechanisms. The engineering data and knowledge base for justifying space flight testing of a-Si:H alloy based solar cells is being developed.

Precision medicine and precision therapeutics: hedgehog signaling pathway, basal cell carcinoma and beyond.

PubMed

Mohan, Shalini V; Chang, Anne Lynn S

2014-06-01

Precision medicine and precision therapeutics is currently in its infancy with tremendous potential to improve patient care by better identifying individuals at risk for skin cancer and predict tumor responses to treatment. This review focuses on the Hedgehog signaling pathway, its critical role in the pathogenesis of basal cell carcinoma, and the emergence of targeted treatments for advanced basal cell carcinoma. Opportunities to utilize precision medicine are outlined, such as molecular profiling to predict basal cell carcinoma response to targeted therapy and to inform therapeutic decisions.

Limited improvement of incorporating primary circulating prostate cells with the CAPRA score to predict biochemical failure-free outcome of radical prostatectomy for prostate cancer.

PubMed

Murray, Nigel P; Aedo, Socrates; Fuentealba, Cynthia; Jacob, Omar; Reyes, Eduardo; Novoa, Camilo; Orellana, Sebastian; Orellana, Nelson

2016-10-01

To establish a prediction model for early biochemical failure based on the Cancer of the Prostate Risk Assessment (CAPRA) score, the presence or absence of primary circulating prostate cells (CPC) and the number of primary CPC (nCPC)/8ml blood sample is detected before surgery. A prospective single-center study of men who underwent radical prostatectomy as monotherapy for prostate cancer. Clinical-pathological findings were used to calculate the CAPRA score. Before surgery blood was taken for CPC detection, mononuclear cells were obtained using differential gel centrifugation, and CPCs identified using immunocytochemistry. A CPC was defined as a cell expressing prostate-specific antigen and P504S, and the presence or absence of CPCs and the number of cells detected/8ml blood sample was registered. Patients were followed up for up to 5 years; biochemical failure was defined as a prostate-specific antigen>0.2ng/ml. The validity of the CAPRA score was calibrated using partial validation, and the fractional polynomial Cox proportional hazard regression was used to build 3 models, which underwent a decision analysis curve to determine the predictive value of the 3 models with respect to biochemical failure. A total of 267 men participated, mean age 65.80 years, and after 5 years of follow-up the biochemical-free survival was 67.42%. The model using CAPRA score showed a hazards ratio (HR) of 5.76 between low and high-risk groups, that of CPC with a HR of 26.84 between positive and negative groups, and the combined model showed a HR of 4.16 for CAPRA score and 19.93 for CPC. Using the continuous variable nCPC, there was no improvement in the predictive value of the model compared with the model using a positive-negative result of CPC detection. The combined CAPRA-nCPC model showed an improvement of the predictive performance for biochemical failure using the Harrell׳s C concordance test and a net benefit on DCA in comparison with either model used separately. The use of primary CPC as a predictive factor based on their presence or absence did not predict aggressive disease or biochemical failure. Although the use of a combined CAPRA-nCPC model improves the prediction of biochemical failure in patients undergoing radical prostatectomy for prostate cancer, this is minimal. The use of the presence or absence of primary CPCs alone did not predict aggressive disease or biochemical failure. Copyright © 2016 Elsevier Inc. All rights reserved.

Improved Time-Lapsed Angular Scattering Microscopy of Single Cells

NASA Astrophysics Data System (ADS)

Cannaday, Ashley E.

By measuring angular scattering patterns from biological samples and fitting them with a Mie theory model, one can estimate the organelle size distribution within many cells. Quantitative organelle sizing of ensembles of cells using this method has been well established. Our goal is to develop the methodology to extend this approach to the single cell level, measuring the angular scattering at multiple time points and estimating the non-nuclear organelle size distribution parameters. The diameters of individual organelle-size beads were successfully extracted using scattering measurements with a minimum deflection angle of 20 degrees. However, the accuracy of size estimates can be limited by the angular range detected. In particular, simulations by our group suggest that, for cell organelle populations with a broader size distribution, the accuracy of size prediction improves substantially if the minimum angle of detection angle is 15 degrees or less. The system was therefore modified to collect scattering angles down to 10 degrees. To confirm experimentally that size predictions will become more stable when lower scattering angles are detected, initial validations were performed on individual polystyrene beads ranging in diameter from 1 to 5 microns. We found that the lower minimum angle enabled the width of this delta-function size distribution to be predicted more accurately. Scattering patterns were then acquired and analyzed from single mouse squamous cell carcinoma cells at multiple time points. The scattering patterns exhibit angular dependencies that look unlike those of any single sphere size, but are well-fit by a broad distribution of sizes, as expected. To determine the fluctuation level in the estimated size distribution due to measurement imperfections alone, formaldehyde-fixed cells were measured. Subsequent measurements on live (non-fixed) cells revealed an order of magnitude greater fluctuation in the estimated sizes compared to fixed cells. With our improved and better-understood approach to single cell angular scattering, we are now capable of reliably detecting changes in organelle size predictions due to biological causes above our measurement error of 20 nm, which enables us to apply our system to future studies of the investigation of various single cell biological processes.

ELECTROCHEMISTRY AND ON-CELL REFORMATION MODELING FOR SOLID OXIDE FUEL CELL STACKS

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

Recknagle, Kurtis P.; Jarboe, Daniel T.; Johnson, Kenneth I.

2007-01-16

ABSTRACT Providing adequate and efficient cooling schemes for solid-oxide-fuel-cell (SOFC) stacks continues to be a challenge coincident with the development of larger, more powerful stacks. The endothermic steam-methane reformation reaction can provide cooling and improved system efficiency when performed directly on the electrochemically active anode. Rapid kinetics of the endothermic reaction typically causes a localized temperature depression on the anode near the fuel inlet. It is desirable to extend the endothermic effect over more of the cell area and mitigate the associated differences in temperature on the cell to alleviate subsequent thermal stresses. In this study, modeling tools validated formore » the prediction of fuel use, on-cell methane reforming, and the distribution of temperature within SOFC stacks, are employed to provide direction for modifying the catalytic activity of anode materials to control the methane conversion rate. Improvements in thermal management that can be achieved through on-cell reforming is predicted and discussed. Two operating scenarios are considered: one in which the methane fuel is fully pre-reformed, and another in which a substantial percentage of the methane is reformed on-cell. For the latter, a range of catalytic activity is considered and the predicted thermal effects on the cell are presented. Simulations of the cell electrochemical and thermal performance with and without on-cell reforming, including structural analyses, show a substantial decrease in thermal stresses for an on-cell reforming case with slowed methane conversion.« less

Gallium arsenide solar cell efficiency: Problems and potential

NASA Technical Reports Server (NTRS)

Weizer, V. G.; Godlewski, M. P.

1985-01-01

Under ideal conditions the GaAs solar cell should be able to operate at an AMO efficiency exceeding 27 percent, whereas to date the best measured efficiencies barely exceed 19 percent. Of more concern is the fact that there has been no improvement in the past half decade, despite the expenditure of considerable effort. State-of-the-art GaAs efficiency is analyzed in an attempt to determine the feasibility of improving on the status quo. The possible gains to be had in the planar cell. An attempt is also made to predict the efficiency levels that could be achieved with a grating geometry. Both the N-base and the P-base BaAs cells in their planar configurations have the potential to operate at AMO efficiencies between 23 and 24 percent. For the former the enabling technology is essentially in hand, while for the latter the problem of passivating the emitter surface remains to be solved. In the dot grating configuration, P-base efficiencies approaching 26 percent are possible with minor improvements in existing technology. N-base grating cell efficiencies comparable to those predicted for the P-base cell are achievable if the N surface can be sufficiently passivated.

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity

PubMed Central

Zhu, Hao; Rusyn, Ivan; Richard, Ann; Tropsha, Alexander

2008-01-01

Background To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem. Objectives We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents. Methods and results Initially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors. Conclusions Our studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology. PMID:18414635

An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

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

Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula

2009-06-01

Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac,more » tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC{sub 50} values 100 {mu}M or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.« less

Rationally optimized cryopreservation of multiple mouse embryonic stem cell lines: I--Comparative fundamental cryobiology of multiple mouse embryonic stem cell lines and the implications for embryonic stem cell cryopreservation protocols.

PubMed

Kashuba, Corinna M; Benson, James D; Critser, John K

2014-04-01

The post-thaw recovery of mouse embryonic stem cells (mESCs) is often assumed to be adequate with current methods. However as this publication will show, this recovery of viable cells actually varies significantly by genetic background. Therefore there is a need to improve the efficiency and reduce the variability of current mESC cryopreservation methods. To address this need, we employed the principles of fundamental cryobiology to improve the cryopreservation protocol of four mESC lines from different genetic backgrounds (BALB/c, CBA, FVB, and 129R1 mESCs) through a comparative study characterizing the membrane permeability characteristics and membrane integrity osmotic tolerance limits of each cell line. In the companion paper, these values were used to predict optimal cryoprotectants, cooling rates, warming rates, and plunge temperatures, and then these predicted optimal protocols were validated against standard freezing protocols. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach.

PubMed

Zhou, Bin; Sun, Qi; Kong, De-Xin

2016-05-31

In this study, we proposed an improved algorithm for identifying proteins relevant to cancer. The algorithm was named two-layer molecular similarity ensemble approach (TL-SEA). We applied TL-SEA to analyzing the correlation between anticancer compounds (against cell lines K562, MCF7 and A549) and active compounds against separate target proteins listed in BindingDB. Several associations between cancer types and related proteins were revealed using this chemoinformatics approach. An analysis of the literature showed that 26 of 35 predicted proteins were correlated with cancer cell proliferation, apoptosis or differentiation. Additionally, interactions between proteins in BindingDB and anticancer chemicals were also predicted. We discuss the roles of the most important predicted proteins in cancer biology and conclude that TL-SEA could be a useful tool for inferring novel proteins involved in cancer and revealing underlying molecular mechanisms.

Drug screening in 3D in vitro tumor models: overcoming current pitfalls of efficacy read-outs.

PubMed

Santo, Vítor E; Rebelo, Sofia P; Estrada, Marta F; Alves, Paula M; Boghaert, Erwin; Brito, Catarina

2017-01-01

There is cumulating evidence that in vitro 3D tumor models with increased physiological relevance can improve the predictive value of pre-clinical research and ultimately contribute to achieve decisions earlier during the development of cancer-targeted therapies. Due to the role of tumor microenvironment in the response of tumor cells to therapeutics, the incorporation of different elements of the tumor niche on cell model design is expected to contribute to the establishment of more predictive in vitro tumor models. This review is focused on the several challenges and adjustments that the field of oncology research is facing to translate these advanced tumor cells models to drug discovery, taking advantage of the progress on culture technologies, imaging platforms, high throughput and automated systems. The choice of 3D cell model, the experimental design, choice of read-outs and interpretation of data obtained from 3D cell models are critical aspects when considering their implementation in drug discovery. In this review, we foresee some of these aspects and depict the potential directions of pre-clinical oncology drug discovery towards improved prediction of drug efficacy. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Potassium Beta-Alumina/Molybdenum/Potassium Electrochemical Cells

NASA Technical Reports Server (NTRS)

Williams, R.; Kisor, A.; Ryan, M.; Nakamura, B.; Kikert, S.; O'Connor, D.

1994-01-01

potassium alkali metal thermal-to-electric converter (K-AMTEC) cells utilizing potassium beta alumina solid electrolyte (K-BASE) are predicted to have improved properties for thermal to electric conversion at somewhat lower temperatures than sodium AMTEC's.

Low-high junction theory applied to solar cells

NASA Technical Reports Server (NTRS)

Godlewski, M. P.; Baraona, C. R.; Brandhorst, H. W., Jr.

1974-01-01

Recent use of alloying techniques for rear contact formation has yielded a new kind of silicon solar cell, the back surface field (BSF) cell, with abnormally high open-circuit voltage and improved radiation resistance. Several analytical models for open-circuit voltage based on the reverse saturation current are formulated to explain these observations. The zero surface recombination velocity (SRV) case of the conventional cell model, the drift field model, and the low-high junction (LHJ) model can predict the experimental trends. The LHJ model applies the theory of the low-high junction and is considered to reflect a more realistic view of cell fabrication. This model can predict the experimental trends observed for BSF cells.

Predictors of Cell Phone Use in Distracted Driving: Extending the Theory of Planned Behavior.

PubMed

Tian, Yan; Robinson, James D

2017-09-01

This study examines the predictors of six distracted driving behaviors, and the survey data partially support Ajzen's (1991) Theory of Planned Behavior (TPB). The data suggest that the attitude variable predicted intention to engage in all six distracted driving behaviors (reading and sending text messages, making and answering cell phone calls, reading/viewing social media, and posting on social media while driving). Extending the model to include past experience and the variable perceived safety of technology yielded an improvement in the prediction of the distraction variables. Specifically, past experience predicted all six distracted driving behaviors, and the variable perceived safety of technology predicted intentions to read/view social media and intention to post on social media while driving. The study provides evidence for the importance of incorporating expanded variables into the original TPB model to predict cell phone use behaviors while driving, and it suggests that it is essential to tailor campaign materials for each specific cell phone use behavior to reduce distracted driving.

Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/. Copyright © 2016 Elsevier B.V. All rights reserved.

Stem cell-derived models to improve mechanistic understanding and prediction of human drug-induced liver injury.

PubMed

Goldring, Christopher; Antoine, Daniel J; Bonner, Frank; Crozier, Jonathan; Denning, Chris; Fontana, Robert J; Hanley, Neil A; Hay, David C; Ingelman-Sundberg, Magnus; Juhila, Satu; Kitteringham, Neil; Silva-Lima, Beatriz; Norris, Alan; Pridgeon, Chris; Ross, James A; Young, Rowena Sison; Tagle, Danilo; Tornesi, Belen; van de Water, Bob; Weaver, Richard J; Zhang, Fang; Park, B Kevin

2017-02-01

Current preclinical drug testing does not predict some forms of adverse drug reactions in humans. Efforts at improving predictability of drug-induced tissue injury in humans include using stem cell technology to generate human cells for screening for adverse effects of drugs in humans. The advent of induced pluripotent stem cells means that it may ultimately be possible to develop personalized toxicology to determine interindividual susceptibility to adverse drug reactions. However, the complexity of idiosyncratic drug-induced liver injury means that no current single-cell model, whether of primary liver tissue origin, from liver cell lines, or derived from stem cells, adequately emulates what is believed to occur during human drug-induced liver injury. Nevertheless, a single-cell model of a human hepatocyte which emulates key features of a hepatocyte is likely to be valuable in assessing potential chemical risk; furthermore, understanding how to generate a relevant hepatocyte will also be critical to efforts to build complex multicellular models of the liver. Currently, hepatocyte-like cells differentiated from stem cells still fall short of recapitulating the full mature hepatocellular phenotype. Therefore, we convened a number of experts from the areas of preclinical and clinical hepatotoxicity and safety assessment, from industry, academia, and regulatory bodies, to specifically explore the application of stem cells in hepatotoxicity safety assessment and to make recommendations for the way forward. In this short review, we particularly discuss the importance of benchmarking stem cell-derived hepatocyte-like cells to their terminally differentiated human counterparts using defined phenotyping, to make sure the cells are relevant and comparable between labs, and outline why this process is essential before the cells are introduced into chemical safety assessment. (Hepatology 2017;65:710-721). © 2016 by the American Association for the Study of Liver Diseases.

Transcription Factor Binding Profiles Reveal Cyclic Expression of Human Protein-coding Genes and Non-coding RNAs

PubMed Central

Cheng, Chao; Ung, Matthew; Grant, Gavin D.; Whitfield, Michael L.

2013-01-01

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements. PMID:23874175

Computational Modeling in Concert with Laboratory Studies: Application to B Cell Differentiation

EPA Science Inventory

Remediation is expensive, so accurate prediction of dose-response is important to help control costs. Dose response is a function of biological mechanisms. Computational models of these mechanisms improve the efficiency of research and provide the capability for prediction.

BEST: Improved Prediction of B-Cell Epitopes from Antigen Sequences

PubMed Central

Gao, Jianzhao; Faraggi, Eshel; Zhou, Yaoqi; Ruan, Jishou; Kurgan, Lukasz

2012-01-01

Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods. PMID:22761950

Computational Model of Secondary Palate Fusion and Disruption

EPA Science Inventory

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks...

OAO battery data analysis

NASA Technical Reports Server (NTRS)

Gaston, S.; Wertheim, M.; Orourke, J. A.

1973-01-01

Summary, consolidation and analysis of specifications, manufacturing process and test controls, and performance results for OAO-2 and OAO-3 lot 20 Amp-Hr sealed nickel cadmium cells and batteries are reported. Correlation of improvements in control requirements with performance is a key feature. Updates for a cell/battery computer model to improve performance prediction capability are included. Applicability of regression analysis computer techniques to relate process controls to performance is checked.

Prediction of essential proteins based on gene expression programming.

PubMed

Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi

2013-01-01

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

Improve the prediction of RNA-binding residues using structural neighbours.

PubMed

Li, Quan; Cao, Zanxia; Liu, Haiyan

2010-03-01

The interactions between RNA-binding proteins (RBPs) with RNA play key roles in managing some of the cell's basic functions. The identification and prediction of RNA binding sites is important for understanding the RNA-binding mechanism. Computational approaches are being developed to predict RNA-binding residues based on the sequence- or structure-derived features. To achieve higher prediction accuracy, improvements on current prediction methods are necessary. We identified that the structural neighbors of RNA-binding and non-RNA-binding residues have different amino acid compositions. Combining this structure-derived feature with evolutionary (PSSM) and other structural information (secondary structure and solvent accessibility) significantly improves the predictions over existing methods. Using a multiple linear regression approach and 6-fold cross validation, our best model can achieve an overall correct rate of 87.8% and MCC of 0.47, with a specificity of 93.4%, correctly predict 52.4% of the RNA-binding residues for a dataset containing 107 non-homologous RNA-binding proteins. Compared with existing methods, including the amino acid compositions of structure neighbors lead to clearly improvement. A web server was developed for predicting RNA binding residues in a protein sequence (or structure),which is available at http://mcgill.3322.org/RNA/.

A redefinition of the representation of mammary cells and enzyme activities in a lactating dairy cow model.

PubMed

Hanigan, M D; Rius, A G; Kolver, E S; Palliser, C C

2007-08-01

The Molly model predicts various aspects of digestion and metabolism in the cow, including nutrient partitioning between milk and body stores. It has been observed previously that the model underpredicts milk component yield responses to nutrition and consequently overpredicts body energy store responses. In Molly, mammary enzyme activity is represented as an aggregate of mammary cell numbers and activity per cell with minimal endocrine regulation. Work by others suggests that mammary cells can cycle between active and quiescent states in response to various stimuli. Simple models of milk production have demonstrated the utility of this representation when using the model to simulate variable milking and nutrient restriction. It was hypothesized that replacing the current representation of mammary cells and enzyme activity in Molly with a representation of active and quiescent cells and improving the representation of endocrine control of cell activity would improve predictions of milk component yield. The static representation of cell numbers was replaced with a representation of cell growth during gestation and early lactation periods and first-order cell death. Enzyme capacity for fat and protein synthesis was assumed to be proportional to cell numbers. Enzyme capacity for lactose synthesis was represented with the same equation form as for cell numbers. Data used for parameter estimation were collected as part of an extended lactation trial. Cows with North American or New Zealand genotypes were fed 0, 3, or 6 kg of concentrate dry matter daily during a 600-d lactation. The original model had root mean square prediction errors of 17.7, 22.3, and 19.8% for lactose, protein, and fat yield, respectively, as compared with values of 8.3, 9.4, and 11.7% for the revised model, respectively. The original model predicted body weight with an error of 19.7% vs. 5.7% for the revised model. Based on these observations, it was concluded that representing mammary synthetic capacity as a function of active cell numbers and revisions to endocrine control of cell activity was meritorious.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

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

He, Yupeng; Gorkin, David U.; Dickel, Diane E.

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

DOE PAGES

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; ...

2017-02-13

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference.

PubMed

Trolle, Thomas; McMurtrey, Curtis P; Sidney, John; Bardet, Wilfried; Osborn, Sean C; Kaever, Thomas; Sette, Alessandro; Hildebrand, William H; Nielsen, Morten; Peters, Bjoern

2016-02-15

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8(+) T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8(+) T cell epitopes. Copyright © 2016 by The American Association of Immunologists, Inc.

Expression of tolerance associated gene-1, a mitochondrial protein inhibiting T cell activation, can be used to predict response to immune modulating therapies.

PubMed

Keeren, Kathrin; Friedrich, Markus; Gebuhr, Inga; Philipp, Sandra; Sabat, Robert; Sterry, Wolfram; Brandt, Christine; Meisel, Christian; Grütz, Gerald; Volk, Hans-Dieter; Sawitzki, Birgit

2009-09-15

Immune modulating therapies gain increasing importance in treatment of patients with autoimmune diseases such as psoriasis. None of the currently applied biologics achieves significant clinical improvement in all treated patients. Because the therapy with biologics is cost intensive and sometimes associated with side effects, noninvasive diagnostic tools for early prediction of responders are of major interest. We studied the effects of Alefacept (LFA3Ig), an approved drug for treatment of psoriasis, on leukocytes in vitro and in vivo to identify gene markers predictive for treatment response and to further investigate its molecular mechanisms of action. In an open-label study, 20 psoriasis patients were treated weekly with 15 mg Alefacept over 12 wk. We demonstrate that transcription of the tolerance-associated gene (TOAG-1) is significantly up-regulated whereas receptor for hyaluronic acid mediated migration (RHAMM) transcription is down-regulated in PBMCs of responding patients before clinical improvement. TOAG-1 is exclusively localized within mitochondria. Overexpression of TOAG-1 in murine T cells leads to increased susceptibility to apoptosis. Addition of Alefacept to stimulated human T cells in vitro resulted in reduced frequencies of activated CD137(+) cells, increased TOAG-1 but reduced RHAMM expression. This was accompanied by reduced proliferation and enhanced apoptosis. Inhibition of proliferation was dependent on enhanced PDL1 expression of APCs. Thus, peripheral changes of TOAG-1 and RHAMM expression can be used to predict clinical response to Alefacept treatment in psoriasis patients. In the presence of APCs Alefacept can inhibit T cell activation and survival by increasing expression of TOAG-1 on T cells and PDL1 on APCs.

Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds

PubMed Central

Huebinger, Ryan M.; Keen, Emma

2018-01-01

As the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the host cells and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model. PMID:29723210

Development and test fuel cell powered on-site integrated total energy systems. Phase 3: Full-scale power plant development

NASA Technical Reports Server (NTRS)

Kaufman, A.

1982-01-01

The on-site system application analysis is summarized. Preparations were completed for the first test of a full-sized single cell. Emphasis of the methanol fuel processor development program shifted toward the use of commercial shell-and-tube heat exchangers. An improved method for predicting the carbon-monoxide tolerance of anode catalysts is described. Other stack support areas reported include improved ABA bipolar plate bonding technology, improved electrical measurement techniques for specification-testing of stack components, and anodic corrosion behavior of carbon materials.

Dissecting engineered cell types and enhancing cell fate conversion via CellNet

PubMed Central

Morris, Samantha A.; Cahan, Patrick; Li, Hu; Zhao, Anna M.; San Roman, Adrianna K.; Shivdasani, Ramesh A.; Collins, James J.; Daley, George Q.

2014-01-01

SUMMARY Engineering clinically relevant cells in vitro holds promise for regenerative medicine, but most protocols fail to faithfully recapitulate target cell properties. To address this, we developed CellNet, a network biology platform that determines whether engineered cells are equivalent to their target tissues, diagnoses aberrant gene regulatory networks, and prioritizes candidate transcriptional regulators to enhance engineered conversions. Using CellNet, we improved B cell to macrophage conversion, transcriptionally and functionally, by knocking down predicted B cell regulators. Analyzing conversion of fibroblasts to induced hepatocytes (iHeps), CellNet revealed an unexpected intestinal program regulated by the master regulator Cdx2. We observed long-term functional engraftment of mouse colon by iHeps, thereby establishing their broader potential as endoderm progenitors and demonstrating direct conversion of fibroblasts into intestinal epithelium. Our studies illustrate how CellNet can be employed to improve direct conversion and to uncover unappreciated properties of engineered cells. PMID:25126792

The Significance of the PD-L1 Expression in Non-Small-Cell Lung Cancer: Trenchant Double Swords as Predictive and Prognostic Markers.

PubMed

Takada, Kazuki; Toyokawa, Gouji; Shoji, Fumihiro; Okamoto, Tatsuro; Maehara, Yoshihiko

2018-03-01

Lung cancer is the leading cause of death due to cancer worldwide. Surgery, chemotherapy, and radiotherapy have been the standard treatment for lung cancer, and targeted molecular therapy has greatly improved the clinical course of patients with non-small-cell lung cancer (NSCLC) harboring driver mutations, such as in epidermal growth factor receptor and anaplastic lymphoma kinase genes. Despite advances in such therapies, the prognosis of patients with NSCLC without driver oncogene mutations remains poor. Immunotherapy targeting programmed cell death-1 (PD-1) and programmed cell death-ligand 1 (PD-L1) has recently been shown to improve the survival in advanced NSCLC. The PD-L1 expression on the surface of tumor cells has emerged as a potential biomarker for predicting responses to immunotherapy and prognosis after surgery in NSCLC. However, the utility of PD-L1 expression as a predictive and prognostic biomarker remains controversial because of the existence of various PD-L1 antibodies, scoring systems, and positivity cutoffs. In this review, we summarize the data from representative clinical trials of PD-1/PD-L1 immune checkpoint inhibitors in NSCLC and previous reports on the association between PD-L1 expression and clinical outcomes in patients with NSCLC. Furthermore, we discuss the future perspectives of immunotherapy and immune checkpoint factors. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation and comparison of the ability of online available prediction programs to predict true linear B-cell epitopes.

PubMed

Costa, Juan G; Faccendini, Pablo L; Sferco, Silvano J; Lagier, Claudia M; Marcipar, Iván S

2013-06-01

This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.

Improved High/Low Junction Silicon Solar Cell

NASA Technical Reports Server (NTRS)

Neugroschel, A.; Pao, S. C.; Lindholm, F. A.; Fossum, J. G.

1986-01-01

Method developed to raise value of open-circuit voltage in silicon solar cells by incorporating high/low junction in cell emitter. Power-conversion efficiency of low-resistivity silicon solar cell considerably less than maximum theoretical value mainly because open-circuit voltage is smaller than simple p/n junction theory predicts. With this method, air-mass-zero opencircuit voltage increased from 600 mV level to approximately 650 mV.

Is the Phone Mightier than the Sword? Cell Phones and Insurgent Violence in Iraq

DTIC Science & Technology

2012-09-03

Does improved communication as provided by modern cell phone technology affect the production of violence during insurgencies? A priori predictions... phone communications on conflict using data on Iraq’s cell phone network and event data on violence. We show that increased mobile communications...with counterinsurgents, and it creates passive signals intelligence collection opportunities. We provide the first systematic test of the effect of cell

Mechanistic Computational Model of Steroidgenesis in H295R Cells: Role of (Oxysterols and Cell Proliferation to Improve Predictability of Biochemical Response to Endocrine Active Chemical-Metyrapone

EPA Science Inventory

The human adrenocortical carcinoma cell line H295R is being used as an in vitro steroidogenesis screening assay to assess the impact of endocrine active chemicals (EACs) capable of altering steroid biosynthesis. To enhance the interpretation and quantitative application of measur...

Simultaneous fermentation and separation in an immobilized cell trickle bed reactor: Acetone-butanol-ethane (ABE) and ethanol fermentation

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

Park, C.H.

1989-01-01

A novel process employing immobilized cells and in-situ product removal was studied for acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum and ethanol fermentation by Saccharomyces cerevisiae. Experimental studies of ABE fermentation in a trickle bed reactor without product separation showed that solvent production could be improved by one order of magnitude compared to conventional batch fermentation. Control of effluent pH near 4.3 and feed glucose concentrations higher than 10 g/L were the necessary conditions for cell growth and solvent production. A mathematical model using an equilibrium staged model predicted efficient separation of butanol from the fermentation broth. Activity coefficients of multicomponentmore » system were estimated by Wilson's equation or the ASOG method. Inhibition by butanol and organic acids was incorporated into the kinetic expression. Experimental performance of simultaneous fermentation and separation in an immobilized cell trickle bed reactor showed that glucose conversion was improved as predicted by mathematical modeling and analysis. The effect of pH and temperature on ethanol fermentation by Saccharomyces cerevisiae was studied in free and immobilized cell reactors. Conditions for the highest glucose conversion, cell viability and least glycerol yield were determined.« less

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

PubMed Central

Biehl, Michael; Sadowski, Peter; Bhanot, Gyan; Bilal, Erhan; Dayarian, Adel; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Zeller, Michael D.; Hormoz, Sahand

2015-01-01

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. Contact: meikelbiehl@gmail.com PMID:24994890

A mathematical model of a lithium/thionyl chloride primary cell

NASA Technical Reports Server (NTRS)

Evans, T. I.; Nguyen, T. V.; White, R. E.

1987-01-01

A 1-D mathematical model for the lithium/thionyl chloride primary cell was developed to investigate methods of improving its performance and safety. The model includes many of the components of a typical lithium/thionyl chloride cell such as the porous lithium chloride film which forms on the lithium anode surface. The governing equations are formulated from fundamental conservation laws using porous electrode theory and concentrated solution theory. The model is used to predict 1-D, time dependent profiles of concentration, porosity, current, and potential as well as cell temperature and voltage. When a certain discharge rate is required, the model can be used to determine the design criteria and operating variables which yield high cell capacities. Model predictions can be used to establish operational and design limits within which the thermal runaway problem, inherent in these cells, can be avoided.

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction*

PubMed Central

Rahman, Kh. Shamsur; Chowdhury, Erfan Ullah; Sachse, Konrad; Kaltenboeck, Bernhard

2016-01-01

X-ray crystallography has shown that an antibody paratope typically binds 15–22 amino acids (aa) of an epitope, of which 2–5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6–11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7–12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, ≤11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16–30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences. PMID:27189949

Low-high junction theory applied to solar cells

NASA Technical Reports Server (NTRS)

Godlewski, M. P.; Baraona, C. R.; Brandhorst, H. W., Jr.

1973-01-01

Recent use of alloying techniques for rear contact formation has yielded a new kind of silicon solar cell, the back surface field (BSF) cell, with abnormally high open circuit voltage and improved radiation resistance. Several analytical models for open circuit voltage based on the reverse saturation current are formulated to explain these observations. The zero SRV case of the conventional cell model, the drift field model, and the low-high junction (LHJ) model can predict the experimental trends. The LHJ model applies the theory of the low-high junction and is considered to reflect a more realistic view of cell fabrication. This model can predict the experimental trends observed for BSF cells. Detailed descriptions and derivations for the models are included. The correspondences between them are discussed. This modeling suggests that the meaning of minority carrier diffusion length measured in BSF cells be reexamined.

Qualification testing of secondary sterilizable silver-zinc cells for use in the Jupiter atmospheric entry probe

NASA Technical Reports Server (NTRS)

Manzo, M. A.

1981-01-01

A series of qualification tests were run on the secondary, sterilizable silver oxide - zinc cell developed at the NASA Lewis Research Center to determine if the cell was capable of providing mission power requirements for the Jupiter atmospheric entry probe. The cells were tested for their ability to survive radiation at the levels predicted for the Jovian atmosphere with no loss of performance. Cell performance was evaluated under various temperature and loading conditions, and the cells were tested under various environmental conditions related to launch and to deceleration into the Jovian atmosphere. The cell performed acceptably except under the required loading at low temperatures. The cell was redesigned to improve low-temperature performance and energy density. The modified cells improved performance at all temperatures. Results of testing cells of both the original and modified designs are discussed.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

Detection of FAM172A expressed in circulating tumor cells is a feasible method to predict high-risk subgroups of colorectal cancer.

PubMed

Cui, Chun-Hui; Chen, Ri-Hong; Zhai, Duan-Yang; Xie, Lang; Qi, Jia; Yu, Jin-Long

2017-06-01

Previous studies used to enumerate circulating tumor cells to predict prognosis and therapeutic effect of colorectal cancer. However, increasing studies have shown that only circulating tumor cells enumeration was not enough to reflect the heterogeneous condition of tumor. In this study, we classified different metastatic-potential circulating tumor cells from colorectal cancer patients and measured FAM172A expression in circulating tumor cells to improve accuracy of clinical diagnosis and treatment of colorectal cancer. Blood samples were collected from 45 primary colorectal cancer patients. Circulating tumor cells were enriched by blood filtration using isolation by size of epithelial tumor cells, and in situ hybridization with RNA method was used to identify and discriminate subgroups of circulating tumor cells. Afterwards, FAM172A expression in individual circulating tumor cells was measured. Three circulating tumor cell subgroups (epithelial/biophenotypic/mesenchymal circulating tumor cells) were identified using epithelial-mesenchymal transition markers. In our research, mesenchymal circulating tumor cells significantly increased along with tumor progression, development of distant metastasis, and vascular invasion. Furthermore, FAM172A expression rate in mesenchymal circulating tumor cells was significantly higher than that in epithelial circulating tumor cells, which suggested that FAM172A may correlate with malignant degree of tumor. This hypothesis was further verified by FAM172A expression in mesenchymal circulating tumor cells, which was strictly related to tumor aggressiveness factors. Mesenchymal circulating tumor cells and FAM172A detection may predict highrisk stage II colorectal cancer. Our research proved that circulating tumor cells were feasible surrogate samples to detect gene expression and could serve as a predictive biomarker for tumor evaluation.

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

PubMed

Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was <3-fold for lysosome-rich tissues investigated here. Accounting for the variability in cellular physiological model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

PubMed Central

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; Nery, Joseph R.; Castanon, Rosa G.; Lee, Ah Young; Shen, Yin; Visel, Axel; Pennacchio, Len A.; Ren, Bing; Ecker, Joseph R.

2017-01-01

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types. REPTILE is available at https://github.com/yupenghe/REPTILE/. PMID:28193886

Analysis of electric and thermal behaviour of lithium-ion cells in realistic driving cycles

NASA Astrophysics Data System (ADS)

Tourani, Abbas; White, Peter; Ivey, Paul

2014-12-01

A substantial part of electric vehicles (EVs) powertrain is the battery cell. The cells are usually connected in series, and failure of a single cell can deactivate an entire module in the battery pack. Hence, understanding the cell behaviour helps to predict and improve the battery performance and leads to design a cost effective thermal management system for the battery pack. A first principle thermo electrochemical model is applied to study the cell behaviour. The model is in good agreement with the experimental results and can predict the heat generation and the temperature distribution across the cell for different operating conditions. The operating temperature effect on the cell performance is studied and the operating temperature for the best performance is verified. In addition, EV cells are examined in a realistic driving cycle from the Artemis class. The study findings lead to the proposal of some crucial recommendation to design cost effective thermal management systems for the battery pack.

Identifying Stem-like Cells Using Mitochondrial Membrane Potential | Center for Cancer Research

Cancer.gov

Therapies that are based on living cells promise to improve treatments for metastatic cancer and for many degenerative diseases. Lasting treatment of these maladies may require the durable persistence of cells. Long-term engraftment of cells – for months or years – and the generation of large numbers of progeny are characteristics of stem cells. Most approaches to isolate viable hematopoetic stem cells and therapeutically active T cells are based on immunophenotyping using highly multicolored flow cytometry. However, these methods do not directly measure the metabolic features of cells, which are known to be important in predicting cell fate.

Mathematical modeling of mutant transferrin-CRM107 molecular conjugates for cancer therapy.

PubMed

Yoon, Dennis J; Chen, Kevin Y; Lopes, André M; Pan, April A; Shiloach, Joseph; Mason, Anne B; Kamei, Daniel T

2017-03-07

The transferrin (Tf) trafficking pathway is a promising mechanism for use in targeted cancer therapy due to the overexpression of transferrin receptors (TfRs) on cancerous cells. We have previously developed a mathematical model of the Tf/TfR trafficking pathway to improve the efficiency of Tf as a drug carrier. By using diphtheria toxin (DT) as a model toxin, we found that mutating the Tf protein to change its iron release rate improves cellular association and efficacy of the drug. Though this is an improvement upon using wild-type Tf as the targeting ligand, conjugated toxins like DT are unfortunately still highly cytotoxic at off-target sites. In this work, we address this hurdle in cancer research by developing a mathematical model to predict the efficacy and selectivity of Tf conjugates that use an alternative toxin. For this purpose, we have chosen to study a mutant of DT, cross-reacting material 107 (CRM107). First, we developed a mathematical model of the Tf-DT trafficking pathway by extending our Tf/TfR model to include intracellular trafficking via DT and DT receptors. Using this mathematical model, we subsequently investigated the efficacy of several conjugates in cancer cells: DT and CRM107 conjugated to wild-type Tf, as well as to our engineered mutant Tf proteins (K206E/R632A Tf and K206E/R534A Tf). We also investigated the selectivity of mutant Tf-CRM107 against non-neoplastic cells. Through the use of our mathematical model, we predicted that (i) mutant Tf-CRM107 exhibits a greater cytotoxicity than wild-type Tf-CRM107 against cancerous cells, (ii) this improvement was more drastic with CRM107 conjugates than with DT conjugates, and (iii) mutant Tf-CRM107 conjugates were selective against non-neoplastic cells. These predictions were validated with in vitro cytotoxicity experiments, demonstrating that mutant Tf-CRM107 conjugates is indeed a more suitable therapeutic agent. Validation from in vitro experiments also confirmed that such whole-cell kinetic models can be useful in cancer therapeutic design. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Analysis and prediction of leucine-rich nuclear export signals.

PubMed

la Cour, Tanja; Kiemer, Lars; Mølgaard, Anne; Gupta, Ramneek; Skriver, Karen; Brunak, Søren

2004-06-01

We present a thorough analysis of nuclear export signals and a prediction server, which we have made publicly available. The machine learning prediction method is a significant improvement over the generally used consensus patterns. Nuclear export signals (NESs) are extremely important regulators of the subcellular location of proteins. This regulation has an impact on transcription and other nuclear processes, which are fundamental to the viability of the cell. NESs are studied in relation to cancer, the cell cycle, cell differentiation and other important aspects of molecular biology. Our conclusion from this analysis is that the most important properties of NESs are accessibility and flexibility allowing relevant proteins to interact with the signal. Furthermore, we show that not only the known hydrophobic residues are important in defining a nuclear export signals. We employ both neural networks and hidden Markov models in the prediction algorithm and verify the method on the most recently discovered NESs. The NES predictor (NetNES) is made available for general use at http://www.cbs.dtu.dk/.

Application of dielectric spectroscopy for monitoring high cell density in monoclonal antibody producing CHO cell cultivations.

PubMed

Párta, László; Zalai, Dénes; Borbély, Sándor; Putics, Akos

2014-02-01

The application of dielectric spectroscopy was frequently investigated as an on-line cell culture monitoring tool; however, it still requires supportive data and experience in order to become a robust technique. In this study, dielectric spectroscopy was used to predict viable cell density (VCD) at industrially relevant high levels in concentrated fed-batch culture of Chinese hamster ovary cells producing a monoclonal antibody for pharmaceutical purposes. For on-line dielectric spectroscopy measurements, capacitance was scanned within a wide range of frequency values (100-19,490 kHz) in six parallel cell cultivation batches. Prior to detailed mathematical analysis of the collected data, principal component analysis (PCA) was applied to compare dielectric behavior of the cultivations. PCA analysis resulted in detecting measurement disturbances. By using the measured spectroscopic data, partial least squares regression (PLS), Cole-Cole, and linear modeling were applied and compared in order to predict VCD. The Cole-Cole and the PLS model provided reliable prediction over the entire cultivation including both the early and decline phases of cell growth, while the linear model failed to estimate VCD in the later, declining cultivation phase. In regards to the measurement error sensitivity, remarkable differences were shown among PLS, Cole-Cole, and linear modeling. VCD prediction accuracy could be improved in the runs with measurement disturbances by first derivative pre-treatment in PLS and by parameter optimization of the Cole-Cole modeling.

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Comparative modeling of InP solar cell structures

NASA Technical Reports Server (NTRS)

Jain, R. K.; Weinberg, I.; Flood, D. J.

1991-01-01

The comparative modeling of p(+)n and n(+)p indium phosphide solar cell structures is studied using a numerical program PC-1D. The optimal design study has predicted that the p(+)n structure offers improved cell efficiencies as compared to n(+)p structure, due to higher open-circuit voltage. The various cell material and process parameters to achieve the maximum cell efficiencies are reported. The effect of some of the cell parameters on InP cell I-V characteristics was studied. The available radiation resistance data on n(+)p and p(+)p InP solar cells are also critically discussed.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).

PubMed

Koestler, Devin C; Jones, Meaghan J; Usset, Joseph; Christensen, Brock C; Butler, Rondi A; Kobor, Michael S; Wiencke, John K; Kelsey, Karl T

2016-03-08

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution. Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R (2)>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R (2)>0.90 and R M S E<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets. Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

Changing practice: red blood cell typing by molecular methods for patients with sickle cell disease.

PubMed

Casas, Jessica; Friedman, David F; Jackson, Tannoa; Vege, Sunitha; Westhoff, Connie M; Chou, Stella T

2015-06-01

Extended red blood cell (RBC) antigen matching is recommended to limit alloimmunization in patients with sickle cell disease (SCD). DNA-based testing to predict blood group phenotypes has enhanced availability of antigen-negative donor units and improved typing of transfused patients, but replacement of routine serologic typing for non-ABO antigens with molecular typing for patients has not been reported. This study compared the historical RBC antigen phenotypes obtained by hemagglutination methods with genotype predictions in 494 patients with SCD. For discrepant results, repeat serologic testing was performed and/or investigated by gene sequencing for silent or variant alleles. Seventy-one typing discrepancies were identified among 6360 antigen comparisons (1.1%). New specimens for repeat serologic testing were obtained for 66 discrepancies and retyping agreed with the genotype in 64 cases. One repeat Jk(b-) serologic phenotype, predicted Jk(b+) by genotype, was found by direct sequencing of JK to be a silenced allele, and one N typing discrepancy remains under investigation. Fifteen false-negative serologic results were associated with alleles encoding weak antigens or single-dose Fy(b) expression. DNA-based RBC typing provided improved accuracy and expanded information on RBC antigens compared to hemagglutination methods, leading to its implementation as the primary method for extended RBC typing for patients with SCD at our institution. © 2015 AABB.

SOX9 as a Predictor for Neurogenesis Potentiality of Amniotic Fluid Stem Cells

PubMed Central

Wei, Pei-Cih; Chao, Angel; Peng, Hsiu-Huei; Chao, An-Shine; Chang, Yao-Lung; Chang, Shuenn-Dyh; Wang, Hsin-Shih; Chang, Yu-Jen; Tsai, Ming-Song; Sieber, Martin; Chen, Hua-Chien; Chen, Shu-Jen; Lee, Yun-Shien

2014-01-01

Preclinical studies of amniotic fluid-derived cell therapy have been successful in the research of neurodegenerative diseases, peripheral nerve injury, spinal cord injury, and brain ischemia. Transplantation of human amniotic fluid stem cells (AFSCs) into rat brain ventricles has shown improvement in symptoms of Parkinson's disease and also highlighted the minimal immune rejection risk of AFSCs, even between species. Although AFSCs appeared to be a promising resource for cell-based regenerative therapy, AFSCs contain a heterogeneous pool of distinct cell types, rendering each preparation of AFSCs unique. Identification of predictive markers for neuron-prone AFSCs is necessary before such stem cell-based therapeutics can become a reality. In an attempt to identify markers of AFSCs to predict their ability for neurogenesis, we performed a two-phase study. In the discovery phase of 23 AFSCs, we tested ZNF521/Zfp521, OCT6, SOX1, SOX2, SOX3, and SOX9 as predictive markers of AFSCs for neural differentiation. In the validation phase, the efficacy of these predictive markers was tested in independent sets of 18 AFSCs and 14 dental pulp stem cells (DPSCs). We found that high expression of SOX9 in AFSCs is associated with good neurogenetic ability, and these positive correlations were confirmed in independent sets of AFSCs and DPSCs. Furthermore, knockdown of SOX9 in AFSCs inhibited their neuronal differentiation. In conclusion, the discovery of SOX9 as a predictive marker for neuron-prone AFSCs could expedite the selection of useful clones for regenerative medicine, in particular, in neurological diseases and injuries. PMID:25154783

Comprehensive summary--Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing.

PubMed

Mueller, Stefan O; Dekant, Wolfgang; Jennings, Paul; Testai, Emanuela; Bois, Frederic

2015-12-25

This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project "Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics" (Predict-IV; Grant 202222). The project's overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV. Copyright © 2014 Elsevier Ltd. All rights reserved.

Model-based analysis of N-glycosylation in Chinese hamster ovary cells

PubMed Central

Krambeck, Frederick J.; Bennun, Sandra V.; Betenbaugh, Michael J.

2017-01-01

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products. PMID:28486471

Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

PubMed

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Combining Early Coagulation and Inflammatory Status Improves Prediction of Mortality in Burned and Nonburned Trauma Patients

DTIC Science & Technology

2008-02-01

clinician to distinguish between the effects of treatment and the effects of disease. Several different prediction models for multiple or- gan failure...treat- ment protocols and allow a clinician to distinguish the effect of treatment from effect of disease. In this study, our model predicted in...TNF produces a decrease in protein C activation by down regulating the expression of endothelial cell protein C receptor and thrombomodulin, both of

Genetic improvement in mastitis resistance: comparison of selection criteria from cross-sectional and random regression sire models for somatic cell score.

PubMed

Odegård, J; Klemetsdal, G; Heringstad, B

2005-04-01

Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.

Genome-scale biological models for industrial microbial systems.

PubMed

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

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

Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome.

PubMed

Kalaiselvan, Sagadevan; Sankar, Sathish; Ramamurthy, Mageshbabu; Ghosh, Asit Ranjan; Nandagopal, Balaji; Sridharan, Gopalan

2017-08-01

Hantaviruses are emerging viral pathogens that causes hantavirus cardiopulmonary syndrome (HCPS) in the Americas, a severe, sometimes fatal, respiratory disease in humans with a case fatality rate of ≥50%. IgM and IgG-based serological detection methods are the most common approaches used for laboratory diagnosis of hantaviruses. Such emerging viral pathogens emphasizes the need for improved rapid diagnostic devices and vaccines incorporating pan-specific epitopes of genotypes. We predicted linear B-cell epitopes for hantaviruses that are specific to genotypes causing HCPS in humans using in silico prediction servers. We modeled the Andes and Sin Nombre hantavirus nucleocapsid protein to locate the identified epitopes. Based on the mean percent prediction probability score, epitope IMASKSVGS/TAEEKLKKKSAF was identified as the best candidate B-cell epitope specific for hantaviruses causing HCPS. Promiscuous epitopes were identified in the C-terminal of the protein. Our study for the first time has reported pan-specific B-cell epitopes for developing immunoassays in the detection of antibodies to hantaviruses causing HCPS. Identification of epitopes with pan-specific recognition of all genotypes causing HCPS could be valuable for the development of immunodiagnositic tools toward pan-detection of hantavirus antibodies in ELISA. J. Cell. Biochem. 118: 2320-2324, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Finite element analysis of the stiffness of fabric reinforced composites

NASA Technical Reports Server (NTRS)

Foye, R. L.

1992-01-01

The objective of this work is the prediction of all three dimensional elastic moduli of textile fabric reinforced composites. The analysis is general enough for use with complex reinforcing geometries and capable of subsequent improvements. It places no restrictions on fabric microgeometry except that the unit cell be determinate and rectangular. The unit cell is divided into rectangular subcells in which the reinforcing geometries are easier to define and analyze. The analysis, based on inhomogeneous finite elements, is applied to a variety of weave, braid, and knit reinforced composites. Some of these predictions are correlated to test data.

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

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

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

Stress-life interrelationships associated with alkaline fuel cells

NASA Technical Reports Server (NTRS)

Thaller, Lawrence H.; Martin, Ronald E.; Stedman, James K.

1987-01-01

A review is presented concerning the interrelationships between applied stress and the expected service life of alkaline fuel cells. Only the physical, chemical, and electrochemical phenomena that take place within the fuel cell stack portion of an overall fuel cell system will be discussed. A brief review will be given covering the significant improvements in performance and life over the past two decades as well as summarizing the more recent advances in understanding which can be used to predict the performance and life characteristics of fuel cell systems that have yet to be built.

Polarity, cell division, and out-of-equilibrium dynamics control the growth of epithelial structures

PubMed Central

Cerruti, Benedetta; Puliafito, Alberto; Shewan, Annette M.; Yu, Wei; Combes, Alexander N.; Little, Melissa H.; Chianale, Federica; Primo, Luca; Serini, Guido; Mostov, Keith E.; Celani, Antonio

2013-01-01

The growth of a well-formed epithelial structure is governed by mechanical constraints, cellular apico-basal polarity, and spatially controlled cell division. Here we compared the predictions of a mathematical model of epithelial growth with the morphological analysis of 3D epithelial structures. In both in vitro cyst models and in developing epithelial structures in vivo, epithelial growth could take place close to or far from mechanical equilibrium, and was determined by the hierarchy of time-scales of cell division, cell–cell rearrangements, and lumen dynamics. Equilibrium properties could be inferred by the analysis of cell–cell contact topologies, and the nonequilibrium phenotype was altered by inhibiting ROCK activity. The occurrence of an aberrant multilumen phenotype was linked to fast nonequilibrium growth, even when geometric control of cell division was correctly enforced. We predicted and verified experimentally that slowing down cell division partially rescued a multilumen phenotype induced by altered polarity. These results improve our understanding of the development of epithelial organs and, ultimately, of carcinogenesis. PMID:24145168

Month-wise variation and prediction of bulk tank somatic cell count in Brazilian dairy herds and its impact on payment based on milk quality.

PubMed

Busanello, Marcos; de Freitas, Larissa Nazareth; Winckler, João Pedro Pereira; Farias, Hiron Pereira; Dos Santos Dias, Carlos Tadeu; Cassoli, Laerte Dagher; Machado, Paulo Fernando

2017-01-01

Payment programs based on milk quality (PPBMQ) are used in several countries around the world as an incentive to improve milk quality. One of the principal milk parameters used in such programs is the bulk tank somatic cell count (BTSCC). In this study, using data from an average of 37,000 farms per month in Brazil where milk was analyzed, BTSCC data were divided into different payment classes based on milk quality. Then, descriptive and graphical analyses were performed. The probability of a change to a worse payment class was calculated, future BTSCC values were predicted using time series models, and financial losses due to the failure to reach the maximum bonus for the payment based on milk quality were simulated. In Brazil, the mean BTSCC has remained high in recent years, without a tendency to improve. The probability of changing to a worse payment class was strongly affected by both the BTSCC average and BTSCC standard deviation for classes 1 and 2 (1000-200,000 and 201,000-400,000 cells/mL, respectively) and only by the BTSCC average for classes 3 and 4 (401,000-500,000 and 501,000-800,000 cells/mL, respectively). The time series models indicated that at some point in the year, farms would not remain in their current class and would accrue financial losses due to payments based on milk quality. The BTSCC for Brazilian dairy farms has not recently improved. The probability of a class change to a worse class is a metric that can aid in decision-making and stimulate farmers to improve milk quality. A time series model can be used to predict the future value of the BTSCC, making it possible to estimate financial losses and to show, moreover, that financial losses occur in all classes of the PPBMQ because the farmers do not remain in the best payment class in all months.

Cathode Characterization with Steel and Copper Collector Bars in an Electrolytic Cell

NASA Astrophysics Data System (ADS)

Das, Subrat; Morsi, Yos; Brooks, Geoffrey

2013-12-01

This article presents finite-element method simulation results of current distribution in an aluminum electrolytic cell. The model uses one quarter of the cell as a computational domain assuming longitudinal (along the length of the cell) and transverse axes of symmetries. The purpose of this work is to closely examine the impact of steel and copper collector bars on the cell current distribution. The findings indicated that an inclined steel collector bar (φ = 1°) can save up to 10-12 mV from the cathode lining in comparison to a horizontal 100 mm × 150-mm steel collector bar. It is predicted that a copper collector bar has a much higher potential of saving cathode voltage drop (CVD) and has a greater impact on the overall current distribution in the cell. A copper collector bar with 72% of cathode length and size of 100 mm × 150 mm is predicted to have more than 150 mV savings in cathode lining. In addition, a significant improvement in current distribution over the entire cathode surface is achieved when compared with a similar size of steel collector bar. There is a reduction of more than 70% in peak current density value due to the higher conductivity of copper. Comparisons between steel and copper collector bars with different sizes are discussed in terms CVD and current density distribution. The most important aspect of the findings is to recognize the influence of copper collector bars on the current distribution in molten metal. Lorentz fields are evaluated at different sizes of steel and copper collector bars. The simulation predicts that there is 50% decrease in Lorentz force due to the improvement in current distribution in the molten metal.

The utility and limitations of current web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection #

PubMed Central

Chaves, Francisco A.; Lee, Alvin H.; Nayak, Jennifer; Richards, Katherine A.; Sant, Andrea J.

2012-01-01

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding their success in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple web-available algorithms by comparing their predictions and our results using purely empirical methods for epitope discovery in influenza that utilized overlapping peptides and cytokine Elispots, for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false positive and false negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide:MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules. PMID:22467652

Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

PubMed Central

Hardison, Ross C.

2017-01-01

Abstract The Roadmap Epigenomics Consortium has published whole-genome functional annotation maps in 127 human cell types by integrating data from studies of multiple epigenetic marks. These maps have been widely used for studying gene regulation in cell type-specific contexts and predicting the functional impact of DNA mutations on disease. Here, we present a new map of functional elements produced by applying a method called IDEAS on the same data. The method has several unique advantages and outperforms existing methods, including that used by the Roadmap Epigenomics Consortium. Using five categories of independent experimental datasets, we compared the IDEAS and Roadmap Epigenomics maps. While the overall concordance between the two maps is high, the maps differ substantially in the prediction details and in their consistency of annotation of a given genomic position across cell types. The annotation from IDEAS is uniformly more accurate than the Roadmap Epigenomics annotation and the improvement is substantial based on several criteria. We further introduce a pipeline that improves the reproducibility of functional annotation maps. Thus, we provide a high-quality map of candidate functional regions across 127 human cell types and compare the quality of different annotation methods in order to facilitate biomedical research in epigenomics. PMID:28973456

A calibration procedure for load cells to improve accuracy of mini-lysimeters in monitoring evapotranspiration

NASA Astrophysics Data System (ADS)

Misra, R. K.; Padhi, J.; Payero, J. O.

2011-08-01

SummaryWe used twelve load cells (20 kg capacity) in a mini-lysimeter system to measure evapotranspiration simultaneously from twelve plants growing in separate pots in a glasshouse. A data logger combined with a multiplexer was used to connect all load cells with the full-bridge excitation mode to acquire load-cell signal. Each load cell was calibrated using fixed load within the range of 0-0.8 times the full load capacity of load cells. Performance of all load cells was assessed on the basis of signal settling time, excitation compensation, hysteresis and temperature. Final calibration of load cells included statistical consideration of these effects to allow prediction of lysimeter weights and evapotranspiration over short-time intervals for improved accuracy and sustained performance. Analysis of the costs for the mini-lysimeter system indicates that evapotranspiration can be measured economically at a reasonable accuracy and sufficient resolution with robust method of load-cell calibration.

Loss of Cytoplasmic CDK1 Predicts Poor Survival in Human Lung Cancer and Confers Chemotherapeutic Resistance

PubMed Central

Zhang, Chunyu; Elkahloun, Abdel G.; Robertson, Matthew; Gills, Joell J.; Tsurutani, Junji; Shih, Joanna H.; Fukuoka, Junya; Hollander, M. Christine; Harris, Curtis C.; Travis, William D.; Jen, Jin; Dennis, Phillip A.

2011-01-01

The dismal lethality of lung cancer is due to late stage at diagnosis and inherent therapeutic resistance. The incorporation of targeted therapies has modestly improved clinical outcomes, but the identification of new targets could further improve clinical outcomes by guiding stratification of poor-risk early stage patients and individualizing therapeutic choices. We hypothesized that a sequential, combined microarray approach would be valuable to identify and validate new targets in lung cancer. We profiled gene expression signatures during lung epithelial cell immortalization and transformation, and showed that genes involved in mitosis were progressively enhanced in carcinogenesis. 28 genes were validated by immunoblotting and 4 genes were further evaluated in non-small cell lung cancer tissue microarrays. Although CDK1 was highly expressed in tumor tissues, its loss from the cytoplasm unexpectedly predicted poor survival and conferred resistance to chemotherapy in multiple cell lines, especially microtubule-directed agents. An analysis of expression of CDK1 and CDK1-associated genes in the NCI60 cell line database confirmed the broad association of these genes with chemotherapeutic responsiveness. These results have implications for personalizing lung cancer therapy and highlight the potential of combined approaches for biomarker discovery. PMID:21887332

Lithium-Ion Batteries for Aerospace Applications

NASA Technical Reports Server (NTRS)

Surampudi, S.; Halpert, G.; Marsh, R. A.; James, R.

1999-01-01

This presentation reviews: (1) the goals and objectives, (2) the NASA and Airforce requirements, (3) the potential near term missions, (4) management approach, (5) the technical approach and (6) the program road map. The objectives of the program include: (1) develop high specific energy and long life lithium ion cells and smart batteries for aerospace and defense applications, (2) establish domestic production sources, and to demonstrate technological readiness for various missions. The management approach is to encourage the teaming of universities, R&D organizations, and battery manufacturing companies, to build on existing commercial and government technology, and to develop two sources for manufacturing cells and batteries. The technological approach includes: (1) develop advanced electrode materials and electrolytes to achieve improved low temperature performance and long cycle life, (2) optimize cell design to improve specific energy, cycle life and safety, (3) establish manufacturing processes to ensure predictable performance, (4) establish manufacturing processes to ensure predictable performance, (5) develop aerospace lithium ion cells in various AH sizes and voltages, (6) develop electronics for smart battery management, (7) develop a performance database required for various applications, and (8) demonstrate technology readiness for the various missions. Charts which review the requirements for the Li-ion battery development program are presented.

Prediction of clinical behaviour and treatment for cancers.

PubMed

Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

2003-01-01

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Expression, purification and epitope analysis of Pla a 2 allergen from Platanus acerifolia pollen.

PubMed

Wang, De-Wang; Ni, Wei-Wei; Zhou, Yan-Jun; Huang, Wen; Cao, Meng-Da; Meng, Ling; Wei, Ji-Fu

2018-01-01

Platanus acerifolia is one of the major sources of outdoor allergens to humans, and can induce allergic asthma, rhinitis, dermatitis and other allergic diseases. Pla a 2 is a polygalacturonase and represents the major allergen identified in P. acerifolia pollen. The aim of the present study was to express and purify Pla a 2, and to predict B and T cell epitopes of Pla a 2. The gene encoding Pla a 2 was cloned into the pET28a vector and subsequently transfected into ArcticExpress™ (DE3) Escherichia coli cells; purified Pla a 2 was analyzed by western blot analysis. The results of the present study revealed that the Pla a 2 allergen has the ability to bind immunoglobulin E within the sera of patients allergic to P. acerifolia pollen. In addition, the B cell epitopes of Pla a 2 were predicted using the DNAStar Protean system, Bioinformatics Predicted Antigenic Peptides and BepiPred 1.0 software; T cell epitopes were predicted using NetMHCIIpan ‑3.0 and ‑2.2. In total, eight B cell epitopes (15‑24, 60‑66, 78‑86, 109‑124, 232‑240, 260‑269, 298‑306 and 315‑322) and five T cell epitopes (62‑67, 86‑91, 125‑132, 217‑222 and 343‑350) were predicted in the present study. These findings may be used to improve allergen immunotherapies and reduce the frequency of pollen‑associated allergic reactions.

Interferon Gamma Messenger RNA Signature in Tumor Biopsies Predicts Outcomes in Patients with Non-Small-Cell Lung Carcinoma or Urothelial Cancer Treated with Durvalumab.

PubMed

Higgs, Brandon W; Morehouse, Christopher; Streicher, Katie L; Brohawn, Philip; Pilataxi, Fernanda; Gupta, Ashok; Ranade, Koustubh

2018-05-01

To identify a predictive biomarker for durvalumab, an anti-programmed death ligand 1 (PD-L1) monoclonal antibody. RNA sequencing of 97 advanced-stage non-small-cell lung carcinoma (NSCLC) biopsies from a nonrandomized phase 1b/2 clinical trial (1108/NCT01693562) were profiled to identify a predictive signature; 62 locally advanced or metastatic urothelial cancer (UC) tumors from the same study were profiled to confirm predictive utility of the signature. Thirty NSCLC patients provided pre- and posttreatment tumors for messenger RNA (mRNA) analysis. NSCLC with ≥25% tumor cells and UC with ≥25% tumor or immune cells stained for PD-L1 at any intensity were scored PD-L1 positive (PD-L1+). Kaplan-Meier and Cox proportional hazards analyses were used to adjust for gender, age, prior therapies, histology, ECOG, liver metastasis, and smoking. Tumor mutation burden (TMB) was calculated using data from The Cancer Genome Atlas (TCGA).  In the NSCLC discovery set, a four-gene interferon gamma (IFNγ)-positive (IFNγ+) signature comprising IFNγ, CD274, LAG3, and CXCL9 was associated with higher overall response rates, longer median progression-free survival, and overall survival compared with signature-low patients. IFNγ+-signature NSCLC patients had improved survival regardless of immunohistochemistry (IHC) PD-L1 status. These associations were replicated in a UC cohort. The IFNγ+ signature was induced twofold (P = 0.003) by durvalumab after 8 weeks of therapy in NSCLC patients, and baseline signature was associated with TMB but not survival in TCGA data.  The IFNγ+ mRNA signature may assist in identifying patients with improved outcomes to durvalumab, independent of PD-L1 assessed by IHC. Copyright ©2018, American Association for Cancer Research.

Efficient Unstructured Grid Adaptation Methods for Sonic Boom Prediction

NASA Technical Reports Server (NTRS)

Campbell, Richard L.; Carter, Melissa B.; Deere, Karen A.; Waithe, Kenrick A.

2008-01-01

This paper examines the use of two grid adaptation methods to improve the accuracy of the near-to-mid field pressure signature prediction of supersonic aircraft computed using the USM3D unstructured grid flow solver. The first method (ADV) is an interactive adaptation process that uses grid movement rather than enrichment to more accurately resolve the expansion and compression waves. The second method (SSGRID) uses an a priori adaptation approach to stretch and shear the original unstructured grid to align the grid with the pressure waves and reduce the cell count required to achieve an accurate signature prediction at a given distance from the vehicle. Both methods initially create negative volume cells that are repaired in a module in the ADV code. While both approaches provide significant improvements in the near field signature (< 3 body lengths) relative to a baseline grid without increasing the number of grid points, only the SSGRID approach allows the details of the signature to be accurately computed at mid-field distances (3-10 body lengths) for direct use with mid-field-to-ground boom propagation codes.

Prognostic factors, predictive markers and cancer biology: the triad for successful oral cancer chemoprevention.

PubMed

Monteiro de Oliveira Novaes, Jose Augusto; William, William N

2016-10-01

Oral squamous cell carcinomas represent a significant cancer burden worldwide. Unfortunately, chemoprevention strategies investigated to date have failed to produce an agent considered standard of care to prevent oral cancers. Nonetheless, recent advances in clinical trial design may streamline drug development in this setting. In this manuscript, we review some of these improvements, including risk prediction tools based on molecular markers that help select patients most suitable for chemoprevention. We also discuss the opportunities that novel preclinical models and modern molecular profiling techniques will bring to the prevention field in the near future, and propose a clinical trials framework that incorporates molecular prognostic factors, predictive markers and cancer biology as a roadmap to improve chemoprevention strategies for oral cancers.

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

Output-Adaptive Tetrahedral Cut-Cell Validation for Sonic Boom Prediction

NASA Technical Reports Server (NTRS)

Park, Michael A.; Darmofal, David L.

2008-01-01

A cut-cell approach to Computational Fluid Dynamics (CFD) that utilizes the median dual of a tetrahedral background grid is described. The discrete adjoint is also calculated, which permits adaptation based on improving the calculation of a specified output (off-body pressure signature) in supersonic inviscid flow. These predicted signatures are compared to wind tunnel measurements on and off the configuration centerline 10 body lengths below the model to validate the method for sonic boom prediction. Accurate mid-field sonic boom pressure signatures are calculated with the Euler equations without the use of hybrid grid or signature propagation methods. Highly-refined, shock-aligned anisotropic grids were produced by this method from coarse isotropic grids created without prior knowledge of shock locations. A heuristic reconstruction limiter provided stable flow and adjoint solution schemes while producing similar signatures to Barth-Jespersen and Venkatakrishnan limiters. The use of cut-cells with an output-based adaptive scheme completely automated this accurate prediction capability after a triangular mesh is generated for the cut surface. This automation drastically reduces the manual intervention required by existing methods.

3D gut-liver chip with a PK model for prediction of first-pass metabolism.

PubMed

Lee, Dong Wook; Ha, Sang Keun; Choi, Inwook; Sung, Jong Hwan

2017-11-07

Accurate prediction of first-pass metabolism is essential for improving the time and cost efficiency of drug development process. Here, we have developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. This chip consists of two separate layers for gut (Caco-2) and liver (HepG2) cell lines, where cells can be co-cultured in both 2D and 3D forms. Both cell lines were maintained well in the chip, verified by confocal microscopy and measurement of hepatic enzyme activity. We investigated the PK profile of paracetamol in the chip, and corresponding PK model was constructed, which was used to predict PK profiles for different chip design parameters. Simulation results implied that a larger absorption surface area and a higher metabolic capacity are required to reproduce the in vivo PK profile of paracetamol more accurately. Our study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.

Learning and Prediction of Slip from Visual Information

NASA Technical Reports Server (NTRS)

Angelova, Anelia; Matthies, Larry; Helmick, Daniel; Perona, Pietro

2007-01-01

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

Application of cytology and molecular biology in diagnosing premalignant or malignant oral lesions

PubMed Central

Mehrotra, Ravi; Gupta, Anurag; Singh, Mamta; Ibrahim, Rahela

2006-01-01

Early detection of a premalignant or cancerous oral lesion promises to improve the survival and the morbidity of patients suffering from these conditions. Cytological study of oral cells is a non-aggressive technique that is well accepted by the patient, and is therefore an attractive option for the early diagnosis of oral cancer, including epithelial atypia and squamous cell carcinoma. However its usage has been limited so far due to poor sensitivity and specificity in diagnosing oral malignancies. Lately it has re-emerged due to improved methods and it's application in oral precancer and cancer as a diagnostic and predictive method as well as for monitoring patients. Newer diagnostic techniques such as "brush biopsy" and molecular studies have been developed. Recent advances in cytological techniques and novel aspects of applications of scraped or exfoliative cytology for detecting these lesions and predicting their progression or recurrence are reviewed here. PMID:16556320

Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Aokage, K.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2015-03-01

Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. In the recent release of positive results from the National Lung Screening Trial (NLST) in the US showing that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of RFS for patients with NSCLC.

The Lack of Utility of Circulating Biomarkers of Inflammation and Endothelial Dysfunction for Type 2 Diabetes Risk Prediction Among Postmenopausal Women

PubMed Central

Chao, Chun; Song, Yiqing; Cook, Nancy; Tseng, Chi-Hong; Manson, JoAnn E.; Eaton, Charles; Margolis, Karen L.; Rodriguez, Beatriz; Phillips, Lawrence S.; Tinker, Lesley F.; Liu, Simin

2011-01-01

Background Recent studies have linked plasma markers of inflammation and endothelial dysfunction to type 2 diabetes mellitus (DM) development. However, the utility of these novel biomarkers for type 2 DM risk prediction remains uncertain. Methods The Women’s Health Initiative Observational Study (WHIOS), a prospective cohort, and a nested case-control study within the WHIOS of 1584 incident type 2 DM cases and 2198 matched controls were used to evaluate the utility of plasma markers of inflammation and endothelial dysfunction for type 2 DM risk prediction. Between September 1994 and December 1998, 93 676 women aged 50 to 79 years were enrolled in the WHIOS. Fasting plasma levels of glucose, insulin, white blood cells, tumor necrosis factor receptor 2, interleukin 6, high-sensitivity C-reactive protein, E-selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 were measured using blood samples collected at baseline. A series of prediction models including traditional risk factors and novel plasma markers were evaluated on the basis of global model fit, model discrimination, net reclassification improvement, and positive and negative predictive values. Results Although white blood cell count and levels of interleukin 6, high-sensitivity C-reactive protein, and soluble intercellular adhesion molecule 1 significantly enhanced model fit, none of the inflammatory and endothelial dysfunction markers improved the ability of model discrimination (area under the receiver operating characteristic curve, 0.93 vs 0.93), net reclassification, or predictive values (positive, 0.22 vs 0.24; negative, 0.99 vs 0.99 [using 15% 6-year type 2 DM risk as the cutoff]) compared with traditional risk factors. Similar results were obtained in ethnic-specific analyses. Conclusion Beyond traditional risk factors, measurement of plasma markers of systemic inflammation and endothelial dysfunction contribute relatively little additional value in clinical type 2 DM risk prediction in a multiethnic cohort of postmenopausal women. PMID:20876407

Efficiency of bulk-heterojunction organic solar cells

PubMed Central

Scharber, M.C.; Sariciftci, N.S.

2013-01-01

During the last years the performance of bulk heterojunction solar cells has been improved significantly. For a large-scale application of this technology further improvements are required. This article reviews the basic working principles and the state of the art device design of bulk heterojunction solar cells. The importance of high power conversion efficiencies for the commercial exploitation is outlined and different efficiency models for bulk heterojunction solar cells are discussed. Assuming state of the art materials and device architectures several models predict power conversion efficiencies in the range of 10–15%. A more general approach assuming device operation close to the Shockley–Queisser-limit leads to even higher efficiencies. Bulk heterojunction devices exhibiting only radiative recombination of charge carriers could be as efficient as ideal inorganic photovoltaic devices. PMID:24302787

Evaluation of high-energy lithium thionyl chloride primary cells

NASA Technical Reports Server (NTRS)

Frank, H. A.

1980-01-01

An advanced commercial primary lithium cell (LiSoCl2) was evaluated in order to establish baseline data for improved lithium batteries for aerospace applications. The cell tested had nominal capacity of 6 Ah. Maximum energy density at low rates (less than C/30, where C is the cell capacity in amp-hrs and 30 corresponds to a 30 hr discharge time) was found to be near 300 Wh/kg. An equation which predicts the operating voltage of these cells as a function of current and state of charge is presented. Heat generation rates of these cells were determined as a function of current in a calorimeter. It was found that heat rates could be theoretically predicted with some degree of accuracy at currents less than 1 amp or the C/6 rate. No explosions were observed in the cells during the condition of overdischarge or reversal nor during high rate discharge. It was found, however, that the cells can vent when overdischarge currents are greater than C/30 and when discharge rates are greater than 1.5C.

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

PubMed

Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

2017-11-01

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes. Copyright © 2017 by The American Association of Immunologists, Inc.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

PubMed

Ryall, Karen A; Shin, Jimin; Yoo, Minjae; Hinz, Trista K; Kim, Jihye; Kang, Jaewoo; Heasley, Lynn E; Tan, Aik Choon

2015-12-01

Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. aikchoon.tan@ucdenver.edu. Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Theoretical and experimental research in space photovoltaics

NASA Technical Reports Server (NTRS)

Faur, Mircea; Faur, Maria

1995-01-01

Theoretical and experimental research is outlined for indium phosphide solar cells, other solar cells for space applications, fabrication and performance measurements of shallow homojunction InP solar cells for space applications, improved processing steps and InP material characterization with applications to fabrication of high efficiency radiation resistant InP solar cells and other opto-electronic InP devices, InP solar cells fabricated by thermal diffusion, experiment-based predicted high efficiency solar cells fabricated by closed-ampoule thermal diffusion, radiation resistance of diffused junction InP solar cells, chemical and electrochemical characterization and processing of InP diffused structures and solar cells, and progress in p(+)n InP diffused solar cells.

Selection of proper candidates with resected pathological stage IIIA-N2 non-small cell lung cancer for postoperative radiotherapy

PubMed Central

Hui, Zhouguang; Dai, Honghai; Liang, Jun; Lv, Jima; Zhou, Zongmei; Feng, Qinfu; Xiao, Zefen; Chen, Dongfu; Zhang, Hongxing; Yin, Weibo; Wang, Luhua

2015-01-01

Background To establish a prediction model in selecting fit patients with resected pIIIA-N2 non-small cell lung cancer (NSCLC) for postoperative radiotherapy (PORT), and evaluate the model in clinical practice. Methods Between January 2003 and December 2005, 221 patients with resected pIIIA-N2 NSCLC were retrospectively analyzed. The effect of PORT on overall survival (OS) of patients with different clinicopathological factors was evaluated and the results were used to establish a prediction model to select patients fit for PORT. Results Compared with the control, PORT significantly improved the OS of patients with a smoking index ≤400 (P = 0.033), cN2 (P = 0.003), pT3 (P = 0.014), squamous cell carcinoma (SCC) (P = 0.013), or ≥4 positive nodes (P = 0.025). Patients were divided from zero to all five factors into low, middle, and high PORT index (PORT-I) groups (scored 0–1, 2, and 3–5, respectively). PORT did not improve OS (3-year, P = 0.531), disease free survival (DFS) (P = 0.358), or loco-regional recurrence free survival (LRFS) (P = 0.412) in the low PORT-I group. PORT significantly improved OS (P = 0.033), and tended to improve DFS (P = 0.064), but not LRFS (P = 0.287) in the middle PORT-I group. PORT could significantly improve OS (P = 0.000), DFS (P = 0.000), and LRFS (P = 0.006) in the high PORT-I group. Conclusion The prediction model is valuable in selecting patients with resected pIIIA-N2 NSCLC fit for PORT. PORT is strongly recommended for patients with three or more of the five factors of smoking index ≤400, cN2, pT3, SCC, and ≥4 positive nodes. PMID:26273382

A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer.

PubMed

Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H; Stanton, Paul; Zhao, Jing; Matuszak, Martha M; Ten Haken, Randall K; Kong, Feng-Ming

2018-03-01

To study whether cytokine markers may improve predictive accuracy of radiation esophagitis (RE) in non-small cell lung cancer (NSCLC) patients. A total of 129 patients with stage I-III NSCLC treated with radiotherapy (RT) from prospective studies were included. Thirty inflammatory cytokines were measured in platelet-poor plasma samples. Logistic regression was performed to evaluate the risk factors of RE. Stepwise Akaike information criterion (AIC) and likelihood ratio test were used to assess model predictions. Forty-nine of 129 patients (38.0%) developed grade ≥2 RE. Univariate analysis showed that age, stage, concurrent chemotherapy, and eight dosimetric parameters were significantly associated with grade ≥2 RE (p < 0.05). IL-4, IL-5, IL-8, IL-13, IL-15, IL-1α, TGFα and eotaxin were also associated with grade ≥2 RE (p < 0.1). Age, esophagus generalized equivalent uniform dose (EUD), and baseline IL-8 were independently associated grade ≥2 RE. The combination of these three factors had significantly higher predictive power than any single factor alone. Addition of IL-8 to toxicity model significantly improves RE predictive accuracy (p = 0.019). Combining baseline level of IL-8, age and esophagus EUD may predict RE more accurately. Refinement of this model with larger sample sizes and validation from multicenter database are warranted. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Molecular Biomarkers of Cancer Stem/Progenitor Cells Associated with Progression, Metastases, and Treatment Resistance of Aggressive Cancers

PubMed Central

Mimeault, Murielle; Batra, Surinder K.

2014-01-01

The validation of novel diagnostic, prognostic, and predictive biomarkers and therapeutic targets in tumor cells is of critical importance for optimizing the choice and efficacy of personalized therapies. Importantly, recent advances have led to the identification of gene-expression signatures in cancer cells, including cancer stem/progenitor cells, in the primary tumors, exosomes, circulating tumor cells (CTC), and disseminated cancer cells at distant metastatic sites. The gene-expression signatures may help to improve the accuracy of diagnosis and predict the therapeutic responses and overall survival of patients with cancer. Potential biomarkers in cancer cells include stem cell–like markers [CD133, aldehyde dehydrogenase (ALDH), CD44, and CD24], growth factors, and their cognate receptors [epidermal growth factor receptor (EGFR), EGFRvIII, and HER2], molecules associated with epithelial–mesenchymal transition (EMT; vimentin, N-cadherin, snail, twist, and Zeb1), regulators of altered metabolism (phosphatidylinositol-3′ kinase/Akt/mTOR), and drug resistance (multidrug transporters and macrophage inhibitory cytokine-1). Moreover, different pluripotency-associated transcription factors (Oct3/4, Nanog, Sox2, and Myc) and microRNAs that are involved in the epigenetic reprogramming and acquisition of stem cell–like properties by cancer cells during cancer progression may also be exploited as molecular biomarkers to predict the risk of metastases, systemic treatment resistance, and disease relapse of patients with cancer. PMID:24273063

An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System.

PubMed

Yin, Shen; Xie, Xiaochen; Lam, James; Cheung, Kie Chung; Gao, Huijun

2016-12-01

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

Mathematical modeling of solid oxide fuel cells

NASA Technical Reports Server (NTRS)

Lu, Cheng-Yi; Maloney, Thomas M.

1988-01-01

Development of predictive techniques, with regard to cell behavior, under various operating conditions is needed to improve cell performance, increase energy density, reduce manufacturing cost, and to broaden utilization of various fuels. Such technology would be especially beneficial for the solid oxide fuel cells (SOFC) at it early demonstration stage. The development of computer models to calculate the temperature, CD, reactant distributions in the tubular and monolithic SOFCs. Results indicate that problems of nonuniform heat generation and fuel gas depletion in the tubular cell module, and of size limitions in the monolithic (MOD 0) design may be encountered during FC operation.

[Recent Advances in Immunotherapy for Non-Small Cell Lung Cancer].

PubMed

Muto, Satoshi; Suzuki, Hiroyuki

2018-02-01

Cancer immunotherapy for non-small cell lung cancer began around 1970 with nonspecific immunomodulators and cytokine therapies. This has since developed into cell therapy including lymphokine-activated killer cells(LAK)and tumor infiltrating lymphocytes(TIL), as well as cancer vaccine therapy. However, no clear indication of effectiveness has been reported. Despite the high expectation over the effectiveness of cancer vaccine therapy, the treatment strategy was deemed unsuccessful, and focus turned to the study of immune escape mechanism, which is now regarded as standard treatment for non-small cell lung cancer. With the advent of immune checkpoint inhibitors, cancer immunotherapy has finally become a standard treatment for non-small cell lung cancer. There are still several obstacles to overcome including the identification of a predictive biomarker for improved efficacy, as well as the establishment of multidrug or multimodality combination therapy. PD-L1 expression is currently used as a predictive biomarker for anti-PD-1 therapy, but does not meet the expectations of the aimed results. Although tumor mutation burden is considered another promising biomarker, there remain clinical problems, for example the need of next generation sequencer. It was reported that combination therapy of immune checkpoint inhibitor after chemoradiation therapy was also effective. However, it remains unclear of what is required to further improve the clinical effects. In this article, we will review the history of cancer immunotherapy for non-small cell lung cancer and discuss the future prospects.

Validation of a Postoperative Nomogram Predicting Recurrence in Patients with Conventional Clear Cell Renal Cell Carcinoma.

PubMed

Lee, Byron H; Feifer, Andrew; Feuerstein, Michael A; Benfante, Nicole E; Kou, Lei; Yu, Changhong; Kattan, Michael W; Russo, Paul

2018-01-01

Clear cell renal cell carcinoma (RCC) continues to be the most commonly diagnosed subtype and is associated with more aggressive behavior than papillary and chromophobe RCC. Predicting disease recurrence after surgical extirpation is important for counseling and targeting those at high risk for adjuvant therapy clinical trials. To validate a postoperative nomogram predicting 5-yr recurrence-free probability (RFP) for clinically localized clear cell RCC. We identified all patients who underwent nephrectomy for clinically localized clear cell RCC from 1990 to 2009 at Memorial Sloan Kettering Cancer Center. After excluding patients with bilateral renal masses, familial RCC syndromes, and T3c or T4 tumors due to the limited number, 1642 participants were available for analysis. Partial or radical nephrectomy. Disease recurrence was defined as any new tumor after nephrectomy or kidney cancer-specific mortality, whichever occurred first. A postoperative nomogram was used to calculate the predicted 5-yr RFP, and these values were compared with the actual 5-yr RFP. Nomogram performance was evaluated by concordance index and calibration plot. Median follow-up was 39 mo (interquartile range: 14-79 mo), and disease recurrence was observed in 50 patients. The nomogram concordance index was 0.81. The calibration curve showed that the nomogram underestimated the actual 5-yr RFP. We updated the nomogram by including the entire patient population, which maintained performance and significantly improved calibration. The updated clear cell RCC postoperative nomogram performed well in the combined cohort. Underestimation of actual 5-yr RFP by the original nomogram may be due to increased surgeon experience and other unknown variables. We updated a valuable prediction tool used for assessing the disease recurrence probability after nephrectomy for clear cell renal cell carcinoma. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.; Trimboli, M. Scott

2015-07-01

This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models.

Prediction of SOFC Performance with or without Experiments: A Study on Minimum Requirements for Experimental Data

DOE PAGES

Yang, Tao; Sezer, Hayri; Celik, Ismail B.; ...

2015-06-02

In the present paper, a physics-based procedure combining experiments and multi-physics numerical simulations is developed for overall analysis of SOFCs operational diagnostics and performance predictions. In this procedure, essential information for the fuel cell is extracted first by utilizing empirical polarization analysis in conjunction with experiments and refined by multi-physics numerical simulations via simultaneous analysis and calibration of polarization curve and impedance behavior. The performance at different utilization cases and operating currents is also predicted to confirm the accuracy of the proposed model. It is demonstrated that, with the present electrochemical model, three air/fuel flow conditions are needed to producemore » a set of complete data for better understanding of the processes occurring within SOFCs. After calibration against button cell experiments, the methodology can be used to assess performance of planar cell without further calibration. The proposed methodology would accelerate the calibration process and improve the efficiency of design and diagnostics.« less

Long-enduring primary hepatocyte-based co-cultures improve prediction of hepatotoxicity.

PubMed

Novik, Eric I; Dwyer, Jacquelyn; Morelli, James K; Parekh, Amit; Cho, Cheul; Pludwinski, Eitan; Shrirao, Anil; Freedman, Robert M; MacDonald, James S; Jayyosi, Zaid

2017-12-01

The failure of drug candidates during clinical trials and post-marketing withdrawal due to Drug Induced Liver Injury (DILI), results in significant late-stage attrition in the pharmaceutical industry. Animal studies have proven insufficient to definitively predict DILI in the clinic, therefore a variety of in vitro models are being tested in an effort to improve prediction of human hepatotoxicity. The model system described here consists of cryopreserved primary rat, dog or human hepatocytes co-cultured together with a fibroblast cell line, which aids in the hepatocytes' maintenance of more in vivo-like characteristics compared to traditional hepatic mono-cultures, including long term viability and retention of activity of cytochrome P450 isozymes. Cell viability was assessed by measurement of ATP following treatment with 29 compounds having known hepatotoxic liabilities. Hμrelrat™, Hμreldog™, and Hμrelhuman™ hepatic co-cultures were treated for 24h, or under repeat-dosing for 7 or 13days, and compared to rat and human hepatic mono-cultures following single-dose exposure for 24h. The results allowed for a comparison of cytotoxicity, species-specific responses and the effect of repeat compound exposure on the prediction of hepatotoxic potential in each model. Results show that the co-culture model had greater sensitivity compared to that of the hepatic mono-cultures. In addition, "time-based ratios" were determined by dividing the compounds' 24-hour TC 50 /C max values by TC 50 /C max values measured after dosing for either 7 or 13days. The results suggest that this approach may serve as a useful adjunct to traditional measurements of hepatotoxicity, improving the predictive value of early screening studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells.

PubMed

Yosipof, Abraham; Nahum, Oren E; Anderson, Assaf Y; Barad, Hannah-Noa; Zaban, Arie; Senderowitz, Hanoch

2015-06-01

Growth in energy demands, coupled with the need for clean energy, are likely to make solar cells an important part of future energy resources. In particular, cells entirely made of metal oxides (MOs) have the potential to provide clean and affordable energy if their power conversion efficiencies are improved. Such improvements require the development of new MOs which could benefit from combining combinatorial material sciences for producing solar cells libraries with data mining tools to direct synthesis efforts. In this work we developed a data mining workflow and applied it to the analysis of two recently reported solar cell libraries based on Titanium and Copper oxides. Our results demonstrate that QSAR models with good prediction statistics for multiple solar cells properties could be developed and that these models highlight important factors affecting these properties in accord with experimental findings. The resulting models are therefore suitable for designing better solar cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

NASA Astrophysics Data System (ADS)

Ramakrishnan, N.; Tourdot, Richard W.; Eckmann, David M.; Ayyaswamy, Portonovo S.; Muzykantov, Vladimir R.; Radhakrishnan, Ravi

2016-06-01

In order to achieve selective targeting of affinity-ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor-ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

CD19(+)CD21(low) B cells and patients at risk for NIH-defined chronic graft-versus-host disease with bronchiolitis obliterans syndrome.

PubMed

Kuzmina, Zoya; Krenn, Katharina; Petkov, Ventzislav; Körmöczi, Ulrike; Weigl, Roman; Rottal, Arno; Kalhs, Peter; Mitterbauer, Margit; Ponhold, Lothar; Dekan, Gerhard; Greinix, Hildegard T; Pickl, Winfried F

2013-03-07

Bronchiolitis obliterans syndrome (BOS), pathognomonic for chronic graft-versus-host disease (cGVHD) of the lung, is a progressive and often fatal complication after allogeneic hematopoietic cell transplantation (HCT). Biomarkers for the prediction and diagnosis of BOS are urgently needed to improve patients' prognosis. We prospectively evaluated B-cell subpopulations and B-cell activating factor (BAFF) in 136 patients (46 BOS, 41 no cGVHD, 49 cutaneous cGVHD) to define novel biomarkers for early diagnosis of National Institutes of Health-defined BOS diagnosed a median of 11 mo after HCT. Patients with newly diagnosed BOS had significantly higher percentages of CD19(+)CD21(low) B cells (25.5 versus 6.6%, P < .0001), BAFF (7.3 versus 3.5 ng/mL, P = .02), and BAFF/CD19(+) ratio (0.18 versus 0.02 ng/10(3) CD19(+) B cells, P 5 .007) compared with patients without cGVHD. The area under the receiver operating curve for CD19(+)CD21(low) B cells was 0.97 (95% confidence interval, 0.94-0.99) and a cutoff point >9% was optimal for diagnosing BOS in patients with first drop of pulmonary function tests with a sensitivity of 96% and a negative predictive value of 94%. Thus, elevated levels of CD19(+)CD21(low) B cells are a potential novel biomarker for HCT patients at risk for developing BOS at an early stage and could allow improvement of patient outcome.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

PubMed

Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

2015-11-01

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

A novel phenomenological multi-physics model of Li-ion battery cells

NASA Astrophysics Data System (ADS)

Oh, Ki-Yong; Samad, Nassim A.; Kim, Youngki; Siegel, Jason B.; Stefanopoulou, Anna G.; Epureanu, Bogdan I.

2016-09-01

A novel phenomenological multi-physics model of Lithium-ion battery cells is developed for control and state estimation purposes. The model can capture electrical, thermal, and mechanical behaviors of battery cells under constrained conditions, e.g., battery pack conditions. Specifically, the proposed model predicts the core and surface temperatures and reaction force induced from the volume change of battery cells because of electrochemically- and thermally-induced swelling. Moreover, the model incorporates the influences of changes in preload and ambient temperature on the force considering severe environmental conditions electrified vehicles face. Intensive experimental validation demonstrates that the proposed multi-physics model accurately predicts the surface temperature and reaction force for a wide operational range of preload and ambient temperature. This high fidelity model can be useful for more accurate and robust state of charge estimation considering the complex dynamic behaviors of the battery cell. Furthermore, the inherent simplicity of the mechanical measurements offers distinct advantages to improve the existing power and thermal management strategies for battery management.

Establishment of a cell-based wound healing assay for bio-relevant testing of wound therapeutics.

PubMed

Planz, Viktoria; Wang, Jing; Windbergs, Maike

Predictive in vitro testing of novel wound therapeutics requires adequate cell-based bio-assays. Such assays represent an integral part during preclinical development as pre-step before entering in vivo studies. Simple "scratch tests" based on defected skin cell monolayers exist, however these can solely be used for testing liquids, as cell monolayer destruction and excessive hydration limit their applicability for (semi-)solid systems like wound dressings. In this context, a cell-based wound healing assay is introduced for rapid and predictive testing of wound therapeutics independent of their physical state in a bio-relevant environment. A novel wound healing assay was established for bio-relevant and predictive testing of (semi-) solid wound therapeutics. The assay allows for physiologically relevant hydration of the tested wound therapeutics at the air-liquid interface and their removal without cell monolayer disruption. In a proof-of-concept study, the applicability and discriminative power could be demonstrated by examining unloaded and drug-loaded wound dressings with two different established wound healing actives (dexpanthenol and metyrapone) and their effect on skin cell behavior. The influence of the released drug on the cells´ healing behavior could successfully be monitored over time. Wound size assessment after 96h resulted in an eight fold smaller wound area for drug treated models compared to the ones treated with unloaded fibers and non-treated wounds. This assay provides valuable first insights towards the establishment of a valid screening and evaluation tool for preclinical wound therapeutic development from liquid to (semi-)solid systems to improve predictability in a simple, yet standardized way. Copyright © 2017 Elsevier Inc. All rights reserved.

Contaminant concentration versus flow velocity: drivers of biodegradation and microbial growth in groundwater model systems.

PubMed

Grösbacher, Michael; Eckert, Dominik; Cirpka, Olaf A; Griebler, Christian

2018-06-01

Aromatic hydrocarbons belong to the most abundant contaminants in groundwater systems. They can serve as carbon and energy source for a multitude of indigenous microorganisms. Predictions of contaminant biodegradation and microbial growth in contaminated aquifers are often vague because the parameters of microbial activity in the mathematical models used for predictions are typically derived from batch experiments, which don't represent conditions in the field. In order to improve our understanding of key drivers of natural attenuation and the accuracy of predictive models, we conducted comparative experiments in batch and sediment flow-through systems with varying concentrations of contaminant in the inflow and flow velocities applying the aerobic Pseudomonas putida strain F1 and the denitrifying Aromatoleum aromaticum strain EbN1. We followed toluene degradation and bacterial growth by measuring toluene and oxygen concentrations and by direct cell counts. In the sediment columns, the total amount of toluene degraded by P. putida F1 increased with increasing source concentration and flow velocity, while toluene removal efficiency gradually decreased. Results point at mass transfer limitation being an important process controlling toluene biodegradation that cannot be assessed with batch experiments. We also observed a decrease in the maximum specific growth rate with increasing source concentration and flow velocity. At low toluene concentrations, the efficiencies in carbon assimilation within the flow-through systems exceeded those in the batch systems. In all column experiments the number of attached cells plateaued after an initial growth phase indicating a specific "carrying capacity" depending on contaminant concentration and flow velocity. Moreover, in all cases, cells attached to the sediment dominated over those in suspension, and toluene degradation was performed practically by attached cells only. The observed effects of varying contaminant inflow concentration and flow velocity on biodegradation could be captured by a reactive-transport model. By monitoring both attached and suspended cells we could quantify the release of new-grown cells from the sediments to the mobile aqueous phase. Studying flow velocity and contaminant concentrations as key drivers of contaminant transformation in sediment flow-through microcosms improves our system understanding and eventually the prediction of microbial biodegradation at contaminated sites.

DNA methylation-based measures of biological age: meta-analysis predicting time to death.

PubMed

Chen, Brian H; Marioni, Riccardo E; Colicino, Elena; Peters, Marjolein J; Ward-Caviness, Cavin K; Tsai, Pei-Chien; Roetker, Nicholas S; Just, Allan C; Demerath, Ellen W; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L; Murabito, Joanne M; Bandinelli, Stefania; Hernandez, Dena G; Melzer, David; Nalls, Michael; Pilling, Luke C; Price, Timothy R; Singleton, Andrew B; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M; Shah, Sonia; Wray, Naomi R; McRae, Allan F; Franco, Oscar H; Hofman, Albert; Uitterlinden, André G; Absher, Devin; Assimes, Themistocles; Levine, Morgan E; Lu, Ake T; Tsao, Philip S; Hou, Lifang; Manson, JoAnn E; Carty, Cara L; LaCroix, Andrea Z; Reiner, Alexander P; Spector, Tim D; Feinberg, Andrew P; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T; Peters, Annette; Deary, Ian J; Pankow, James S; Ferrucci, Luigi; Horvath, Steve

2016-09-28

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10 -9 ) , independent of chronological age, even after adjusting for additional risk factors (p<5.4x10 -4 ) , and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10 -43 ). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

Evaluation program for secondary spacecraft cells

NASA Technical Reports Server (NTRS)

Christy, D. E.; Harkness, J. D.

1973-01-01

A life cycle test of secondary electric batteries for spacecraft applications was conducted. A sample number of nickel cadmium batteries were subjected to general performance tests to determine the limit of their actual capabilities. Weaknesses discovered in cell design are reported and aid in research and development efforts toward improving the reliability of spacecraft batteries. A statistical analysis of the life cycle prediction and cause of failure versus test conditions is provided.

Modeling of the interaction between osteoblasts and biocompatible substrates as a function of adhesion strength.

PubMed

Portan, D V; Deligianni, D D; Deligianni, K; Kroustalli, A A; Tyllianakis, M; Papanicolaou, G C

2018-03-01

A goal of current implantology research is to design devices that induce controlled, guided, and rapid healing. Nanoscale structured substrates [e.g., titania nanotubes (TNTs) or carbon nanotubes (CNTs)] dramatically improve the functions of conventional biomaterials. The present investigation evaluated the behavior of osteoblasts cells cultured on smooth and nanostructured substrates, by measuring osteoblasts specific biomarkers [alkaline phosphatase (AP) and total protein] and cells adhesion strength to substrates, followed by semi-empirical modeling to predict the experimental results. Findings were in total agreement with the current state of the art. The proliferation, as well as the AP and total protein levels were higher on the nanostructure phases (TNTs, CNTs) comparing to the smooth ones (plastic and pure titanium). Cells adhesion strength measured was found higher on the nanostructured materials. This coincided with a higher value of proteins which are directly implicated in the process of adherence. Results were accurately predicted through the Viscoelastic Hybrid Interphase Model. A gradual adherence of bone cells to implants using multilayered biomaterials that involve biodegradable polymeric films and a nanoscale modification of titanium surface is suggested to improve performance through an interphase-mediated osteointegration of orthopedic implants. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 621-628, 2018. © 2017 Wiley Periodicals, Inc.

Improved Assimilation of Streamflow and Satellite Soil Moisture with the Evolutionary Particle Filter and Geostatistical Modeling

NASA Astrophysics Data System (ADS)

Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman

2017-04-01

Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a reliable and effective DA approach in hydrologic applications.

Survival improvement in patients with non-small cell lung cancer between 1983 and 2012: Analysis of the Surveillance, Epidemiology, and End Results database.

PubMed

Wang, Shuncong; Sun, Tiantian; Sun, Huanhuan; Li, Xiaobo; Li, Jie; Zheng, Xiaobin; Mallampati, Saradhi; Sun, Hongliu; Zhou, Xiuling; Zhou, Cuiling; Zhang, Hongyu; Cheng, Zhibin; Ma, Haiqing

2017-05-01

Non-small cell lung cancer is the most common malignancy in males; it constitutes the majority of lung cancer cases and requires massive medical resources. Despite improvements in managing non-small cell lung cancer, long-term survival remains very low. This study evaluated survival improvement in patients with non-small cell lung cancer in each decade between 1983 and 2012 to determine the impact of race, sex, age, and socioeconomic status on the survival rates in these patients. We extracted data on non-small cell lung cancer cases in each decade between 1983 and 2012 from the Surveillance, Epidemiology, and End Results registries. In total, 573,987 patients with non-small cell lung cancer were identified in 18 Surveillance, Epidemiology, and End Results registry regions during this period. The 12-month relative survival rates improved slightly across three decades, from 39.7% to 40.9% to 45.5%, with larger improvement in the last two decades. However, the 5-year-relative survival rates were very low, with 14.3%, 15.5%, and 18.4%, respectively, in three decades, indicating the urgency for novel comprehensive cancer care. In addition, our data demonstrated superiority in survival time among non-small cell lung cancer patients of lower socioeconomic status and White race. Although survival rates of non-small cell lung cancer patients have improved across the three decades, the 5-year-relative survival rates remain very poor. In addition, widening survival disparities among the race, the sex, and various socioeconomic status groups were confirmed. This study will help in predicting future tendencies of incidence and survival of non-small cell lung cancer, will contribute to better clinical trials by balancing survival disparities, and will eventually improve the clinical management of non-small cell lung cancer.

Discovery and replication of a peripheral tissue DNA methylation biosignature to augment a suicide prediction model.

PubMed

Clive, Makena L; Boks, Marco P; Vinkers, Christiaan H; Osborne, Lauren M; Payne, Jennifer L; Ressler, Kerry J; Smith, Alicia K; Wilcox, Holly C; Kaminsky, Zachary

2016-01-01

Suicide is the second leading cause of death among adolescents in the USA, and rates are rising. Methods to identify individuals at risk are essential for implementing prevention strategies, and the development of a biomarker can potentially improve prediction of suicidal behaviors. Prediction of our previously reported SKA2 biomarker for suicide and PTSD is substantially improved by questionnaires assessing perceived stress or anxiety and is therefore reliant on psychological assessment. However, such stress-related states may also leave a biosignature that could equally improve suicide prediction. In genome-wide DNA methylation data, we observed significant overlap between waking cortisol-associated and suicide-associated DNA methylation in blood and the brain, respectively. Using a custom bioinformatic brain to blood discovery algorithm, we derived a DNA methylation biosignature that interacts with SKA2 methylation to improve the prediction of suicidal ideation in our existing suicide prediction model across both blood and saliva data sets. This biosignature was independently validated in the Grady Trauma Project cohort and interacted with HPA axis metrics in the same cohort. The biosignature showed a relationship with immune status by its correlation with myeloid-derived cell proportions in all data sets and with IL-6 measures in a prospective postpartum depression cohort. Three probes showed significant correlations with the biosignature: cg08469255 ( DDR1 ), cg22029879 ( ARHGEF10 ), and cg24437859 ( SHP1 ), of which SHP1 methylation correlated with immune measures. We conclude that this biosignature interacts with SKA2 methylation to improve suicide prediction and may represent a biological state of immune and HPA axis modulation that mediates suicidal behavior.

Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

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

Xavier, MA; Trimboli, MS

This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggestmore » significant performance improvements might be achieved by extending the result to electrochemical models. (C) 2015 Elsevier B.V. All rights reserved.« less

Toxicity modelling of Plk1-targeted therapies in genetically engineered mice and cultured primary mammalian cells

PubMed Central

Raab, Monika; Kappel, Sven; Krämer, Andrea; Sanhaji, Mourad; Matthess, Yves; Kurunci-Csacsko, Elisabeth; Calzada-Wack, Julia; Rathkolb, Birgit; Rozman, Jan; Adler, Thure; Busch, Dirk H.; Esposito, Irene; Fuchs, Helmut; Gailus-Durner, Valérie; Klingenspor, Martin; Wolf, Eckhard; Sänger, Nicole; Prinz, Florian; Angelis, Martin Hrabě de; Seibler, Jost; Yuan, Juping; Bergmann, Martin; Knecht, Rainald; Kreft, Bertolt; Strebhardt, Klaus

2011-01-01

High attrition rates of novel anti-cancer drugs highlight the need for improved models to predict toxicity. Although polo-like kinase 1 (Plk1) inhibitors are attractive candidates for drug development, the role of Plk1 in primary cells remains widely unexplored. Therefore, we evaluated the utility of an RNA interference-based model to assess responses to an inducible knockdown (iKD) of Plk1 in adult mice. Here we show that Plk1 silencing can be achieved in several organs, although adverse events are rare. We compared responses in Plk1-iKD mice with those in primary cells kept under controlled culture conditions. In contrast to the addiction of many cancer cell lines to the non-oncogene Plk1, the primary cells' proliferation, spindle assembly and apoptosis exhibit only a low dependency on Plk1. Responses to Plk1-depletion, both in cultured primary cells and in our iKD-mouse model, correspond well and thus provide the basis for using validated iKD mice in predicting responses to therapeutic interventions. PMID:21772266

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

PubMed Central

Ponomarenko, Julia V; Bourne, Philip E

2007-01-01

Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. PMID:17910770

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Long-term survival in patients with peripheral T-cell non-Hodgkin lymphomas after allogeneic hematopoietic stem cell transplant.

PubMed

Goldberg, Jenna D; Chou, Joanne F; Horwitz, Steven; Teruya-Feldstein, Julie; Barker, Juliet N; Boulad, Farid; Castro-Malaspina, Hugo; Giralt, Sergio; Jakubowski, Ann A; Koehne, Guenther; van den Brink, Marcel R M; Young, James W; Zhang, Zhigang; Papadopoulos, Esperanza B; Perales, Miguel-Angel

2012-06-01

Peripheral T-cell non-Hodgkin lymphomas (T-NHL) are rare diseases, with a worse prognosis compared to their B-cell counterparts. Allogeneic hematopoietic stem cell transplant may have a role in the treatment of relapsed/refractory disease or high-risk histologies in the upfront setting. However, there is limited information on the efficacy of allogeneic transplant for these diseases, as well as what factors may predict outcomes. We therefore performed a retrospective study of 34 patients who received an allogeneic transplant for the treatment of T-NHL at a single center between 1 January 1992 and 31 December 2009. The median follow-up for survivors was 45 months (range 9-160 months). The 2-year overall survival (OS) was 0.61 (95% confidence interval [CI]: 0.43-0.75) with a plateau at 28 months. Ki-67 expression ≤ 25% was predictive of improved OS (p < 0.01), and transplant in complete remission was predictive of a decreased cumulative incidence of events (p = 0.04). Three patients received a donor leukocyte infusion, and two patients demonstrated a response, supporting a graft-versus-lymphoma effect. These data demonstrate that allogeneic transplant is a viable option for the treatment of T-NHL and merits prospective evaluation.

Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy

PubMed Central

Johnson, Douglas B.; Estrada, Monica V.; Salgado, Roberto; Sanchez, Violeta; Doxie, Deon B.; Opalenik, Susan R.; Vilgelm, Anna E.; Feld, Emily; Johnson, Adam S.; Greenplate, Allison R.; Sanders, Melinda E.; Lovly, Christine M.; Frederick, Dennie T.; Kelley, Mark C.; Richmond, Ann; Irish, Jonathan M.; Shyr, Yu; Sullivan, Ryan J.; Puzanov, Igor; Sosman, Jeffrey A.; Balko, Justin M.

2016-01-01

Anti-PD-1 therapy yields objective clinical responses in 30–40% of advanced melanoma patients. Since most patients do not respond, predictive biomarkers to guide treatment selection are needed. We hypothesize that MHC-I/II expression is required for tumour antigen presentation and may predict anti-PD-1 therapy response. In this study, across 60 melanoma cell lines, we find bimodal expression patterns of MHC-II, while MHC-I expression was ubiquitous. A unique subset of melanomas are capable of expressing MHC-II under basal or IFNγ-stimulated conditions. Using pathway analysis, we show that MHC-II(+) cell lines demonstrate signatures of ‘PD-1 signalling', ‘allograft rejection' and ‘T-cell receptor signalling', among others. In two independent cohorts of anti-PD-1-treated melanoma patients, MHC-II positivity on tumour cells is associated with therapeutic response, progression-free and overall survival, as well as CD4+ and CD8+ tumour infiltrate. MHC-II+ tumours can be identified by melanoma-specific immunohistochemistry using commercially available antibodies for HLA-DR to improve anti-PD-1 patient selection. PMID:26822383

Analytical relationships for prediction of the mechanical properties of additively manufactured porous biomaterials

PubMed Central

Hedayati, Reza

2016-01-01

Abstract Recent developments in additive manufacturing techniques have motivated an increasing number of researchers to study regular porous biomaterials that are based on repeating unit cells. The physical and mechanical properties of such porous biomaterials have therefore received increasing attention during recent years. One of the areas that have revived is analytical study of the mechanical behavior of regular porous biomaterials with the aim of deriving analytical relationships that could predict the relative density and mechanical properties of porous biomaterials, given the design and dimensions of their repeating unit cells. In this article, we review the analytical relationships that have been presented in the literature for predicting the relative density, elastic modulus, Poisson's ratio, yield stress, and buckling limit of regular porous structures based on various types of unit cells. The reviewed analytical relationships are used to compare the mechanical properties of porous biomaterials based on different types of unit cells. The major areas where the analytical relationships have improved during the recent years are discussed and suggestions are made for future research directions. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 3164–3174, 2016. PMID:27502358

Predicting the response to CTLA-4 blockade by longitudinal noninvasive monitoring of CD8 T cells

PubMed Central

Whang, Katherine A.; LeGall, Camille; Cragnolini, Juan J.; Bierie, Brian; Gostissa, Monica; Grotenbreg, Gijsbert M.; Bhan, Atul; Weinberg, Robert A.

2017-01-01

Immunotherapy using checkpoint-blocking antibodies against targets such as CTLA-4 and PD-1 can cure melanoma and non–small cell lung cancer in a subset of patients. The presence of CD8 T cells in the tumor correlates with improved survival. We show that immuno–positron emission tomography (immuno-PET) can visualize tumors by detecting infiltrating lymphocytes and, through longitudinal observation of individual animals, distinguish responding tumors from those that do not respond to therapy. We used 89Zr-labeled PEGylated single-domain antibody fragments (VHHs) specific for CD8 to track the presence of intratumoral CD8+ T cells in the immunotherapy-susceptible B16 melanoma model in response to checkpoint blockade. A 89Zr-labeled PEGylated anti-CD8 VHH detected thymus and secondary lymphoid structures as well as intratumoral CD8 T cells. Animals that responded to CTLA-4 therapy showed a homogeneous distribution of the anti-CD8 PET signal throughout the tumor, whereas more heterogeneous infiltration of CD8 T cells correlated with faster tumor growth and worse responses. To support the validity of these observations, we used two different transplantable breast cancer models, yielding results that conformed with predictions based on the antimelanoma response. It may thus be possible to use immuno-PET and monitor antitumor immune responses as a prognostic tool to predict patient responses to checkpoint therapies. PMID:28666979

An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy.

PubMed

Wang, Ying; Qu, Xiao; Kam, Ngar-Woon; Wang, Kai; Shen, Hongchang; Liu, Qi; Du, Jiajun

2018-06-26

Emerging inflammatory response biomarkers are developed to predict the survival of patients with cancer, the aim of our study is to establish an inflammation-related nomogram based on the classical predictive biomarkers to predict the survivals of patients with non-small cell lung cancer (NSCLC). Nine hundred and fifty-two NSCLC patients with lung cancer surgery performed were enrolled into this study. The cutoffs of inflammatory response biomarkers were determined by Receiver operating curve (ROC). Univariate and multivariate analysis were conducted to select independent prognostic factors to develop the nomogram. The median follow-up time was 40.0 months (range, 1 to 92 months). The neutrophil to lymphocyte ratio (cut-off: 3.10, HR:1.648, P = 0.045) was selected to establish the nomogram which could predict the 5-year OS probability. The C-index of nomogram was 0.72 and the 5-year OS calibration curve displayed an optimal agreement between the actual observed outcomes and the predictive results. Neutrophil to lymphocyte ratio was shown to be a valuable biomarker for predicting survival of patients with NSCLC. The addition of neutrophil to lymphocyte ratio could improve the accuracy and predictability of the nomogram in order to provide reference for clinicians to assess patient outcomes.

Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development

PubMed Central

2015-01-01

Background High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV. Results This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV. Conclusions This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV. PMID:26680271

Murine bone marrow cells cultured ex vivo in the presence of multiple cytokine combinations lose radioprotective and long-term engraftment potential.

PubMed

Von Drygalski, A; Alespeiti, G; Ren, L; Adamson, J W

2004-02-01

The desire to improve engraftment following transplantation of limited numbers of hematopoietic stem cells (HSC) has spurred the investigation of ex vivo stem cell expansion techniques. While surrogate outcomes, such as an increase in SCID-repopulating cells, suggest successful stem cell expansion in some studies, it is not clear that such assays predict outcomes using a more clinically relevant approach (e.g., myeloablation). We have addressed this by testing three cytokine combinations for their ability to increase the radioprotective and long-term marrow reconstitution capacity of hematopoietic cells cultured ex vivo. Low numbers of light-density (LD) mouse bone marrow (BM) cells or their expanded product were injected into lethally irradiated (9 Gy) congenic recipients. Survival rates and percent donor engraftment were compared at 2, 5, and 7 months post-transplant. The three cytokine combinations used were: (i) kit-ligand (L), thrombopoietin (Tpo), Flt-3 L; (ii) cytokines in (i) plus interleukin-11 (IL-11); (iii) cytokines in (ii) plus IL-3. At 7 months post-transplant, LD cell doses of 10(4), 2-2.5 x 10(4), and 0.5-1.0 x 10(5) gave predictable survivals of 20-30%, 40-70%, and 100%, respectively. Mean percent donor engraftments were 54.9% (SEM 36%), 55.7% (SEM 36%), and 76.3% (SEM 21%), respectively. When cells expanded for 3 or 5-7 days with the various cytokine combinations were transplanted into different groups of mice, survival rates and percent donor engraftment were almost uniformly poorer than results obtained with unmanipulated cells, and cells expanded for 5-7 days led to poorer outcomes than cells expanded for 3 days. Overall, ex vivo expansion of LD BM cells with the cytokine combinations chosen failed to improve transplant outcomes in this model.

Biological Marker Analysis as Part of the CIBERES-RTIC Cancer-SEPAR Strategic Project on Lung Cancer.

PubMed

Monsó, Eduard; Montuenga, Luis M; Sánchez de Cos, Julio; Villena, Cristina

2015-09-01

The aim of the Clinical and Molecular Staging of Stage I-IIp Lung Cancer Project is to identify molecular variables that improve the prognostic and predictive accuracy of TMN classification in stage I/IIp non-small cell lung cancer (NSCLC). Clinical data and lung tissue, tumor and blood samples will be collected from 3 patient cohorts created for this purpose. The prognostic protein signature will be validated from these samples, and micro-RNA, ALK, Ros1, Pdl-1, and TKT, TKTL1 y G6PD expression will be analyzed. Tissue inflammatory markers and stromal cell markers will also be analyzed. Methylation of p16, DAPK, RASSF1a, APC and CDH13 genes in the tissue samples will be determined, and inflammatory markers in peripheral blood will also be analyzed. Variables that improve the prognostic and predictive accuracy of TNM in NSCLC by molecular staging may be identified from this extensive analytical panel. Copyright © 2014 SEPAR. Published by Elsevier Espana. All rights reserved.

Improved assessment of body cell mass by segmental bioimpedance analysis in malnourished subjects and acromegaly.

PubMed

Pirlich, M; Schütz, T; Ockenga, J; Biering, H; Gerl, H; Schmidt, B; Ertl, S; Plauth, M; Lochs, H

2003-04-01

Estimation of body cell mass (BCM) has been regarded valuable for the assessment of malnutrition. To investigate the value of segmental bioelectrical impedance analysis (BIA) for BCM estimation in malnourished subjects and acromegaly. Nineteen controls and 63 patients with either reduced (liver cirrhosis without and with ascites, Cushing's disease) or increased BCM (acromegaly) were included. Whole-body and segmental BIA (separately measuring arm, trunk, leg) at 50 kHz was compared with BCM measured by total-body potassium. Multiple regression analysis was used to develop specific equations for BCM in each subgroup. Compared to whole-body BIA equations, the inclusion of arm resistance improved the specific equation in cirrhotic patients without ascites and in Cushing's disease resulting in excellent prediction of BCM (R(2) = 0.93 and 0.92, respectively; both P<0.001). In acromegaly, inclusion of resistance and reactance of the trunk best described BCM (R(2) = 0.94, P<0.001). In controls and in cirrhotic patients with ascites, segmental impedance parameters did not improve BCM prediction (best values obtained by whole-body measurements: R(2)=0.88 and 0.60; P<0.001 and <0.003, respectively). Segmental BIA improves the assessment of BCM in malnourished patients and acromegaly, but not in patients with severe fluid overload. Copyright 2003 Elsevier Science Ltd.

Electroluminescence Studies on Longwavelength Indium Arsenide Quantum Dot Microcavities Grown on Gallium Arsenide

DTIC Science & Technology

2011-12-01

communication links using VCSEL arrays [1, 2], medical imaging using super luminescent diodes [3], and tunable lasers capable of remotely sensing...increase the efficiency of solar cells [6, 7, 8], vastly improve photo detector sensitivity [9], and provide optical memory storage densities predicted...semiconductor lasers” Applied Physics B: Lasers and Optics, Volume 90, Number 2, 2008, Pages 339-343. 6. Nozik, A.J. “Quantum dot solar cells

Dynamics of blood flow in a microfluidic ladder network

NASA Astrophysics Data System (ADS)

Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen

The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.

Anemia and Red Blood Cell Indices Predict HIV-Associated Neurocognitive Impairment in the Highly Active Antiretroviral Therapy Era

PubMed Central

Kallianpur, Asha R.; Wang, Quan; Jia, Peilin; Hulgan, Todd; Zhao, Zhongming; Letendre, Scott L.; Ellis, Ronald J.; Heaton, Robert K.; Franklin, Donald R.; Barnholtz-Sloan, Jill; Collier, Ann C.; Marra, Christina M.; Clifford, David B.; Gelman, Benjamin B.; McArthur, Justin C.; Morgello, Susan; Simpson, David M.; McCutchan, J. A.; Grant, Igor

2016-01-01

Background. Anemia has been linked to adverse human immunodeficiency virus (HIV) outcomes, including dementia, in the era before highly active antiretroviral therapy (HAART). Milder forms of HIV-associated neurocognitive disorder (HAND) remain common in HIV-infected persons, despite HAART, but whether anemia predicts HAND in the HAART era is unknown. Methods. We evaluated time-dependent associations of anemia and cross-sectional associations of red blood cell indices with neurocognitive impairment in a multicenter, HAART-era HIV cohort study (N = 1261), adjusting for potential confounders, including age, nadir CD4+ T-cell count, zidovudine use, and comorbid conditions. Subjects underwent comprehensive neuropsychiatric and neuromedical assessments. Results. HAND, defined according to standardized criteria, occurred in 595 subjects (47%) at entry. Mean corpuscular volume and mean corpuscular hemoglobin were positively associated with the global deficit score, a continuous measure of neurocognitive impairment (both P < .01), as well as with all HAND, milder forms of HAND, and HIV-associated dementia in multivariable analyses (all P < .05). Anemia independently predicted development of HAND during a median follow-up of 72 months (adjusted hazard ratio, 1.55; P < .01). Conclusions. Anemia and red blood cell indices predict HAND in the HAART era and may contribute to risk assessment. Future studies should address whether treating anemia may help to prevent HAND or improve cognitive function in HIV-infected persons. PMID:26690344

Density Functional Theory Investigations of D-A-D' Structural Molecules as Donor Materials in Organic Solar Cell.

PubMed

Chen, Junxian; Liu, Qingyu; Li, Hao; Zhao, Zhigang; Lu, Zhiyun; Huang, Yan; Xu, Dingguo

2018-01-01

Squaraine core based small molecules in bulk heterojunction organic solar cells have received extensive attentions due to their distinguished photochemical properties in far red and infrared domain. In this paper, combining theoretical simulations and experimental syntheses and characterizations, three major factors (fill factor, short circuit and open-cirvuit voltage) have been carried out together to achieve improvement of power conversion efficiencies of solar cells. As model material systems with D-A-D' framework, two asymmetric squaraines (CNSQ and CCSQ-Tol) as donor materials in bulk heterojunction organic solar cell were synthesized and characterized. Intensive density functional theory computations were applied to identify some direct connections between three factors and corresponding molecular structural properties. It then helps us to predict one new molecule of CCSQ'-Ox that matches all the requirements to improve the power conversion efficiency.

Supercapacitive microbial fuel cell: Characterization and analysis for improved charge storage/delivery performance.

PubMed

Houghton, Jeremiah; Santoro, Carlo; Soavi, Francesca; Serov, Alexey; Ieropoulos, Ioannis; Arbizzani, Catia; Atanassov, Plamen

2016-10-01

Supercapacitive microbial fuel cells with various anode and cathode dimensions were investigated in order to determine the effect on cell capacitance and delivered power quality. The cathode size was shown to be the limiting component of the system in contrast to anode size. By doubling the cathode area, the peak power output was improved by roughly 120% for a 10ms pulse discharge and internal resistance of the cell was decreased by ∼47%. A model was constructed in order to predict the performance of a hypothetical cylindrical MFC design with larger relative cathode size. It was found that a small device based on conventional materials with a volume of approximately 21cm(3) would be capable of delivering a peak power output of approximately 25mW at 70mA, corresponding to ∼1300Wm(-3). Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine.

PubMed

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma'ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS 2 . The L1000CDS 2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS 2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS 2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS 2 , we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS 2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS 2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

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

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

New directions in diagnostic evaluation of insect allergy.

PubMed

Golden, David B K

2014-08-01

Diagnosis of insect sting allergy and prediction of risk of sting anaphylaxis are often difficult because tests for venom-specific IgE antibodies have a limited positive predictive value and do not reliably predict the severity of sting reactions. Component-resolved diagnosis using recombinant venom allergens has shown promise in improving the specificity of diagnostic testing for insect sting allergy. Basophil activation tests have been explored as more sensitive assays for identification of patients with insect allergy and for prediction of clinical outcomes. Measurement of mast cell mediators reflects the underlying risk for more severe reactions and limited clinical response to treatment. Measurement of IgE to recombinant venom allergens can distinguish cross-sensitization from dual sensitization to honeybee and vespid venoms, thus helping to limit venom immunotherapy to a single venom instead of multiple venoms in many patients. Basophil activation tests can detect venom allergy in patients who show no detectable venom-specific IgE in standard diagnostic tests and can predict increased risk of systemic reactions to venom immunotherapy, and to stings during and after stopping venom immunotherapy. The risk of severe or fatal anaphylaxis to stings can also be predicted by measurement of baseline serum tryptase or other mast cell mediators.

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

Daily, Michael D.; Olsen, Brett N.; Schlesinger, Paul H.

In mammalian cells cholesterol is essential for membrane function, but in excess can be cytototoxic. The cellular response to acute cholesterol loading involves biophysical-based mechanisms that regulate cholesterol levels, through modulation of the “activity” or accessibility of cholesterol to extra-membrane acceptors. Experiments and united atom (UA) simulations show that at high concentrations of cholesterol, lipid bilayers thin significantly and cholesterol availability to external acceptors increases substantially. Such cholesterol activation is critical to its trafficking within cells. Here we aim to reduce the computational cost to enable simulation of large and complex systems involved in cholesterol regulation, such as those includingmore » oxysterols and cholesterol-sensing proteins. To accomplish this, we have modified the published MARTINI coarse-grained force field to improve its predictions of cholesterol-induced changes in both macroscopic and microscopic properties of membranes. Most notably, MARTINI fails to capture both the (macroscopic) area condensation and membrane thickening seen at less than 30% cholesterol and the thinning seen above 40% cholesterol. The thinning at high concentration is critical to cholesterol activation. Microscopic properties of interest include cholesterol-cholesterol radial distribution functions (RDFs), tilt angle, and accessible surface area. First, we develop an “angle-corrected” model wherein we modify the coarse-grained bond angle potentials based on atomistic simulations. This modification significantly improves prediction of macroscopic properties, most notably the thickening/thinning behavior, and also slightly improves microscopic property prediction relative to MARTINI. Second, we add to the angle correction a “volume correction” by also adjusting phospholipid bond lengths to achieve a more accurate volume per molecule. The angle + volume correction substantially further improves the quantitative agreement of the macroscopic properties (area per molecule and thickness) with united atom simulations. However, this improvement also reduces the accuracy of microscopic predictions like radial distribution functions and cholesterol tilt below that of either MARTINI or the angle-corrected model. Thus, while both of our forcefield corrections improve MARTINI, the combined angle and volume correction should be used for problems involving sterol effects on the overall structure of the membrane, while our angle-corrected model should be used in cases where the properties of individual lipid and sterol models are critically important.« less

Performance and cost analysis of Siriraj liquid-based cytology: a direct-to-vial study.

PubMed

Laiwejpithaya, Somsak; Benjapibal, Mongkol; Laiwejpithaya, Sujera; Wongtiraporn, Weerasak; Sangkarat, Suthi; Rattanachaiyanont, Manee

2009-12-01

To compare the cytological diagnoses, specimen adequacy, and cost of the Siriraj liquid-based cytology (LBC) with those of the conventional smear technique. An observational study with historical comparison was conducted in a tertiary university hospital. Cytological reports of 23,676 Siriraj-LBC specimens obtained in 2006 were compared with those of 25,510 conventional smears obtained in 2004. Overall prevalence of abnormal cervical cytology detected by conventional smear was 1.76% and by Siriraj-LBC was 3.70%. Compared with the conventional method, the Siriraj-LBC yielded a significantly higher overall detection rate of abnormal cervical cytology, with a 110.23% increase in the detection rate (P<0.001), mainly due to the increase in diagnosis of squamous intraepithelial lesions (SIL), both low and high grade, together with atypical squamous cells of undetermined significance, "atypical squamous cells cannot exclude HSIL", and malignancies, but not atypical glandular cells. The Siriraj-LBC had a smaller proportion of unsatisfactory slides (4.94% vs. 18.60%, P<0.001) and a higher negative predictive value (96.33% vs. 92.74%, P=0.001), but no difference in positive predictive value (83.03% vs. 86.83%, P=0.285). The cost of Siriraj-LBC was approximately 67% higher than that of the conventional cytology used in Siriraj Hospital and 50-70% lower than that of the commercially available LBC techniques in Thailand. The Siriraj-LBC increases the detection rate of abnormal cytology, improves specimen adequacy, and enhances the negative predictive value without compromising the positive predictive value. For centers where conventional Pap smear does not perform well, the introduction of a low cost Siriraj-LBC might help to improve performance and it may be an economical alternative to the commercially available liquid-based cytology.

Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells

NASA Astrophysics Data System (ADS)

Harris, Stephen J.; Harris, David J.; Li, Chen

2017-02-01

There are relatively few publications that assess capacity decline in enough commercial cells to quantify cell-to-cell variation, but those that do show a surprisingly wide variability. Capacity curves cross each other often, a challenge for efforts to measure the state of health and predict the remaining useful life (RUL) of individual cells. We analyze capacity fade statistics for 24 commercial pouch cells, providing an estimate for the time to 5% failure. Our data indicate that RUL predictions based on remaining capacity or internal resistance are accurate only once the cells have already sorted themselves into "better" and "worse" ones. Analysis of our failure data, using maximum likelihood techniques, provide uniformly good fits for a variety of definitions of failure with normal and with 2- and 3-parameter Weibull probability density functions, but we argue against using a 3-parameter Weibull function for our data. pdf fitting parameters appear to converge after about 15 failures, although business objectives should ultimately determine whether data from a given number of batteries provides sufficient confidence to end lifecycle testing. Increased efforts to make batteries with more consistent lifetimes should lead to improvements in battery cost and safety.

Numerical simulation of proton exchange membrane fuel cells at high operating temperature

NASA Astrophysics Data System (ADS)

Peng, Jie; Lee, Seung Jae

A three-dimensional, single-phase, non-isothermal numerical model for proton exchange membrane (PEM) fuel cell at high operating temperature (T ≥ 393 K) was developed and implemented into a computational fluid dynamic (CFD) code. The model accounts for convective and diffusive transport and allows predicting the concentration of species. The heat generated from electrochemical reactions, entropic heat and ohmic heat arising from the electrolyte ionic resistance were considered. The heat transport model was coupled with the electrochemical and mass transport models. The product water was assumed to be vaporous and treated as ideal gas. Water transportation across the membrane was ignored because of its low water electro-osmosis drag force in the polymer polybenzimidazole (PBI) membrane. The results show that the thermal effects strongly affect the fuel cell performance. The current density increases with the increasing of operating temperature. In addition, numerical prediction reveals that the width and distribution of gas channel and current collector land area are key optimization parameters for the cell performance improvement.

Adult Acute Myeloid Leukemia Treatment (PDQ®)—Health Professional Version

Cancer.gov

Acute myeloid leukemia (AML; also called acute myelogenous leukemia, acute nonlymphocytic leukemia) treatment advances have resulted in substantially improved CR rates. Cytogenetic analysis helps predict outcomes of treatment which includes chemotherapy, radiation, and stem cell transplant. Get detailed information about AML in this clinician summary.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.

PubMed

Hong, Huixiao; Shen, Jie; Ng, Hui Wen; Sakkiah, Sugunadevi; Ye, Hao; Ge, Weigong; Gong, Ping; Xiao, Wenming; Tong, Weida

2016-03-25

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PubMed

Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

2017-01-01

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts.

PubMed

Cellot, Giada; Cilia, Emanuele; Cipollone, Sara; Rancic, Vladimir; Sucapane, Antonella; Giordani, Silvia; Gambazzi, Luca; Markram, Henry; Grandolfo, Micaela; Scaini, Denis; Gelain, Fabrizio; Casalis, Loredana; Prato, Maurizio; Giugliano, Michele; Ballerini, Laura

2009-02-01

Carbon nanotubes have been applied in several areas of nerve tissue engineering to probe and augment cell behaviour, to label and track subcellular components, and to study the growth and organization of neural networks. Recent reports show that nanotubes can sustain and promote neuronal electrical activity in networks of cultured cells, but the ways in which they affect cellular function are still poorly understood. Here, we show, using single-cell electrophysiology techniques, electron microscopy analysis and theoretical modelling, that nanotubes improve the responsiveness of neurons by forming tight contacts with the cell membranes that might favour electrical shortcuts between the proximal and distal compartments of the neuron. We propose the 'electrotonic hypothesis' to explain the physical interactions between the cell and nanotube, and the mechanisms of how carbon nanotubes might affect the collective electrical activity of cultured neuronal networks. These considerations offer a perspective that would allow us to predict or engineer interactions between neurons and carbon nanotubes.

DNA methylation-based measures of biological age: meta-analysis predicting time to death

PubMed Central

Chen, Brian H.; Marioni, Riccardo E.; Colicino, Elena; Peters, Marjolein J.; Ward-Caviness, Cavin K.; Tsai, Pei-Chien; Roetker, Nicholas S.; Just, Allan C.; Demerath, Ellen W.; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R.; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P.; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L.; Murabito, Joanne M.; Bandinelli, Stefania; Hernandez, Dena G.; Melzer, David; Nalls, Michael; Pilling, Luke C.; Price, Timothy R.; Singleton, Andrew B.; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M.; Shah, Sonia; Wray, Naomi R.; McRae, Allan F.; Franco, Oscar H.; Hofman, Albert; Uitterlinden, André G.; Absher, Devin; Assimes, Themistocles; Levine, Morgan E.; Lu, Ake T.; Tsao, Philip S.; Hou, Lifang; Manson, JoAnn E.; Carty, Cara L.; LaCroix, Andrea Z.; Reiner, Alexander P.; Spector, Tim D.; Feinberg, Andrew P.; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T.; Peters, Annette; Deary, Ian J.; Pankow, James S.; Ferrucci, Luigi; Horvath, Steve

2016-01-01

Estimates of biological age based on DNA methylation patterns, often referred to as “epigenetic age”, “DNAm age”, have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2×10−9), independent of chronological age, even after adjusting for additional risk factors (p<5.4×10−4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5×10−43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality. PMID:27690265

Improved Performance and Safety for High Energy Batteries Through Use of Hazard Anticipation and Capacity Prediction

NASA Technical Reports Server (NTRS)

Atwater, Terrill

1993-01-01

Prediction of the capacity remaining in used high rate, high energy batteries is important information to the user. Knowledge of the capacity remaining in used batteries results in better utilization. This translates into improved readiness and cost savings due to complete, efficient use. High rate batteries, due to their chemical nature, are highly sensitive to misuse (i.e., over discharge or very high rate discharge). Battery failure due to misuse or manufacturing defects could be disastrous. Since high rate, high energy batteries are expensive and energetic, a reliable method of predicting both failures and remaining energy has been actively sought. Due to concerns over safety, the behavior of lithium/sulphur dioxide cells at different temperatures and current drains was examined. The main thrust of this effort was to determine failure conditions for incorporation in hazard anticipation circuitry. In addition, capacity prediction formulas have been developed from test data. A process that performs continuous, real-time hazard anticipation and capacity prediction was developed. The introduction of this process into microchip technology will enable the production of reliable, safe, and efficient high energy batteries.

Hepatic Differentiation and Maturation of Human Embryonic Stem Cells Cultured in a Perfused Three-Dimensional Bioreactor

PubMed Central

Synnergren, Jane; Jensen, Janne; Björquist, Petter; Ingelman-Sundberg, Magnus

2013-01-01

Drug-induced liver injury is a serious and frequently occurring adverse drug reaction in the clinics and is hard to predict during preclinical studies. Today, primary hepatocytes are the most frequently used cell model for drug discovery and prediction of toxicity. However, their use is marred by high donor variability regarding drug metabolism and toxicity, and instable expression levels of liver-specific genes such as cytochromes P450. An in vitro model system based on human embryonic stem cells (hESC), with their unique properties of pluripotency and self-renewal, has potential to provide a stable and unlimited supply of human hepatocytes. Much effort has been made to direct hESC toward the hepatic lineage, mostly using 2-dimensional (2D) cultures. Although the results are encouraging, these cells lack important functionality. Here, we investigate if hepatic differentiation of hESC can be improved by using a 3-dimensional (3D) bioreactor system. Human ESCs were differentiated toward the hepatic lineage using the same cells in either the 3D or 2D system. A global transcriptional analysis identified important differences between the 2 differentiation regimes, and we identified 10 pathways, highly related to liver functions, which were significantly upregulated in cells differentiated in the bioreactor compared to 2D control cultures. The enhanced hepatic differentiation observed in the bioreactor system was also supported by immunocytochemistry. Taken together, our results suggest that hepatic differentiation of hESC is improved when using this 3D bioreactor technology as compared to 2D culture systems. PMID:22970843

Hepatic differentiation and maturation of human embryonic stem cells cultured in a perfused three-dimensional bioreactor.

PubMed

Sivertsson, Louise; Synnergren, Jane; Jensen, Janne; Björquist, Petter; Ingelman-Sundberg, Magnus

2013-02-15

Drug-induced liver injury is a serious and frequently occurring adverse drug reaction in the clinics and is hard to predict during preclinical studies. Today, primary hepatocytes are the most frequently used cell model for drug discovery and prediction of toxicity. However, their use is marred by high donor variability regarding drug metabolism and toxicity, and instable expression levels of liver-specific genes such as cytochromes P450. An in vitro model system based on human embryonic stem cells (hESC), with their unique properties of pluripotency and self-renewal, has potential to provide a stable and unlimited supply of human hepatocytes. Much effort has been made to direct hESC toward the hepatic lineage, mostly using 2-dimensional (2D) cultures. Although the results are encouraging, these cells lack important functionality. Here, we investigate if hepatic differentiation of hESC can be improved by using a 3-dimensional (3D) bioreactor system. Human ESCs were differentiated toward the hepatic lineage using the same cells in either the 3D or 2D system. A global transcriptional analysis identified important differences between the 2 differentiation regimes, and we identified 10 pathways, highly related to liver functions, which were significantly upregulated in cells differentiated in the bioreactor compared to 2D control cultures. The enhanced hepatic differentiation observed in the bioreactor system was also supported by immunocytochemistry. Taken together, our results suggest that hepatic differentiation of hESC is improved when using this 3D bioreactor technology as compared to 2D culture systems.

Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

PubMed

Lesnik, Keaton Larson; Liu, Hong

2017-09-19

The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Simplifying the complexity of resistance heterogeneity in metastasis

PubMed Central

Lavi, Orit; Greene, James M.; Levy, Doron; Gottesman, Michael M.

2014-01-01

The main goal of treatment regimens for metastasis is to control growth rates, not eradicate all cancer cells. Mathematical models offer methodologies that incorporate high-throughput data with dynamic effects on net growth. The ideal approach would simplify, but not over-simplify, a complex problem into meaningful and manageable estimators that predict a patient’s response to specific treatments. Here, we explore three fundamental approaches with different assumptions concerning resistance mechanisms, in which the cells are categorized into either discrete compartments or described by a continuous range of resistance levels. We argue in favor of modeling resistance as a continuum and demonstrate how integrating cellular growth rates, density-dependent versus exponential growth, and intratumoral heterogeneity improves predictions concerning the resistance heterogeneity of metastases. PMID:24491979

Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition.

PubMed

Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Möller, Steffen; Hartmann, Enno; Kalies, Kai-Uwe; Suganthan, P N; Martinetz, Thomas

2010-12-01

Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail. This work proposes a model for the sorting of proteins that are involved in apoptosis, allowing us to both the prediction of their subcellular locations as well as the molecular properties that contributed to it. We report a novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physicochemical properties. GA is used for selecting a near-optimal subset of informative features that is most relevant for the classification. Jackknife cross-validation is applied to test the predictive capability of the proposed method on 317 apoptosis proteins. Our method achieved 85.80% accuracy using all 119 features and 89.91% accuracy for 25 features selected by GA. Our models were examined by a test dataset of 98 apoptosis proteins and obtained an overall accuracy of 90.34%. The results show that the proposed approach is promising; it is able to select small subsets of features and still improves the classification accuracy. Our model can contribute to the understanding of programmed cell death and drug discovery. The software and dataset are available at http://www.inb.uni-luebeck.de/tools-demos/apoptosis/GASVM.

Does folic acid supplementation prevent or promote colorectal cancer? Results from model-based predictions.

PubMed

Luebeck, E Georg; Moolgavkar, Suresh H; Liu, Amy Y; Boynton, Alanna; Ulrich, Cornelia M

2008-06-01

Folate is essential for nucleotide synthesis, DNA replication, and methyl group supply. Low-folate status has been associated with increased risks of several cancer types, suggesting a chemopreventive role of folate. However, recent findings on giving folic acid to patients with a history of colorectal polyps raise concerns about the efficacy and safety of folate supplementation and the long-term health effects of folate fortification. Results suggest that undetected precursor lesions may progress under folic acid supplementation, consistent with the role of folate role in nucleotide synthesis and cell proliferation. To better understand the possible trade-offs between the protective effects due to decreased mutation rates and possibly concomitant detrimental effects due to increased cell proliferation of folic acid, we used a biologically based mathematical model of colorectal carcinogenesis. We predict changes in cancer risk based on timing of treatment start and the potential effect of folic acid on cell proliferation and mutation rates. Changes in colorectal cancer risk in response to folic acid supplementation are likely a complex function of treatment start, duration, and effect on cell proliferation and mutations rates. Predicted colorectal cancer incidence rates under supplementation are mostly higher than rates without folic acid supplementation unless supplementation is initiated early in life (before age 20 years). To the extent to which this model predicts reality, it indicates that the effect on cancer risk when starting folic acid supplementation late in life is small, yet mostly detrimental. Experimental studies are needed to provide direct evidence for this dual role of folate in colorectal cancer and to validate and improve the model predictions.

Efficient multiscale magnetic-domain analysis of iron-core material under mechanical stress

NASA Astrophysics Data System (ADS)

Nishikubo, Atsushi; Ito, Shumpei; Mifune, Takeshi; Matsuo, Tetsuji; Kaido, Chikara; Takahashi, Yasuhito; Fujiwara, Koji

2018-05-01

For an efficient analysis of magnetization, a partial-implicit solution method is improved using an assembled domain structure model with six-domain mesoscopic particles exhibiting pinning-type hysteresis. The quantitative analysis of non-oriented silicon steel succeeds in predicting the stress dependence of hysteresis loss with computation times greatly reduced by using the improved partial-implicit method. The effect of cell division along the thickness direction is also evaluated.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

NASA Astrophysics Data System (ADS)

Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

2017-03-01

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.

PubMed

Pei, Linmin; Reza, Syed M S; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M

2017-02-11

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

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

Smith, Kandler A; Santhanagopalan, Shriram; Yang, Chuanbo

Computer models are helping to accelerate the design and validation of next generation batteries and provide valuable insights not possible through experimental testing alone. Validated 3-D physics-based models exist for predicting electrochemical performance, thermal and mechanical response of cells and packs under normal and abuse scenarios. The talk describes present efforts to make the models better suited for engineering design, including improving their computation speed, developing faster processes for model parameter identification including under aging, and predicting the performance of a proposed electrode material recipe a priori using microstructure models.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Model-directed engineering of "difficult-to-express" monoclonal antibody production by Chinese hamster ovary cells.

PubMed

Pybus, Leon P; Dean, Greg; West, Nathan R; Smith, Andrew; Daramola, Olalekan; Field, Ray; Wilkinson, Stephen J; James, David C

2014-02-01

Despite improvements in volumetric titer for monoclonal antibody (MAb) production processes using Chinese hamster ovary (CHO) cells, some "difficult-to-express" (DTE) MAbs inexplicably reach much lower process titers. These DTE MAbs require intensive cell line and process development activity, rendering them more costly or even unsuitable to manufacture. To rapidly and rationally identify an optimal strategy to improve production of DTE MAbs, we have developed an engineering design platform combining high-yielding transient production, empirical modeling of MAb synthesis incorporating an unfolded protein response (UPR) regulatory loop with directed expression and cell engineering approaches. Utilizing a panel of eight IgG1 λ MAbs varying >4-fold in volumetric titer, we showed that MAb-specific limitations on folding and assembly rate functioned to induce a proportionate UPR in host CHO cells with a corresponding reduction in cell growth rate. Derived from comparative empirical modeling of cellular constraints on the production of each MAb we employed two strategies to increase production of DTE MAbs designed to avoid UPR induction through an improvement in the rate/cellular capacity for MAb folding and assembly reactions. Firstly, we altered the transfected LC:HC gene ratio and secondly, we co-expressed a variety of molecular chaperones, foldases or UPR transactivators (BiP, CypB, PDI, and active forms of ATF6 and XBP1) with recombinant MAbs. DTE MAb production was significantly improved by both strategies, although the mode of action was dependent upon the approach employed. Increased LC:HC ratio or CypB co-expression improved cell growth with no effect on qP. In contrast, BiP, ATF6c and XBP1s co-expression increased qP and reduced cell growth. This study demonstrates that expression-engineering strategies to improve production of DTE proteins in mammalian cells should be product specific, and based on rapid predictive tools to assess the relative impact of different engineering interventions. © 2013 Wiley Periodicals, Inc.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1).

PubMed

Singh, Aman P; Maass, Katie F; Betts, Alison M; Wittrup, K Dane; Kulkarni, Chethana; King, Lindsay E; Khot, Antari; Shah, Dhaval K

2016-07-01

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution function

DOE PAGES

He, Lian; Wu, Stephen G.; Wan, Ni; ...

2015-12-24

In this study, genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs. Analysis of the integratedmore » GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and CO 2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %. Conclusions: The multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.« less

Photovoltaic Engineering Testbed Designed for Calibrating Photovoltaic Devices in Space

NASA Technical Reports Server (NTRS)

Landis, Geoffrey A.

2002-01-01

Accurate prediction of the performance of solar arrays in space requires that the cells be tested in comparison with a space-flown standard. Recognizing that improvements in future solar cell technology will require an ever-increasing fidelity of standards, the Photovoltaics and Space Environment Branch at the NASA Glenn Research Center, in collaboration with the Ohio Aerospace Institute, designed a prototype facility to allow routine calibration, measurement, and qualification of solar cells on the International Space Station, and then the return of the cells to Earth for laboratory use. For solar cell testing, the Photovoltaic Engineering Testbed (PET) site provides a true air-mass-zero (AM0) solar spectrum. This allows solar cells to be accurately calibrated using the full spectrum of the Sun.

Improving Interpersonal Communication through Community Service

ERIC Educational Resources Information Center

Hoffman, August John; Wallach, Julie; Sanchez, Eduardo; Afkhami, Hasti

2009-01-01

The current study sought to determine if community based gardening projects would reduce perceptions of the need to use communication devices--cell phones or text messaging--and increase the likelihood of participating in future volunteer projects. Results strongly support the predictions in that the experimental group post-test mean score of the…

Toward preclinical predictive drug testing for metabolism and hepatotoxicity by using in vitro models derived from human embryonic stem cells and human cell lines - a report on the Vitrocellomics EU-project.

PubMed

Mandenius, Carl-Fredrik; Andersson, Tommy B; Alves, Paula M; Batzl-Hartmann, Christine; Björquist, Petter; Carrondo, Manuel J T; Chesne, Christophe; Coecke, Sandra; Edsbagge, Josefina; Fredriksson, J Magnus; Gerlach, Jörg C; Heinzle, Elmar; Ingelman-Sundberg, Magnus; Johansson, Inger; Küppers-Munther, Barbara; Müller-Vieira, Ursula; Noor, Fozia; Zeilinger, Katrin

2011-05-01

Drug-induced liver injury is a common reason for drug attrition in late clinical phases, and even for post-launch withdrawals. As a consequence, there is a broad consensus in the pharmaceutical industry, and within regulatory authorities, that a significant improvement of the current in vitro test methodologies for accurate assessment and prediction of such adverse effects is needed. For this purpose, appropriate in vivo-like hepatic in vitro models are necessary, in addition to novel sources of human hepatocytes. In this report, we describe recent and ongoing research toward the use of human embryonic stem cell (hESC)-derived hepatic cells, in conjunction with new and improved test methods, for evaluating drug metabolism and hepatotoxicity. Recent progress on the directed differentiation of human embryonic stem cells to the functional hepatic phenotype is reported, as well as the development and adaptation of bioreactors and toxicity assay technologies for the testing of hepatic cells. The aim of achieving a testing platform for metabolism and hepatotoxicity assessment, based on hESC-derived hepatic cells, has advanced markedly in the last 2-3 years. However, great challenges still remain, before such new test systems could be routinely used by the industry. In particular, we give an overview of results from the Vitrocellomics project (EU Framework 6) and discuss these in relation to the current state-of-the-art and the remaining difficulties, with suggestions on how to proceed before such in vitro systems can be implemented in industrial discovery and development settings and in regulatory acceptance. 2011 FRAME.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling.

PubMed

Kornecki, Martin; Strube, Jochen

2018-03-16

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network-either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

PubMed Central

Kornecki, Martin; Strube, Jochen

2018-01-01

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream. PMID:29547557

Dynamic Call Admission Control Scheme Based on Predictive User Mobility Behavior for Cellular Networks

NASA Astrophysics Data System (ADS)

Intarasothonchun, Silada; Thipchaksurat, Sakchai; Varakulsiripunth, Ruttikorn; Onozato, Yoshikuni

In this paper, we propose a modified scheme of MSODB and PMS, called Predictive User Mobility Behavior (PUMB) to improve performance of resource reservation and call admission control for cellular networks. This algorithm is proposed in which bandwidth is allocated more efficiently to neighboring cells by key mobility parameters in order to provide QoS guarantees for transferring traffic. The probability is used to form a cluster of cells and the shadow cluster, where a mobile unit is likely to visit. When a mobile unit may change the direction and migrate to the cell that does not belong to its shadow cluster, we can support it by making efficient use of predicted nonconforming call. Concomitantly, to ensure continuity of on-going calls with better utilization of resources, bandwidth is borrowed from predicted nonconforming calls and existing adaptive calls without affecting the minimum QoS guarantees. The performance of the PUMB is demonstrated by simulation results in terms of new call blocking probability, handoff call dropping probability, bandwidth utilization, call successful probability, and overhead message transmission when arrival rate and speed of mobile units are varied. Our results show that PUMB provides the better performances comparing with those of MSODB and PMS under different traffic conditions.

Risk prediction for malignant conversion of oral epithelial dysplasia by hypoxia related protein expression.

PubMed

Zhang, Xianglan; Han, Seonhui; Han, Hye-Yeon; Ryu, Mi Heon; Kim, Ki-Yeol; Choi, Eun-Joo; Cha, In-Ho; Kim, Jin

2013-08-01

Increased aerobic glycolysis is a unique finding in cancers and hypoxia-related proteins are associated with aerobic glycolysis. Therefore, we aimed to investigate whether hypoxia-related proteins can be predictive markers for malignant conversion of oral premalignant lesions with epithelial dysplasia (OED). Expression of HIF-1α, Glut-1 and CA9 were detected in clinical samples of eight normal oral mucosa, 85 transitional areas of oral squamous cell carcinoma (OSCC) and 28 OED with or without malignant conversion using immunohistochemistry and were also comparatively detected in immortalised human oral keratinocyte (IHOK) and OSCC cell lines under hypoxia using immunoblotting. Sequential expression of HIF-1α, Glut-1 and CA9 was found both in transitional areas of OSCC and cell lines of IHOK and OSCC under hypoxia, supporting hypoxia-aerobic glycolysis-acidosis axis. Expression of all proteins showed significant association with malignant conversion of OED and CA9 was an independent risk factor of malignant transformation of OED. But the predictability of malignant transformation was improved when all three proteins were applied together. High expression of CA9 was an independent predictive marker of malignant conversion. Moreover, the combined application of these three proteins may be useful to assess the risk of malignant conversion of OED.

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins.

PubMed

Langó, Tamás; Róna, Gergely; Hunyadi-Gulyás, Éva; Turiák, Lilla; Varga, Julia; Dobson, László; Várady, György; Drahos, László; Vértessy, Beáta G; Medzihradszky, Katalin F; Szakács, Gergely; Tusnády, Gábor E

2017-02-13

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

Guidelines for improving the reproducibility of quantitative multiparameter immunofluorescence measurements by laser scanning cytometry on fixed cell suspensions from human solid tumors.

PubMed

Shackney, Stanley; Emlet, David R; Pollice, Agnese; Smith, Charles; Brown, Kathryn; Kociban, Deborah

2006-01-01

Laser scanning Cytometry (LSC) is a versatile technology that makes it possible to perform multiple measurements on individual cells and correlate them cell by cell with other cellular features. It would be highly desirable to be able to perform reproducible, quantitative, correlated cell-based immunofluorescence studies on individual cells from human solid tumors. However, such studies can be challenging because of the presence of large numbers of cell aggregates and other confounding factors. Techniques have been developed to deal with cell aggregates in data sets collected by LSC. Experience has also been gained in addressing other key technical and methodological issues that can affect the reproducibility of such cell-based immunofluorescence measurements. We describe practical aspects of cell sample collection, cell fixation and staining, protocols for performing multiparameter immunofluorescence measurements by LSC, use of controls and reference samples, and approaches to data analysis that we have found useful in improving the accuracy and reproducibility of LSC data obtained in human tumor samples. We provide examples of the potential advantages of LSC in examining quantitative aspects of cell-based analysis. Improvements in the quality of cell-based multiparameter immunofluorescence measurements make it possible to extract useful information from relatively small numbers of cells. This, in turn, permits the performance of multiple multicolor panels on each tumor sample. With links among the different panels that are provided by overlapping measurements, it is possible to develop increasingly more extensive profiles of intracellular expression of multiple proteins in clinical samples of human solid tumors. Examples of such linked panels of measurements are provided. Advances in methodology can improve cell-based multiparameter immunofluorescence measurements on cell suspensions from human solid tumors by LSC for use in prognostic and predictive clinical applications. Copyright (c) 2005 Wiley-Liss, Inc.

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach

NASA Astrophysics Data System (ADS)

Luan, Feng; Kleandrova, Valeria V.; González-Díaz, Humberto; Ruso, Juan M.; Melo, André; Speck-Planche, Alejandro; Cordeiro, M. Natália D. S.

2014-08-01

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr01285b

Novel cell population data from a haematology analyzer can predict timing and efficiency of stem cell transplantation.

PubMed

Golubeva, Vera; Mikhalevich, Juliana; Novikova, Julia; Tupizina, Olga; Trofimova, Svetlana; Zueva, Yekaterina

2014-02-01

Autologous hematopoietic stem cell transplantation (AHSCT) is a necessary component for many oncohematological diseases treatment. For a successful result of AHSCT a sufficient quantity of hematopoietic stem cells (HSCs) is needed. It has been proposed that morphological changes of myeloid cells could reflect the processes of bone marrow stimulation and may provide useful information to predict the stimulation efficiency and expected outcome of CD34(+) stem cells. The Beckman Coulter Cellular Analysis System DxH800 performs Flow Cytometric Digital Morphology analysis of leukocytes. All leukocyte cellular measurements can be reported as numerical values called Cell Population Data (CPD), which are able to detect morphological changes in the cell size and distribution of neutrophils. Our findings suggest that the changes in neutrophil CPD were detectable 2-4days before the observed increase in CD34(+) count in the peripheral blood and can potentially improve the management of patients. There was also a good correlation between MN-V-NE and ImmNeIndex with the CD34(+) count suggesting they can be used as a surrogate for the CD34(+) count (r=0.67 and 0.65 p<0.005 respectively). Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Antibody specific epitope prediction-emergence of a new paradigm.

PubMed

Sela-Culang, Inbal; Ofran, Yanay; Peters, Bjoern

2015-04-01

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data. Copyright © 2015 Elsevier B.V. All rights reserved.

Improving a free air breathing proton exchange membrane fuel cell through the Maximum Efficiency Point Tracking method

NASA Astrophysics Data System (ADS)

Higuita Cano, Mauricio; Mousli, Mohamed Islam Aniss; Kelouwani, Sousso; Agbossou, Kodjo; Hammoudi, Mhamed; Dubé, Yves

2017-03-01

This work investigates the design and validation of a fuel cell management system (FCMS) which can perform when the fuel cell is at water freezing temperature. This FCMS is based on a new tracking technique with intelligent prediction, which combined the Maximum Efficiency Point Tracking with variable perturbation-current step and the fuzzy logic technique (MEPT-FL). Unlike conventional fuel cell control systems, our proposed FCMS considers the cold-weather conditions, the reduction of fuel cell set-point oscillations. In addition, the FCMS is built to respond quickly and effectively to the variations of electric load. A temperature controller stage is designed in conjunction with the MEPT-FL in order to operate the FC at low-temperature values whilst tracking at the same time the maximum efficiency point. The simulation results have as well experimental validation suggest that propose approach is effective and can achieve an average efficiency improvement up to 8%. The MEPT-FL is validated using a Proton Exchange Membrane Fuel Cell (PEMFC) of 500 W.

Discriminating modes of toxic action in mice using toxicity in BALB/c mouse fibroblast (3T3) cells.

PubMed

Huang, Tao; Yan, Lichen; Zheng, Shanshan; Wang, Yue; Wang, Xiaohong; Fan, Lingyun; Li, Chao; Zhao, Yuanhui; Martyniuk, Christopher J

2017-12-01

The objective of this study was to determine whether toxicity in mouse fibroblast cells (3T3 cells) could predict toxicity in mice. Synthesized data on toxicity was subjected to regression analysis and it was observed that relationship of toxicities between mice and 3T3 cells was not strong (R 2  = 0.41). Inclusion of molecular descriptors (e.g. ionization, pKa) improved the regression to R 2  = 0.56, indicating that this relationship is influenced by kinetic processes of chemicals or specific toxic mechanisms associated to the compounds. However, to determine if we were able to discriminate modes of action (MOAs) in mice using the toxicities generated from 3T3 cells, compounds were first classified into "baseline" and "reactive" guided by the toxic ratio (TR) for each compound in mice. Sequence, binomial and recursive partitioning analyses provided strong predictions of MOAs in mice based upon toxicities in 3T3 cells. The correct classification of MOAs based on these methods was 86%. Nearly all the baseline compounds predicted from toxicities in 3T3 cells were identified as baseline compounds from the TR in mice. The incorrect assignment of MOAs for some compounds is hypothesized to be due to experimental uncertainty that exists in toxicity assays for both mice and 3T3 cells. Conversely, lack of assignment can also arise because some reactive compounds have MOAs that are different in mice compared to 3T3 cells. The methods developed here are novel and contribute to efforts to reduce animal numbers in toxicity tests that are used to evaluate risks associated with organic pollutants in the environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial homogenization methods for pin-by-pin neutron transport calculations

NASA Astrophysics Data System (ADS)

Kozlowski, Tomasz

For practical reactor core applications low-order transport approximations such as SP3 have been shown to provide sufficient accuracy for both static and transient calculations with considerably less computational expense than the discrete ordinate or the full spherical harmonics methods. These methods have been applied in several core simulators where homogenization was performed at the level of the pin cell. One of the principal problems has been to recover the error introduced by pin-cell homogenization. Two basic approaches to treat pin-cell homogenization error have been proposed: Superhomogenization (SPH) factors and Pin-Cell Discontinuity Factors (PDF). These methods are based on well established Equivalence Theory and Generalized Equivalence Theory to generate appropriate group constants. These methods are able to treat all sources of error together, allowing even few-group diffusion with one mesh per cell to reproduce the reference solution. A detailed investigation and consistent comparison of both homogenization techniques showed potential of PDF approach to improve accuracy of core calculation, but also reveal its limitation. In principle, the method is applicable only for the boundary conditions at which it was created, i.e. for boundary conditions considered during the homogenization process---normally zero current. Therefore, there exists a need to improve this method, making it more general and environment independent. The goal of proposed general homogenization technique is to create a function that is able to correctly predict the appropriate correction factor with only homogeneous information available, i.e. a function based on heterogeneous solution that could approximate PDFs using homogeneous solution. It has been shown that the PDF can be well approximated by least-square polynomial fit of non-dimensional heterogeneous solution and later used for PDF prediction using homogeneous solution. This shows a promise for PDF prediction for off-reference conditions, such as during reactor transients which provide conditions that can not typically be anticipated a priori.

Experimental characterization of intrapulse tissue conductivity changes for electroporation.

PubMed

Neal, Robert E; Garcia, Paulo A; Robertson, John L; Davalos, Rafael V

2011-01-01

Cells exposed to short electric pulses experience a change in their transmembrane potential, which can lead to increased membrane permeability of the cell. When the energy of the pulses surpasses a threshold, the cell dies in a non-thermal manner known as irreversible electroporation (IRE). IRE has shown promise in the focal ablation of pathologic tissues. Its non-thermal mechanism spares sensitive structures and facilitates rapid lesion resolution. IRE effects depend on the electric field distribution, which can be predicted with numerical modeling. When the cells become permeabilized, the bulk tissue properties change, affecting this distribution. For IRE to become a reliable and successful treatment of diseased tissues, robust predictive treatment planning methods must be developed. It is vital to understand the changes in tissue properties undergoing the electric pulses to improve numerical models and predict treatment volumes. We report on the experimental characterization of these changes for kidney tissue. Tissue samples were pulsed between plate electrodes while intrapulse voltage and current data were measured to determine the conductivity of the tissue during the pulse. Conductivity was then established as a function of the electric field to which the tissue is exposed. This conductivity curve was used in a numerical model to demonstrate the impact of accounting for these changes when modeling electric field distributions to develop treatment plans.

Analytical relationships for prediction of the mechanical properties of additively manufactured porous biomaterials.

PubMed

Zadpoor, Amir Abbas; Hedayati, Reza

2016-12-01

Recent developments in additive manufacturing techniques have motivated an increasing number of researchers to study regular porous biomaterials that are based on repeating unit cells. The physical and mechanical properties of such porous biomaterials have therefore received increasing attention during recent years. One of the areas that have revived is analytical study of the mechanical behavior of regular porous biomaterials with the aim of deriving analytical relationships that could predict the relative density and mechanical properties of porous biomaterials, given the design and dimensions of their repeating unit cells. In this article, we review the analytical relationships that have been presented in the literature for predicting the relative density, elastic modulus, Poisson's ratio, yield stress, and buckling limit of regular porous structures based on various types of unit cells. The reviewed analytical relationships are used to compare the mechanical properties of porous biomaterials based on different types of unit cells. The major areas where the analytical relationships have improved during the recent years are discussed and suggestions are made for future research directions. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 3164-3174, 2016. © 2016 The Authors Journal of Biomedical Materials Research Part A Published by Wiley Periodicals, Inc.

Biomarkers Predict Prognosis of Esophageal Cancer Patients | Center for Cancer Research

Cancer.gov

New treatment strategies are needed to improve outcomes for patients with esophageal cancer. With five-year survival rates less than 25 percent, this is one of the deadliest forms of cancer. There are two main types of esophageal cancer—squamous cell carcinoma and adenocarcinoma. Esophageal adenocarcinoma is frequently preceded by Barrett’s esophagus, a chronic inflammatory condition caused by gastroesophageal reflux. It is known that communication between tumor cells and the immune system can alter the behavior of tumor cells, and chronic inflammation has been implicated in several types of human cancers, including cancer of the esophagus.

Physics of ultra-high bioproductivity in algal photobioreactors

NASA Astrophysics Data System (ADS)

Greenwald, Efrat; Gordon, Jeffrey M.; Zarmi, Yair

2012-04-01

Cultivating algae at high densities in thin photobioreactors engenders time scales for random cell motion that approach photosynthetic rate-limiting time scales. This synchronization allows bioproductivity above that achieved with conventional strategies. We show that a diffusion model for cell motion (1) accounts for high bioproductivity at irradiance values previously deemed restricted by photoinhibition, (2) predicts the existence of optimal culture densities and their dependence on irradiance, consistent with available data, (3) accounts for the observed degree to which mixing improves bioproductivity, and (4) provides an estimate of effective cell diffusion coefficients, in accord with independent hydrodynamic estimates.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

PubMed Central

Jespersen, Martin Closter; Peters, Bjoern

2017-01-01

Abstract Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. PMID:28472356

Mechanisms of allergen immunotherapy for inhaled allergens and predictive biomarkers.

PubMed

Shamji, Mohamed H; Durham, Stephen R

2017-12-01

Allergen immunotherapy is effective in patients with IgE-dependent allergic rhinitis and asthma. When immunotherapy is given continuously for 3 years, there is persistent clinical benefit for several years after its discontinuation. This disease-modifying effect is both antigen-specific and antigen-driven. Clinical improvement is accompanied by decreases in numbers of effector cells in target organs, including mast cells, basophils, eosinophils, and type 2 innate lymphoid cells. Immunotherapy results in the production of blocking IgG/IgG 4 antibodies that can inhibit IgE-dependent activation mediated through both high-affinity IgE receptors (FcεRI) on mast cells and basophils and low-affinity IgE receptors (FcεRII) on B cells. Suppression of T H 2 immunity can occur as a consequence of either deletion or anergy of antigen-specific T cells; induction of antigen-specific regulatory T cells; or immune deviation in favor of T H 1 responses. It is not clear whether the altered long-term memory resides within the T-cell or the B-cell compartment. Recent data highlight the role of IL-10-producing regulatory B cells and "protective" antibodies that likely contribute to long-term tolerance. Understanding mechanisms underlying induction and persistence of tolerance should identify predictive biomarkers of clinical response and discover novel and more effective strategies for immunotherapy. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Kinetic models in industrial biotechnology - Improving cell factory performance.

PubMed

Almquist, Joachim; Cvijovic, Marija; Hatzimanikatis, Vassily; Nielsen, Jens; Jirstrand, Mats

2014-07-01

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

PubMed

Sperschneider, Jana; Gardiner, Donald M; Dodds, Peter N; Tini, Francesco; Covarelli, Lorenzo; Singh, Karam B; Manners, John M; Taylor, Jennifer M

2016-04-01

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au. © 2015 CSIRO New Phytologist © 2015 New Phytologist Trust.

Predicting the Dynamics of Protein Abundance

PubMed Central

Mehdi, Ahmed M.; Patrick, Ralph; Bailey, Timothy L.; Bodén, Mikael

2014-01-01

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA–protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/. PMID:24532840

Predicting the dynamics of protein abundance.

PubMed

Mehdi, Ahmed M; Patrick, Ralph; Bailey, Timothy L; Bodén, Mikael

2014-05-01

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

Analytical solution for haze values of aluminium-induced texture (AIT) glass superstrates for a-Si:H solar cells.

PubMed

Sahraei, Nasim; Forberich, Karen; Venkataraj, Selvaraj; Aberle, Armin G; Peters, Marius

2014-01-13

Light scattering at randomly textured interfaces is essential to improve the absorption of thin-film silicon solar cells. Aluminium-induced texture (AIT) glass provides suitable scattering for amorphous silicon (a-Si:H) solar cells. The scattering properties of textured surfaces are usually characterised by two properties: the angularly resolved intensity distribution and the haze. However, we find that the commonly used haze equations cannot accurately describe the experimentally observed spectral dependence of the haze of AIT glass. This is particularly the case for surface morphologies with a large rms roughness and small lateral feature sizes. In this paper we present an improved method for haze calculation, based on the power spectral density (PSD) function of the randomly textured surface. To better reproduce the measured haze characteristics, we suggest two improvements: i) inclusion of the average lateral feature size of the textured surface into the haze calculation, and ii) considering the opening angle of the haze measurement. We show that with these two improvements an accurate prediction of the haze of AIT glass is possible. Furthermore, we use the new equation to define optimum morphology parameters for AIT glass to be used for a-Si:H solar cell applications. The autocorrelation length is identified as the critical parameter. For the investigated a-Si:H solar cells, the optimum autocorrelation length is shown to be 320 nm.

Improving Soldier Recovery from Catastrophic Bone Injuries: Developing an Animal Model for Standardizing the Bone Reparative Potential of Emerging Progenitor Cell Therapies

DTIC Science & Technology

2011-08-01

phase hydrogel or nanofiber mesh structures as a weight bearing scaffold for skeletal repair. This biocompatibility of this design will be first... delivery . If BMP is delivered into a site that is being filled with SMAA+ progenitors, it may hault the further inward migration by inducing...differentiation of the front wave of progenitor cells. This would predict that a shell of bone would be observed surrounding the delivery vehicle, but

Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapeutics.

PubMed

Strug, Lisa J; Gonska, Tanja; He, Gengming; Keenan, Katherine; Ip, Wan; Boëlle, Pierre-Yves; Lin, Fan; Panjwani, Naim; Gong, Jiafen; Li, Weili; Soave, David; Xiao, Bowei; Tullis, Elizabeth; Rabin, Harvey; Parkins, Michael D; Price, April; Zuberbuhler, Peter C; Corvol, Harriet; Ratjen, Felix; Sun, Lei; Bear, Christine E; Rommens, Johanna M

2016-10-15

Cystic fibrosis is realizing the promise of personalized medicine. Recent advances in drug development that target the causal CFTR directly result in lung function improvement, but variability in response is demanding better prediction of outcomes to improve management decisions. The genetic modifier SLC26A9 contributes to disease severity in the CF pancreas and intestine at birth and here we assess its relationship with disease severity and therapeutic response in the airways. SLC26A9 association with lung disease was assessed in individuals from the Canadian and French CF Gene Modifier consortia with CFTR-gating mutations and in those homozygous for the common Phe508del mutation. Variability in response to a CFTR-directed therapy attributed to SLC26A9 genotype was assessed in Canadian patients with gating mutations. A primary airway model system determined if SLC26A9 shows modification of Phe508del CFTR function upon treatment with a CFTR corrector. In those with gating mutations that retain cell surface-localized CFTR we show that SLC26A9 modifies lung function while this is not the case in individuals homozygous for Phe508del where cell surface expression is lacking. Treatment response to ivacaftor, which aims to improve CFTR-channel opening probability in patients with gating mutations, shows substantial variability in response, 28% of which can be explained by rs7512462 in SLC26A9 (P = 0.0006). When homozygous Phe508del primary bronchial cells are treated to restore surface CFTR, SLC26A9 likewise modifies treatment response (P = 0.02). Our findings indicate that SLC26A9 airway modification requires CFTR at the cell surface, and that a common variant in SLC26A9 may predict response to CFTR-directed therapeutics.

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

PubMed

Racle, Julien; de Jonge, Kaat; Baumgaertner, Petra; Speiser, Daniel E; Gfeller, David

2017-11-13

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma

PubMed Central

Lee, Tsair-Fwu; Liou, Ming-Hsiang; Huang, Yu-Jie; Chao, Pei-Ju; Ting, Hui-Min; Lee, Hsiao-Yi

2014-01-01

To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP) models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84 patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The numbers of predictive factors for a multivariable logistic regression model were determined using the least absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models were achieved by LASSO with the smallest number of factors while preserving predictive value with higher AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical and socio-economic factors being selected, namely age, financial status, T stage, and education for different models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved by using multivariable logistic regression models with LASSO technique. The predictive model developed in HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa. PMID:25163814

Improvement in Saccharification Yield of Mixed Rumen Enzymes by Identification of Recalcitrant Cell Wall Constituents Using Enzyme Fingerprinting.

PubMed

Badhan, Ajay; Wang, Yu-Xi; Gruninger, Robert; Patton, Donald; Powlowski, Justin; Tsang, Adrian; McAllister, Tim A

2015-01-01

Identification of recalcitrant factors that limit digestion of forages and the development of enzymatic approaches that improve hydrolysis could play a key role in improving the efficiency of meat and milk production in ruminants. Enzyme fingerprinting of barley silage fed to heifers and total tract indigestible fibre residue (TIFR) collected from feces was used to identify cell wall components resistant to total tract digestion. Enzyme fingerprinting results identified acetyl xylan esterases as key to the enhanced ruminal digestion. FTIR analysis also suggested cross-link cell wall polymers as principal components of indigested fiber residues in feces. Based on structural information from enzymatic fingerprinting and FTIR, enzyme pretreatment to enhance glucose yield from barley straw and alfalfa hay upon exposure to mixed rumen-enzymes was developed. Prehydrolysis effects of recombinant fungal fibrolytic hydrolases were analyzed using microassay in combination with statistical experimental design. Recombinant hemicellulases and auxiliary enzymes initiated degradation of plant structural polysaccharides upon application and improved the in vitro saccharification of alfalfa and barley straw by mixed rumen enzymes. The validation results showed that microassay in combination with statistical experimental design can be successfully used to predict effective enzyme pretreatments that can enhance plant cell wall digestion by mixed rumen enzymes.

Microtubule dynamics in cell division: exploring living cells with polarized light microscopy.

PubMed

Inoué, Shinya

2008-01-01

This Perspective is an account of my early experience while I studied the dynamic organization and behavior of the mitotic spindle and its submicroscopic filaments using polarized light microscopy. The birefringence of spindle filaments in normally dividing plant and animal cells, and those treated by various agents, revealed (a) the reality of spindle fibers and fibrils in healthy living cells; (b) the labile, dynamic nature of the molecular filaments making up the spindle fibers; (c) the mode of fibrogenesis and action of orienting centers; and (d) force-generating properties based on the disassembly and assembly of the fibrils. These studies, which were carried out directly on living cells using improved polarizing microscopes, in fact predicted the reversible assembly properties of microtubules.

Analytical validation of quantitative immunohistochemical assays of tumor infiltrating lymphocyte biomarkers.

PubMed

Singh, U; Cui, Y; Dimaano, N; Mehta, S; Pruitt, S K; Yearley, J; Laterza, O F; Juco, J W; Dogdas, B

2018-06-04

Tumor infiltrating lymphocytes (TIL), especially T-cells, have both prognostic and therapeutic applications. The presence of CD8+ effector T-cells and the ratio of CD8+ cells to FOXP3+ regulatory T-cells have been used as biomarkers of disease prognosis to predict response to various immunotherapies. Blocking the interaction between inhibitory receptors on T-cells and their ligands with therapeutic antibodies including atezolizumab, nivolumab, pembrolizumab and tremelimumab increases the immune response against cancer cells and has shown significant improvement in clinical benefits and survival in several different tumor types. The improved clinical outcome is presumed to be associated with a higher tumor infiltration; therefore, it is thought that more accurate methods for measuring the amount of TIL could assist prognosis and predict treatment response. We have developed and validated quantitative immunohistochemistry (IHC) assays for CD3, CD8 and FOXP3 for immunophenotyping T-lymphocytes in tumor tissue. Various types of formalin fixed, paraffin embedded (FFPE) tumor tissues were immunolabeled with anti-CD3, anti-CD8 and anti-FOXP3 antibodies using an IHC autostainer. The tumor area of stained tissues, including the invasive margin of the tumor, was scored by a pathologist (visual scoring) and by computer-based quantitative image analysis. Two image analysis scores were obtained for the staining of each biomarker: the percent positive cells in the tumor area and positive cells/mm 2 tumor area. Comparison of visual vs. image analysis scoring methods using regression analysis showed high correlation and indicated that quantitative image analysis can be used to score the number of positive cells in IHC stained slides. To demonstrate that the IHC assays produce consistent results in normal daily testing, we evaluated the specificity, sensitivity and reproducibility of the IHC assays using both visual and image analysis scoring methods. We found that CD3, CD8 and FOXP3 IHC assays met the fit-for-purpose analytical acceptance validation criteria and that they can be used to support clinical studies.

MicroRNA-9 functions as a tumor suppressor and enhances radio-sensitivity in radio-resistant A549 cells by targeting neuropilin 1.

PubMed

Xiong, Kai; Shao, Li Hong; Zhang, Hai Qin; Jin, Linlin; Wei, Wei; Dong, Zhuo; Zhu, Yue Quan; Wu, Ning; Jin, Shun Zi; Xue, Li Xiang

2018-03-01

Radiotherapy is commonly used to treat lung cancer but may not kill all cancer cells, which may be attributed to the radiotherapy resistance that often occurs in non-small cell lung cancer (NSCLC). At present, the molecular mechanism of radio-resistance remains unclear. Neuropilin 1 (NRP1), a co-receptor for vascular endothelial growth factor (VEGF), was demonstrated to be associated with radio-resistance of NSCLC cells via the VEGF-phosphoinositide 3-kinase-nuclear factor-κB pathway in our previous study. It was hypothesized that certain microRNAs (miRs) may serve crucial functions in radio-sensitivity by regulating NRP1. Bioinformatics predicted that NRP1 was a potential target of miR-9, and this was validated by luciferase reporter assays. Functionally, miR-9-transfected A549 cells exhibited a decreased proliferation rate, increased apoptosis rate and attenuated migratory and invasive abilities. Additionally, a high expression of miR-9 also significantly enhanced the radio-sensitivity of A549 cells in vitro and in vivo . These data improve understanding of the mechanisms of cell radio-resistance, and suggest that miR-9 may be a molecular target for the prediction of radio-sensitivity in NSCLC.

Validation of World Health Organisation HIV/AIDS clinical staging in predicting initiation of antiretroviral therapy and clinical predictors of low CD4 cell count in Uganda.

PubMed

Baveewo, Steven; Ssali, Francis; Karamagi, Charles; Kalyango, Joan N; Hahn, Judith A; Ekoru, Kenneth; Mugyenyi, Peter; Katabira, Elly

2011-05-12

The WHO clinical guidelines for HIV/AIDS are widely used in resource limited settings to represent the gold standard of CD4 counts for antiviral therapy initiation. The utility of the WHO-defined stage 1 and 2 clinical factors used in WHO HIV/AIDS clinical staging in predicting low CD4 cell count has not been established in Uganda. Although the WHO staging has shown low sensitivity for predicting CD4<200 cells/mm(3), it has not been evaluated at for CD4 cut-offs of <250 cells/mm(3) or <350 cells/mm(3). To validate the World Health Organisation HIV/AIDS clinical staging in predicting initiation of antiretroviral therapy in a low-resource setting and to determine the clinical predictors of low CD4 cell count in Uganda. Data was collected on 395 participants from the Joint Clinical Research Centre, of whom 242 (61.3%) were classified as in stages 1 and 2 and 262 (68%) were females. Participants had a mean age of 36.8 years (SD 8.5). We found a significant inverse correlation between the CD4 lymphocyte count and WHO clinical stages. The sensitivity the WHO clinical staging at CD4 cell count of 250 cells/mm(3) and 350 cells/mm(3) was 53.5% and 49.1% respectively. Angular cheilitis, papular pruritic eruptions and recurrent upper respiratory tract infections were found to be significant predictors of low CD4 cell count among participants in WHO stage 1 and 2. The WHO HIV/AIDS clinical staging guidelines have a low sensitivity and about half of the participants in stages 1 and 2 would be eligible for ART initiation if they had been tested for CD4 count. Angular cheilitis and papular pruritic eruptions and recurrent upper respiratory tract infections may be used, in addition to the WHO staging, to improve sensitivity in the interim, as access to CD4 machines increases in Uganda.

Biocompatible polymer coating of titania nanotube arrays for improved drug elution and osteoblast adhesion.

PubMed

Gulati, Karan; Ramakrishnan, Saminathan; Aw, Moom Sinn; Atkins, Gerald J; Findlay, David M; Losic, Dusan

2012-01-01

Bacterial infection, extensive inflammation and poor osseointegration have been identified as the major reasons for [early] orthopaedic implant failures based on titanium. Creating implants with drug-eluting properties to locally deliver drugs is an appealing way to address some of these problems. To improve properties of titanium for orthopaedic applications, this study explored the modification of titanium surfaces with titaniananotube (TNT) arrays, and approach that combines drug delivery into bone and potentially improved bone integration. A titania layer with an array of nanotube structures (∼120 nm in diameter and 50 μm in length) was synthesized on titanium surfaces by electrochemical anodization and loaded with the water-insoluble anti-inflammatory drug indomethacin. A simple dip-coating process of polymer modification formed thin biocompatible polymer films over the drug-loaded TNTs to create TNTs with predictable drug release characteristics. Two biodegradable and antibacterial polymers, chitosan and poly(lactic-co-glycolic acid), were tested for their ability to extend the drug release time of TNTs and produce favourable bone cell adhesion properties. Dependent on polymer thickness, a significant improvement in the drug release characteristics was demonstrated, with reduced burst release (from 77% to >20%) and extended overall release from 4 days to more than 30 days. Excellent osteoblast adhesion and cell proliferation on polymer-coated TNTs compared with uncoated TNTs were also observed. These results suggest that polymer-modified implants with a TNT layer are capable of delivering a drug to a bone site over an extended period and with predictable kinetics. In addition, favourable bone cell adhesion suggests that such an implant would have good biocompatibility. The described approach is broadly applicable to a wide range of drugs and implants currently used in orthopaedic practice. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris.

PubMed

Saitua, Francisco; Torres, Paulina; Pérez-Correa, José Ricardo; Agosin, Eduardo

2017-02-21

Pichia pastoris shows physiological advantages in producing recombinant proteins, compared to other commonly used cell factories. This yeast is mostly grown in dynamic cultivation systems, where the cell's environment is continuously changing and many variables influence process productivity. In this context, a model capable of explaining and predicting cell behavior for the rational design of bioprocesses is highly desirable. Currently, there are five genome-scale metabolic reconstructions of P. pastoris which have been used to predict extracellular cell behavior in stationary conditions. In this work, we assembled a dynamic genome-scale metabolic model for glucose-limited, aerobic cultivations of Pichia pastoris. Starting from an initial model structure for batch and fed-batch cultures, we performed pre/post regression diagnostics to ensure that model parameters were identifiable, significant and sensitive. Once identified, the non-relevant ones were iteratively fixed until a priori robust modeling structures were found for each type of cultivation. Next, the robustness of these reduced structures was confirmed by calibrating the model with new datasets, where no sensitivity, identifiability or significance problems appeared in their parameters. Afterwards, the model was validated for the prediction of batch and fed-batch dynamics in the studied conditions. Lastly, the model was employed as a case study to analyze the metabolic flux distribution of a fed-batch culture and to unravel genetic and process engineering strategies to improve the production of recombinant Human Serum Albumin (HSA). Simulation of single knock-outs indicated that deviation of carbon towards cysteine and tryptophan formation improves HSA production. The deletion of methylene tetrahydrofolate dehydrogenase could increase the HSA volumetric productivity by 630%. Moreover, given specific bioprocess limitations and strain characteristics, the model suggests that implementation of a decreasing specific growth rate during the feed phase of a fed-batch culture results in a 25% increase of the volumetric productivity of the protein. In this work, we formulated a dynamic genome scale metabolic model of Pichia pastoris that yields realistic metabolic flux distributions throughout dynamic cultivations. The model can be calibrated with experimental data to rationally propose genetic and process engineering strategies to improve the performance of a P. pastoris strain of interest.

Immunophenotyping does not improve predictivity of the local lymph node assay in mice.

PubMed

Strauss, Volker; Kolle, Susanne N; Honarvar, Naveed; Dammann, Martina; Groeters, Sibylle; Faulhammer, Frank; Landsiedel, Robert; van Ravenzwaay, Bennard

2015-04-01

The local lymph node assay (LLNA) is a regulatory accepted test for the identification of skin sensitizing substances by measuring radioactive thymidine incorporation into the lymph node. However, there is evidence that LLNA is overestimating the sensitization potential of certain substance classes in particular those exerting skin irritation. Some reports describe the additional use of flow cytometry-based immunophenotyping to better discriminate irritants from sensitizing irritants in LLNA. In the present study, the 22 performance standards plus 8 surfactants were assessed using the radioactive LLNA method. In addition, lymph node cells were immunophenotyped to evaluate the specificity of the lymph node response using cell surface markers such as B220 or CD19, CD3, CD4, CD8, I-A(κ) and CD69 with the aim to allow a better discrimination above all between irritants and sensitizers, but also non-irritating sensitizers and non-sensitizers. However, the markers assessed in this study do not sufficiently differentiate between irritants and irritant sensitizers and therefore did not improve the predictive capacity of the LLNA. Copyright © 2014 John Wiley & Sons, Ltd.

Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma.

PubMed

Imbs, Diane-Charlotte; El Cheikh, Raouf; Boyer, Arnaud; Ciccolini, Joseph; Mascaux, Céline; Lacarelle, Bruno; Barlesi, Fabrice; Barbolosi, Dominique; Benzekry, Sébastien

2018-01-01

Concomitant administration of bevacizumab and pemetrexed-cisplatin is a common treatment for advanced nonsquamous non-small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC-bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

WE-H-BRA-07: Mechanistic Modelling of the Relative Biological Effectiveness of Heavy Charged Particles

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

McMahon, S; Queen’s University, Belfast, Belfast; McNamara, A

2016-06-15

Purpose Uncertainty in the Relative Biological Effectiveness (RBE) of heavy charged particles compared to photons remains one of the major uncertainties in particle therapy. As RBEs depend strongly on clinical variables such as tissue type, dose, and radiation quality, more accurate individualised models are needed to fully optimise treatments. MethodsWe have developed a model of DNA damage and repair following X-ray irradiation in a number of settings, incorporating mechanistic descriptions of DNA repair pathways, geometric effects on DNA repair, cell cycle effects and cell death. Our model has previously been shown to accurately predict a range of biological endpoints includingmore » chromosome aberrations, mutations, and cell death. This model was combined with nanodosimetric models of individual ion tracks to calculate the additional probability of lethal damage forming within a single track. These lethal damage probabilities can be used to predict survival and RBE for cells irradiated with ions of different Linear Energy Transfer (LET). ResultsBy combining the X-ray response model with nanodosimetry information, predictions of RBE can be made without cell-line specific fitting. The model’s RBE predictions were found to agree well with empirical proton RBE models (Mean absolute difference between models of 1.9% and 1.8% for cells with α/β ratios of 9 and 1.4, respectively, for LETs between 0 and 15 keV/µm). The model also accurately recovers the impact of high-LET carbon ion exposures, showing both the reduced efficacy of ions at extremely high LET, as well as the impact of defects in non-homologous end joining on RBE values in Chinese Hamster Ovary cells.ConclusionOur model is predicts RBE without the inclusion of empirical LET fitting parameters for a range of experimental conditions. This approach has the potential to deliver improved personalisation of particle therapy, with future developments allowing for the calculation of individualised RBEs. SJM is supported by a Marie Curie International Outgoing Fellowship from the European Commission’s FP7 program (EC FP7 MC-IOF-623630)« less

Upgrading HepG2 cells with adenoviral vectors that encode drug-metabolizing enzymes: application for drug hepatotoxicity testing.

PubMed

Gómez-Lechón, M José; Tolosa, Laia; Donato, M Teresa

2017-02-01

Drug attrition rates due to hepatotoxicity are an important safety issue considered in drug development. The HepG2 hepatoma cell line is currently being used for drug-induced hepatotoxicity evaluations, but its expression of drug-metabolizing enzymes is poor compared with hepatocytes. Different approaches have been proposed to upgrade HepG2 cells for more reliable drug-induced liver injury predictions. Areas covered: We describe the advantages and limitations of HepG2 cells transduced with adenoviral vectors that encode drug-metabolizing enzymes for safety risk assessments of bioactivable compounds. Adenoviral transduction facilitates efficient and controlled delivery of multiple drug-metabolizing activities to HepG2 cells at comparable levels to primary human hepatocytes by generating an 'artificial hepatocyte'. Furthermore, adenoviral transduction enables the design of tailored cells expressing particular metabolic capacities. Expert opinion: Upgraded HepG2 cells that recreate known inter-individual variations in hepatic CYP and conjugating activities due to both genetic (e.g., polymorphisms) or environmental (e.g., induction, inhibition) factors seems a suitable model to identify bioactivable drug and conduct hepatotoxicity risk assessments. This strategy should enable the generation of customized cells by reproducing human pheno- and genotypic CYP variability to represent a valuable human hepatic cell model to develop new safer drugs and to improve existing predictive toxicity assays.

Modeling the human invader in the United States

USGS Publications Warehouse

Stohlgren, Thomas J.; Jarnevich, Catherine S.; Giri, Chandra P.

2010-01-01

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km2) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km2> cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km2 > cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km2> cell is urban), while we predict that 24.5% of 1-km2> cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

Low Expression of Mucin-4 Predicts Poor Prognosis in Patients With Clear-Cell Renal Cell Carcinoma

PubMed Central

Fu, Hangcheng; Liu, Yidong; Xu, Le; Chang, Yuan; Zhou, Lin; Zhang, Weijuan; Yang, Yuanfeng; Xu, Jiejie

2016-01-01

Abstract Mucin-4 (MUC4), a member of membrane-bound mucins, has been reported to exert a large variety of distinctive roles in tumorigenesis of different cancers. MUC4 is aberrantly expressed in clear-cell renal cell carcinoma (ccRCC) but its prognostic value is still unveiled. This study aims to assess the clinical significance of MUC4 expression in patients with ccRCC. The expression of MUC4 was assessed by immunohistochemistry in 198 patients with ccRCC who underwent nephrectomy retrospectively in 2003 and 2004. Sixty-seven patients died before the last follow-up in the cohort. Kaplan–Meier method with log-rank test was applied to compare survival curves. Univariate and multivariate Cox regression models were applied to evaluate the prognostic value of MUC4 expression in overall survival (OS). The predictive nomogram was constructed based on the independent prognostic factors. The calibration was built to evaluate the predictive accuracy of nomogram. In patients with ccRCC, MUC4 expression, which was determined to be an independent prognostic indicator for OS (hazard ratio [HR] 3.891; P < 0.001), was negatively associated with tumor size (P = 0.036), Fuhrman grade (P = 0.044), and OS (P < 0.001). The prognostic accuracy of TNM stage, UCLA Integrated Scoring System (UISS), and Mayo clinic stage, size, grade, and necrosis score (SSIGN) prognostic models was improved when MUC4 expression was added. The independent prognostic factors, pT stage, distant metastases, Fuhrman grade, sarcomatoid, and MUC4 expression were integrated to establish a predictive nomogram with high predictive accuracy. MUC4 expression is an independent prognostic factor for OS in patients with ccRCC. PMID:27124015

Changes in beta cell function during the proximate post-diagnosis period in persons with type 1 diabetes.

PubMed

DiMeglio, Linda A; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Slover, Robert; Aye, Tandy; Weinzimer, Stuart A; Bremer, Andrew A; Buckingham, Bruce

2016-06-01

Prior studies examining beta-cell preservation in type 1 diabetes have predominantly assessed stimulated C-peptide concentrations approximately 10 wk after diagnosis. We examined whether earlier assessments might aid in prediction of beta cell function over time. Using data from a multi-center randomized trial assessing the effect of intensive diabetes management initiated within 1 wk of diagnosis, we assessed which clinical factors predicted 90-min mixed-meal tolerance test (MMTT) stimulated C-peptide values obtained 2 and 6 wk after diagnosis. We also studied associations of these factors with C-peptide values at 1- and 2-year post-diagnosis. Data from intervention and control groups were pooled. Among 67 study participants (mean age 13.3 ± 5.7 yr, range 7.8-45.7 yr) in multivariable analyses, C-peptide increased from baseline to 2 wks and then 6 wk. C-peptide levels at these times were significantly correlated with 1- and 2-yr C-peptide concentrations (all p < 0.001), with the strongest observed associations between 6-wk C-peptide and the 1- and 2-yr values (r = 0.66 and r = 0.61, respectively). In multivariable analyses, greater baseline and 6-wk C-peptide, and older age independently predicted greater 1- and 2-yr C-peptide concentrations. C-peptide assessments close to diagnosis were predictive of subsequent C-peptide production. Our data demonstrate a clear increase in C-peptide over the initial 6 wk after diabetes diagnosis followed by a plateau. Our data do not suggest that MMTT assessments performed closer to diagnosis than 6 wk would improve prediction of subsequent residual beta cell function. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Mechanical properties of triaxially braided composites: Experimental and analytical results

NASA Technical Reports Server (NTRS)

Masters, John E.; Foye, Raymond L.; Pastore, Christopher M.; Gowayed, Yasser A.

1992-01-01

This paper investigates the unnotched tensile properties of two-dimensional triaxial braid reinforced composites from both an experimental and analytical viewpoint. The materials are graphite fibers in an epoxy matrix. Three different reinforcing fiber architectures were considered. Specimens were cut from resin transfer molded (RTM) composite panels made from each braid. There were considerable differences in the observed elastic constants from different size strain gage and extensometer readings. Larger strain gages gave more consistent results and correlated better with the extensometer readings. Experimental strains correlated reasonably well with analytical predictions in the longitudinal, zero degree, fiber direction but not in the transverse direction. Tensile strength results were not always predictable even in reinforcing directions. Minor changes in braid geometry led to disproportionate strength variations. The unit cell structure of the triaxial braid was discussed with the assistence of computer analysis of the microgeometry. Photomicrographs of the braid geometry were used to improve upon the computer graphics representations of unit cells. These unit cells were used to predict the elastic moduli with various degrees of sophistication. The simple and the complex analyses were generally in agreement but none adequately matched the experimental results for all the braids.

Mechanical properties of triaxially braided composites: Experimental and analytical results

NASA Technical Reports Server (NTRS)

Masters, John E.; Foye, Raymond L.; Pastore, Christopher M.; Gowayed, Yasser A.

1992-01-01

The unnotched tensile properties of 2-D triaxial braid reinforced composites from both an experimental and an analytical viewpoint are studied. The materials are graphite fibers in an epoxy matrix. Three different reinforcing fiber architectures were considered. Specimens were cut from resin transfer molded (RTM) composite panels made from each braid. There were considerable differences in the observed elastic constants from different size strain gage and extensometer reading. Larger strain gages gave more consistent results and correlated better with the extensometer reading. Experimental strains correlated reasonably well with analytical predictions in the longitudinal, 0 degrees, fiber direction but not in the transverse direction. Tensile strength results were not always predictable even in reinforcing directions. Minor changes in braid geometry led to disproportionate strength variations. The unit cell structure of the triaxial braid was discussed with the assistance of computer analysis of the microgeometry. Photomicrographs of braid geometry were used to improve upon the computer graphics representations of unit cells. These unit cells were used to predict the elastic moduli with various degrees of sophistication. The simple and the complex analyses were generally in agreement but none adequately matched the experimental results for all the braids.

Gankyrin is a predictive and oncogenic factor in well-differentiated and dedifferentiated liposarcoma.

PubMed

Hwang, Ju-Ae; Yang, Heung-Mo; Hong, Doo-Pyo; Joo, Sung-Yeon; Choi, Yoon-La; Park, Joo-Hung; Lazar, Alexander J; Pollock, Raphael E; Lev, Dina; Kim, Sung Joo

2014-10-15

Liposarcoma is one of the most common histologic types of soft tissue sarcoma and is frequently an aggressive cancer with poor outcome. Hence, alternative approaches other than surgical excision are necessary to improve treatment of well-differentiated/dedifferentiated liposarcoma (WDLPS/DDLPS). For this reason, we performed a two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry/mass spectrometry (MALDI-TOF/MS) analysis to identify new factors for WDLPS and DDLPS. Among the selected candidate proteins, gankyrin, known to be an oncoprotein, showed a significantly high level of expression pattern and inversely low expression of p53/p21 in WDLPS and DDLPS tissues, suggesting possible utility as a new predictive factor. Moreover, inhibition of gankyrin not only led to reduction of in vitro cell growth ability including cell proliferation, colony-formation, and migration, but also in vivo DDLPS cell tumorigenesis, perhaps via downregulation of the p53 tumor suppressor gene and its p21 target and also reduction of AKT/mTOR signal activation. This study identifies gankyrin, for the first time, as new potential predictive and oncogenic factor of WDLPS and DDLPS, suggesting the potential for service as a future LPS therapeutic approach.

Discrete particle model for cement infiltration within open-cell structures: Prevention of osteoporotic fracture.

PubMed

Ramos-Infante, Samuel Jesús; Ten-Esteve, Amadeo; Alberich-Bayarri, Angel; Pérez, María Angeles

2018-01-01

This paper proposes a discrete particle model based on the random-walk theory for simulating cement infiltration within open-cell structures to prevent osteoporotic proximal femur fractures. Model parameters consider the cement viscosity (high and low) and the desired direction of injection (vertical and diagonal). In vitro and in silico characterizations of augmented open-cell structures validated the computational model and quantified the improved mechanical properties (Young's modulus) of the augmented specimens. The cement injection pattern was successfully predicted in all the simulated cases. All the augmented specimens exhibited enhanced mechanical properties computationally and experimentally (maximum improvements of 237.95 ± 12.91% and 246.85 ± 35.57%, respectively). The open-cell structures with high porosity fraction showed a considerable increase in mechanical properties. Cement augmentation in low porosity fraction specimens resulted in a lesser increase in mechanical properties. The results suggest that the proposed discrete particle model is adequate for use as a femoroplasty planning framework.

A community effort to assess and improve drug sensitivity prediction algorithms

PubMed Central

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2015-01-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods. PMID:24880487

A community effort to assess and improve drug sensitivity prediction algorithms.

PubMed

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2014-12-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

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

Kostadinova, Radina; Boess, Franziska; Applegate, Dawn

2013-04-01

Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitromore » three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity observed in vivo. ► 3D liver co-cultures can detect species-specific drug toxicity observed in vivo. ► This in vitro model may improve assessment of human relevance of preclinical findings.« less

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

PubMed

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xiaohong; Molinelli, Evan J; Babur, Özgün; Bemis, Debra L; Onur Sumer, Selcuk; Solit, David B; Pratilas, Christine A; Sander, Chris

2015-08-18

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.

A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

PubMed

Bagheri, Neda; Shiina, Marisa; Lauffenburger, Douglas A; Korn, W Michael

2011-02-01

Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

Preoperative Beta Cell Function Is Predictive of Diabetes Remission After Bariatric Surgery.

PubMed

Souteiro, Pedro; Belo, Sandra; Neves, João Sérgio; Magalhães, Daniela; Silva, Rita Bettencourt; Oliveira, Sofia Castro; Costa, Maria Manuel; Saavedra, Ana; Oliveira, Joana; Cunha, Filipe; Lau, Eva; Esteves, César; Freitas, Paula; Varela, Ana; Queirós, Joana; Carvalho, Davide

2017-02-01

Bariatric surgery can improve glucose metabolism in obese patients with diabetes, but the factors that can predict diabetes remission are still under discussion. The present study aims to examine the impact of preoperative beta cell function on diabetes remission following surgery. We investigated a cohort of 363 obese diabetic patients who underwent bariatric surgery. The impact of several preoperative beta cell function indexes on diabetes remission was explored through bivariate logistic regression models. Postoperative diabetes remission was achieved in 39.9 % of patients. Younger patients (p < 0.001) and those with lower HbA1c (p = 0.001) at the baseline evaluation had higher odds of diabetes remission. Use of oral anti-diabetics and insulin therapy did not reach statistical significance when they were adjusted for age and HbA1c. Among the evaluated indexes of beta cell function, higher values of insulinogenix index, Stumvoll first- and second-phase indexes, fasting C-peptide, C-peptide area under the curve (AUC), C-peptide/glucose AUC, ISR (insulin secretion rate) AUC, and ISR/glucose AUC predicted diabetes remission even after adjustment for age and HbA1c. Among them, C-peptide AUC had the higher discriminative power (AUC 0.76; p < 0.001). Patients' age and preoperative HbA1c can forecast diabetes remission following surgery. Unlike other studies, our group found that the use of oral anti-diabetics and insulin therapy were not independent predictors of postoperative diabetes status. Preoperative beta cell function, mainly C-peptide AUC, is useful in predicting diabetes remission, and it should be assessed in all obese diabetic patients before bariatric or metabolic surgery.

Fertility Preservation in Klinefelter Syndrome Patients during the Transition Period.

PubMed

Rives, Nathalie; Rives, Aurélie; Rondanino, Christine; Castanet, Mireille; Cuny, Ariane; Sibert, Louis

2018-01-01

Spermatozoa have occasionally been identified in ejaculate of adult Klinefelter syndrome (KS) patients but very exceptionally in KS adolescents. Spermatozoa can also be retrieved in testicular tissue of KS adolescents. The testis may also harbor spermatogonia and noncompletely differentiated germ cells. Neither clinical features nor hormonal parameters could predict germ cell recovery in KS adults or adolescents. No predictive factors can actually demonstrate that early diagnosis of KS would allow increasing the chance of sperm retrieval even if it has been suggested that semen quality may decline with age in KS patients. Leydig cell dysfunction may also be another factor that might affect the spermatogenesis process in XXY adolescents. Fertility preservation might be preferentially proposed in KS adolescents when semen sampling is possible, when the patient is able to consider alternative options to become a father, and to accept germ cell retrieval failure. However, precocious diagnosis of KS has also to be considered because it might not solely improve the possibility of fertility preservation after the onset of puberty, but also the medical care and the quality of life of these patients. © 2018 S. Karger AG, Basel.

An Improved Method for TAL Effectors DNA-Binding Sites Prediction Reveals Functional Convergence in TAL Repertoires of Xanthomonas oryzae Strains

PubMed Central

Pérez-Quintero, Alvaro L.; Rodriguez-R, Luis M.; Dereeper, Alexis; López, Camilo; Koebnik, Ralf; Szurek, Boris; Cunnac, Sebastien

2013-01-01

Transcription Activators-Like Effectors (TALEs) belong to a family of virulence proteins from the Xanthomonas genus of bacterial plant pathogens that are translocated into the plant cell. In the nucleus, TALEs act as transcription factors inducing the expression of susceptibility genes. A code for TALE-DNA binding specificity and high-resolution three-dimensional structures of TALE-DNA complexes were recently reported. Accurate prediction of TAL Effector Binding Elements (EBEs) is essential to elucidate the biological functions of the many sequenced TALEs as well as for robust design of artificial TALE DNA-binding domains in biotechnological applications. In this work a program with improved EBE prediction performances was developed using an updated specificity matrix and a position weight correction function to account for the matching pattern observed in a validation set of TALE-DNA interactions. To gain a systems perspective on the large TALE repertoires from X. oryzae strains, this program was used to predict rice gene targets for 99 sequenced family members. Integrating predictions and available expression data in a TALE-gene network revealed multiple candidate transcriptional targets for many TALEs as well as several possible instances of functional convergence among TALEs. PMID:23869221

Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.

CDK6 protects epithelial ovarian cancer from platinum-induced death via FOXO3 regulation.

PubMed

Dall'Acqua, Alessandra; Sonego, Maura; Pellizzari, Ilenia; Pellarin, Ilenia; Canzonieri, Vincenzo; D'Andrea, Sara; Benevol, Sara; Sorio, Roberto; Giorda, Giorgio; Califano, Daniela; Bagnoli, Marina; Militello, Loredana; Mezzanzanica, Delia; Chiappetta, Gennaro; Armenia, Joshua; Belletti, Barbara; Schiappacassi, Monica; Baldassarre, Gustavo

2017-10-01

Epithelial ovarian cancer (EOC) is an infrequent but highly lethal disease, almost invariably treated with platinum-based therapies. Improving the response to platinum represents a great challenge, since it could significantly impact on patient survival. Here, we report that silencing or pharmacological inhibition of CDK6 increases EOC cell sensitivity to platinum. We observed that, upon platinum treatment, CDK6 phosphorylated and stabilized the transcription factor FOXO3, eventually inducing ATR transcription. Blockage of this pathway resulted in EOC cell death, due to altered DNA damage response accompanied by increased apoptosis. These observations were recapitulated in EOC cell lines in vitro , in xenografts in vivo , and in primary tumor cells derived from platinum-treated patients. Consistently, high CDK6 and FOXO3 expression levels in primary EOC predict poor patient survival. Our data suggest that CDK6 represents an actionable target that can be exploited to improve platinum efficacy in EOC patients. As CDK4/6 inhibitors are successfully used in cancer patients, our findings can be immediately transferred to the clinic to improve the outcome of EOC patients. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

White butterflies as solar photovoltaic concentrators.

PubMed

Shanks, Katie; Senthilarasu, S; Ffrench-Constant, Richard H; Mallick, Tapas K

2015-07-31

Man's harvesting of photovoltaic energy requires the deployment of extensive arrays of solar panels. To improve both the gathering of thermal and photovoltaic energy from the sun we have examined the concept of biomimicry in white butterflies of the family Pieridae. We tested the hypothesis that the V-shaped posture of basking white butterflies mimics the V-trough concentrator which is designed to increase solar input to photovoltaic cells. These solar concentrators improve harvesting efficiency but are both heavy and bulky, severely limiting their deployment. Here, we show that the attachment of butterfly wings to a solar cell increases its output power by 42.3%, proving that the wings are indeed highly reflective. Importantly, and relative to current concentrators, the wings improve the power to weight ratio of the overall structure 17-fold, vastly expanding their potential application. Moreover, a single mono-layer of scale cells removed from the butterflies' wings maintained this high reflectivity showing that a single layer of scale cell-like structures can also form a useful coating. As predicted, the wings increased the temperature of the butterflies' thorax dramatically, showing that the V-shaped basking posture of white butterflies has indeed evolved to increase the temperature of their flight muscles prior to take-off.

White butterflies as solar photovoltaic concentrators

NASA Astrophysics Data System (ADS)

Shanks, Katie; Senthilarasu, S.; Ffrench-Constant, Richard H.; Mallick, Tapas K.

2015-07-01

Man’s harvesting of photovoltaic energy requires the deployment of extensive arrays of solar panels. To improve both the gathering of thermal and photovoltaic energy from the sun we have examined the concept of biomimicry in white butterflies of the family Pieridae. We tested the hypothesis that the V-shaped posture of basking white butterflies mimics the V-trough concentrator which is designed to increase solar input to photovoltaic cells. These solar concentrators improve harvesting efficiency but are both heavy and bulky, severely limiting their deployment. Here, we show that the attachment of butterfly wings to a solar cell increases its output power by 42.3%, proving that the wings are indeed highly reflective. Importantly, and relative to current concentrators, the wings improve the power to weight ratio of the overall structure 17-fold, vastly expanding their potential application. Moreover, a single mono-layer of scale cells removed from the butterflies’ wings maintained this high reflectivity showing that a single layer of scale cell-like structures can also form a useful coating. As predicted, the wings increased the temperature of the butterflies’ thorax dramatically, showing that the V-shaped basking posture of white butterflies has indeed evolved to increase the temperature of their flight muscles prior to take-off.

Incorporation of nanovoids into metallic gratings for broadband plasmonic organic solar cells.

PubMed

Lee, Sangjun; In, Sungjun; Mason, Daniel R; Park, Namkyoo

2013-02-25

We present investigation and optimization of a newly proposed plasmonic organic solar cell geometry based on the incorporation of nanovoids into conventional rectangular backplane gratings. Hybridization of strongly localized plasmonic modes of the nanovoids with Fabry-Perot cavity modes originating from surface plasmon reflection at the grating elements is shown to significantly boost performance in the long wavelength regime. This constitutes improved broadband operation while maintaining absorption enhancements at short wavelengths derived from conventional rectangular grating. Our calculations predict a figure of merit enhancement of up to 41% compared to when the nanovoid indented grating is absent. This is a significant improvement over the previously considered rectangular grating structures, which is further shown to be maintained over the entire angular range.

Transformational Solar Array Final Report

NASA Technical Reports Server (NTRS)

Gaddy, Edward; Ballarotto, Mihaela; Drabenstadt, Christian; Nichols, John; Douglas, Mark; Spence, Brian; Stall, Richard A.; Sulyma, Chris; Sharps, Paul

2017-01-01

We have made outstanding progress in the Base Phase towards achieving the final NASA Research Announcement (NRA) goals. Progress is better than anticipated due to the lighter than predicted mass of the IMM solar cells. We look forward to further improvements in the IMM cell performance during Option I and Option II; so, we have confidence that the first four items listed in the table will improve to better than the NRA goals. The computation of the end of life blanket efficiency is uncertain because we have extrapolated the radiation damage from room temperature measurements. The last three items listed in the Table were not intended to be accomplished during the Base Phase; they will be achieved during Option I and Option II.

Surface-engineered substrates for improved human pluripotent stem cell culture under fully defined conditions.

PubMed

Saha, Krishanu; Mei, Ying; Reisterer, Colin M; Pyzocha, Neena Kenton; Yang, Jing; Muffat, Julien; Davies, Martyn C; Alexander, Morgan R; Langer, Robert; Anderson, Daniel G; Jaenisch, Rudolf

2011-11-15

The current gold standard for the culture of human pluripotent stem cells requires the use of a feeder layer of cells. Here, we develop a spatially defined culture system based on UV/ozone radiation modification of typical cell culture plastics to define a favorable surface environment for human pluripotent stem cell culture. Chemical and geometrical optimization of the surfaces enables control of early cell aggregation from fully dissociated cells, as predicted from a numerical model of cell migration, and results in significant increases in cell growth of undifferentiated cells. These chemically defined xeno-free substrates generate more than three times the number of cells than feeder-containing substrates per surface area. Further, reprogramming and typical gene-targeting protocols can be readily performed on these engineered surfaces. These substrates provide an attractive cell culture platform for the production of clinically relevant factor-free reprogrammed cells from patient tissue samples and facilitate the definition of standardized scale-up friendly methods for disease modeling and cell therapeutic applications.

Positive predictive value estimates for cell-free noninvasive prenatal screening from data of a large referral genetic diagnostic laboratory.

PubMed

Petersen, Andrea K; Cheung, Sau Wai; Smith, Janice L; Bi, Weimin; Ward, Patricia A; Peacock, Sandra; Braxton, Alicia; Van Den Veyver, Ignatia B; Breman, Amy M

2017-12-01

Since its debut in 2011, cell-free fetal DNA screening has undergone rapid expansion with respect to both utilization and coverage. However, conclusive data regarding the clinical validity and utility of this screening tool, both for the originally included common autosomal and sex-chromosomal aneuploidies as well as the more recently added chromosomal microdeletion syndromes, have lagged behind. Thus, there is a continued need to educate clinicians and patients about the current benefits and limitations of this screening tool to inform pre- and posttest counseling, pre/perinatal decision making, and medical risk assessment/management. The objective of this study was to determine the positive predictive value and false-positive rates for different chromosomal abnormalities identified by cell-free fetal DNA screening using a large data set of diagnostic testing results on invasive samples submitted to the laboratory for confirmatory studies. We tested 712 patient samples sent to our laboratory to confirm a cell-free fetal DNA screening result, indicating high risk for a chromosome abnormality. We compiled data from all cases in which the indication for confirmatory testing was a positive cell-free fetal DNA screen, including the common trisomies, sex chromosomal aneuploidies, microdeletion syndromes, and other large genome-wide copy number abnormalities. Testing modalities included fluorescence in situ hybridization, G-banded karyotype, and/or chromosomal microarray analysis performed on chorionic villus samples, amniotic fluid, or postnatally obtained blood samples. Positive predictive values and false-positive rates were calculated from tabulated data. The positive predictive values for trisomy 13, 18, and 21 were consistent with previous reports at 45%, 76%, and 84%, respectively. For the microdeletion syndrome regions, positive predictive values ranged from 0% for detection of Cri-du-Chat syndrome and Prader-Willi/Angelman syndrome to 14% for 1p36 deletion syndrome and 21% for 22q11.2 deletion syndrome. Detection of sex chromosomal aneuploidies had positive predictive values of 26% for monosomy X, 50% for 47,XXX, and 86% for 47,XXY. The positive predictive values for detection of common autosomal and sex chromosomal aneuploidies by cell-free fetal DNA screening were comparable with other studies. Identification of microdeletions was associated with lower positive predictive values and higher false-positive rates, likely because of the low prevalence of the individual targeted microdeletion syndromes in the general population. Although the obtained positive predictive values compare favorably with those seen in traditional screening approaches for common aneuploidies, they highlight the importance of educating clinicians and patients on the limitations of cell-free fetal DNA screening tests. Improvement of the cell-free fetal DNA screening technology and continued monitoring of its performance after introduction into clinical practice will be important to fully establish its clinical utility. Nonetheless, our data provide valuable information that may aid result interpretation, patient counseling, and clinical decision making/management. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

PubMed Central

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma’ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource. PMID:28413689

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

PubMed Central

Racle, Julien; de Jonge, Kaat; Baumgaertner, Petra; Speiser, Daniel E

2017-01-01

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org). PMID:29130882

Demonstration of the feasibility of automated silicon solar cell fabrication

NASA Technical Reports Server (NTRS)

Taylor, W. E.; Schwartz, F. M.

1975-01-01

A study effort was undertaken to determine the process, steps and design requirements of an automated silicon solar cell production facility. Identification of the key process steps was made and a laboratory model was conceptually designed to demonstrate the feasibility of automating the silicon solar cell fabrication process. A detailed laboratory model was designed to demonstrate those functions most critical to the question of solar cell fabrication process automating feasibility. The study and conceptual design have established the technical feasibility of automating the solar cell manufacturing process to produce low cost solar cells with improved performance. Estimates predict an automated process throughput of 21,973 kilograms of silicon a year on a three shift 49-week basis, producing 4,747,000 hexagonal cells (38mm/side), a total of 3,373 kilowatts at an estimated manufacturing cost of $0.866 per cell or $1.22 per watt.

12-Chemokine Gene Signature Identifies Lymph Node-like Structures in Melanoma: Potential for Patient Selection for Immunotherapy?

NASA Astrophysics Data System (ADS)

Messina, Jane L.; Fenstermacher, David A.; Eschrich, Steven; Qu, Xiaotao; Berglund, Anders E.; Lloyd, Mark C.; Schell, Michael J.; Sondak, Vernon K.; Weber, Jeffrey S.; Mulé, James J.

2012-10-01

We have interrogated a 12-chemokine gene expression signature (GES) on genomic arrays of 14,492 distinct solid tumors and show broad distribution across different histologies. We hypothesized that this 12-chemokine GES might accurately predict a unique intratumoral immune reaction in stage IV (non-locoregional) melanoma metastases. The 12-chemokine GES predicted the presence of unique, lymph node-like structures, containing CD20+ B cell follicles with prominent areas of CD3+ T cells (both CD4+ and CD8+ subsets). CD86+, but not FoxP3+, cells were present within these unique structures as well. The direct correlation between the 12-chemokine GES score and the presence of unique, lymph nodal structures was also associated with better overall survival of the subset of melanoma patients. The use of this novel 12-chemokine GES may reveal basic information on in situ mechanisms of the anti-tumor immune response, potentially leading to improvements in the identification and selection of melanoma patients most suitable for immunotherapy.

A poroelastic model describing nutrient transport and cell stresses within a cyclically strained collagen hydrogel.

PubMed

Vaughan, Benjamin L; Galie, Peter A; Stegemann, Jan P; Grotberg, James B

2013-11-05

In the creation of engineered tissue constructs, the successful transport of nutrients and oxygen to the contained cells is a significant challenge. In highly porous scaffolds subject to cyclic strain, the mechanical deformations can induce substantial fluid pressure gradients, which affect the transport of solutes. In this article, we describe a poroelastic model to predict the solid and fluid mechanics of a highly porous hydrogel subject to cyclic strain. The model was validated by matching the predicted penetration of a bead into the hydrogel from the model with experimental observations and provides insight into nutrient transport. Additionally, the model provides estimates of the wall-shear stresses experienced by the cells embedded within the scaffold. These results provide insight into the mechanics of and convective nutrient transport within a cyclically strained hydrogel, which could lead to the improved design of engineered tissues. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the 'rule-of-two' model.

PubMed

Chen, Minjun; Tung, Chun-Wei; Shi, Qiang; Guo, Lei; Shi, Leming; Fang, Hong; Borlak, Jürgen; Tong, Weida

2014-07-01

Drug-induced liver injury (DILI) is a major cause of drug failures in both the preclinical and clinical phase. Consequently, improving prediction of DILI at an early stage of drug discovery will reduce the potential failures in the subsequent drug development program. In this regard, high-content screening (HCS) assays are considered as a promising strategy for the study of DILI; however, the predictive performance of HCS assays is frequently insufficient. In the present study, a new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model (i.e., 'rule-of-two' defined by daily dose ≥100 mg/day & logP ≥3). The RO2 model was derived from the observation that high daily doses and lipophilicity of an oral medication were associated with significant DILI risk in humans. In the developed testing strategy, the RO2 model was used for the rational selection of candidates for HCS assays, and only the negatives predicted by the RO2 model were further investigated by HCS. Subsequently, the effects of drug treatment on cell loss, nuclear size, DNA damage/fragmentation, apoptosis, lysosomal mass, mitochondrial membrane potential, and steatosis were studied in cultures of primary rat hepatocytes. Using a set of 70 drugs with clear evidence of clinically relevant DILI, the testing strategy improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone. Moreover, the testing strategy was further validated by including published data (Cosgrove et al. in Toxicol Appl Pharmacol 237:317-330, 2009) on drug-cytokine-induced hepatotoxicity, which improved the accuracies by 7 %. Taken collectively, the proposed testing strategy can significantly improve the prediction of in vitro assays for detecting DILI liability in an early drug discovery phase.

Firefighter willingness to participate in a stem cell clinical trial for burns: A mixed methods study.

PubMed

Horch, Jenny D; Carr, Eloise C J; Harasym, Patricia; Burnett, Lindsay; Biernaskie, Jeff; Gabriel, Vincent

2016-12-01

Adult stem cells represent a potentially renewable and autologous source of cells to regenerate skin and improve wound healing. Firefighters are at risk of sustaining a burn and potentially benefiting from a split thickness skin graft (STSG). This mixed methods study examined firefighter willingness to participate in a future stem cell clinical trial, outcome priorities and factors associated with this decision. A sequential explanatory mixed methods design was used. The quantitative phase (online questionnaire) was followed by the qualitative phase (semi-structured interviews). A sample of 149 firefighters completed the online survey, and a purposeful sample of 15 firefighters was interviewed. A majority (74%) reported they would participate in a future stem cell clinical trial if they experienced burn benefiting from STSG. Hypothetical concerns related to receiving a STSG were pain, itch, scarring/redness and skin durability. Participants indicated willingness to undergo stem cell therapy if the risk of no improvement was 43% or less. Risk tolerance was predicted by perceived social support and having children. Interviews revealed four main themes: a desire to help others, improving clinical outcomes, trusting relationships, and a belief in scientific investigation. Many participants admitted lacking sufficient knowledge to make an informed decision regarding stem cell therapies. Firefighters indicated they were largely willing to participate in a stem cell clinical trial but also indicated a lack of knowledge upon which to make a decision. Public education of the role of stem cells in STSG will be increasingly important as clinical trials are developed. Copyright © 2016 Elsevier Ltd and ISBI. All rights reserved.

Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation.

PubMed

Teschendorff, Andrew E; Jones, Allison; Fiegl, Heidi; Sargent, Alexandra; Zhuang, Joanna J; Kitchener, Henry C; Widschwendter, Martin

2012-03-27

Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.

Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

PubMed Central

2012-01-01

Background Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. Methods We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. Results We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). Conclusions We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. Trial registration The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821. PMID:22453031

Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility.

PubMed

Devenyi, Ryan A; Ortega, Francis A; Groenendaal, Willemijn; Krogh-Madsen, Trine; Christini, David J; Sobie, Eric A

2017-04-01

Arrhythmias result from disruptions to cardiac electrical activity, although the factors that control cellular action potentials are incompletely understood. We combined mathematical modelling with experiments in heart cells from guinea pigs to determine how cellular electrical activity is regulated. A mismatch between modelling predictions and the experimental results allowed us to construct an improved, more predictive mathematical model. The balance between two particular potassium currents dictates how heart cells respond to perturbations and their susceptibility to arrhythmias. Imbalances of ionic currents can destabilize the cardiac action potential and potentially trigger lethal cardiac arrhythmias. In the present study, we combined mathematical modelling with information-rich dynamic clamp experiments to determine the regulation of action potential morphology in guinea pig ventricular myocytes. Parameter sensitivity analysis was used to predict how changes in ionic currents alter action potential duration, and these were tested experimentally using dynamic clamp, a technique that allows for multiple perturbations to be tested in each cell. Surprisingly, we found that a leading mathematical model, developed with traditional approaches, systematically underestimated experimental responses to dynamic clamp perturbations. We then re-parameterized the model using a genetic algorithm, which allowed us to estimate ionic current levels in each of the cells studied. This unbiased model adjustment consistently predicted an increase in the rapid delayed rectifier K + current and a drastic decrease in the slow delayed rectifier K + current, and this prediction was validated experimentally. Subsequent simulations with the adjusted model generated the clinically relevant prediction that the slow delayed rectifier is better able to stabilize the action potential and suppress pro-arrhythmic events than the rapid delayed rectifier. In summary, iterative coupling of simulations and experiments enabled novel insight into how the balance between cardiac K + currents influences ventricular arrhythmia susceptibility. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Application of a coupled smoothed particle hydrodynamics (SPH) and coarse-grained (CG) numerical modelling approach to study three-dimensional (3-D) deformations of single cells of different food-plant materials during drying.

PubMed

Rathnayaka, C M; Karunasena, H C P; Senadeera, W; Gu, Y T

2018-03-14

Numerical modelling has gained popularity in many science and engineering streams due to the economic feasibility and advanced analytical features compared to conventional experimental and theoretical models. Food drying is one of the areas where numerical modelling is increasingly applied to improve drying process performance and product quality. This investigation applies a three dimensional (3-D) Smoothed Particle Hydrodynamics (SPH) and Coarse-Grained (CG) numerical approach to predict the morphological changes of different categories of food-plant cells such as apple, grape, potato and carrot during drying. To validate the model predictions, experimental findings from in-house experimental procedures (for apple) and sources of literature (for grape, potato and carrot) have been utilised. The subsequent comaprison indicate that the model predictions demonstrate a reasonable agreement with the experimental findings, both qualitatively and quantitatively. In this numerical model, a higher computational accuracy has been maintained by limiting the consistency error below 1% for all four cell types. The proposed meshfree-based approach is well-equipped to predict the morphological changes of plant cellular structure over a wide range of moisture contents (10% to 100% dry basis). Compared to the previous 2-D meshfree-based models developed for plant cell drying, the proposed model can draw more useful insights on the morphological behaviour due to the 3-D nature of the model. In addition, the proposed computational modelling approach has a high potential to be used as a comprehensive tool in many other tissue morphology related investigations.

Acid-mediated Lipinski's second rule: application to drug design and targeting in cancer.

PubMed

Omran, Ziad; Rauch, Cyril

2014-05-01

With a predicted 382.4 per 100,000 people expected to suffer from some form of malignant neoplasm by 2015, and a current death toll of 1 out of 8 deaths worldwide, improving treatment and/or drug design is an essential focus of cancer research. Multi-drug resistance is the leading cause of chemotherapeutic failure, and delivery of anticancer drugs to the inside of cancerous cells is another major challenge. Fifteen years ago, in a completely different field in which improving drug delivery is the objective, the bioavailability of oral compounds, Christopher Lipinski formulated some rules that are still used by the pharmaceutical industry as rules of thumb to improve drug delivery to their target. Although Lipinski's rules were not formulated to improve delivery of antineoplastic drugs to the inside of cancer cells, it is interesting to note that the problems are similar. On the basis of the strong similarity between the fields, we discuss how they can be connected and how new drug targets can be defined in cancer.

Adoptive T-cell Therapy Using Autologous Tumor-infiltrating Lymphocytes for Metastatic Melanoma: Current Status and Future Outlook

PubMed Central

Wu, Richard; Forget, Marie-Andree; Chacon, Jessica; Bernatchez, Chantale; Haymaker, Cara; Chen, Jie Qing; Hwu, Patrick; Radvanyi, Laszlo

2012-01-01

Immunotherapy using autologous T-cells has emerged to be a powerful treatment option for patients with metastatic melanoma. These include the adoptive transfer of autologous tumor-infiltrating lymphocytes (TIL), T-cells transduced with high-affinity T-cell receptors (TCR) against major melanosomal tumor antigens, and T cells transduced with chimeric antigen receptors (CAR) composed of hybrid immunoglobulin light chains with endo-domains of T-cell signaling molecules. Among these and other options for T-cell therapy, TIL together with high-dose IL-2 has had the longest clinical history with multiple clinical trials in centers across the world consistently demonstrating durable clinical response rates near 50% or more. A distinct advantage of TIL therapy making it still the T-cell therapy of choice is the broad nature of the T-cell recognition against both defined as well as un-defined tumors antigens against all possible MHC, rather than the single specificity and limited MHC coverage of the newer TCR and CAR transduction technologies. In the past decade, significant inroads have been made in defining the phenotypes of T cells in TIL mediating tumor regression. CD8+ T cells are emerging to be critical, although the exact subset of CD8+ T cells exhibiting the highest clinical activity in terms of memory and effector markers is still controversial. We present a model in which both effector-memory and more differentiated effector T cells ultimately may need to cooperate to mediate long-term tumor control in responding patients. Although TIL therapy has shown great potential to treat metastatic melanoma, a number of issues have emerged that need to be addressed to bring it more into the mainstream of melanoma care. First, we have a reached the point where a pivotal phase II or phase III trials are needed in an attempt to gain regulatory approval of TIL as standard-of-care. Second, improvements in how we expand TIL for therapy are needed, that minimize the time the T-cells are in culture and improve the memory and effector characteristics of the T cells for longer persistence and enhanced anti-tumor activity in vivo. Third, there is a critical need to identify surrogate and predictive biomarkers in order to better select suitable patients for TIL therapy in order to improve response rate and duration. Overall, the outlook for TIL therapy for melanoma is very bright. We predict that TIL will indeed emerge to become an approved treatment in the upcoming years through pivotal clinical trials. Moreover, new approaches combining TIL with targeted signaling pathway drugs, such as mutant B-RAF inhibitors, and synergistic immunomodulatory interventions enhancing T-cell costimulation and preventing negative regulation, should further increase therapeutic efficacy and durable complete response rates. PMID:22453018

How and why does the immunological synapse form? Physical chemistry meets cell biology.

PubMed

Chakraborty, Arup K

2002-03-05

During T lymphocyte (T cell) recognition of an antigen, a highly organized and specific pattern of membrane proteins forms in the junction between the T cell and the antigen-presenting cell (APC). This specialized cell-cell junction is called the immunological synapse. It is several micrometers large and forms over many minutes. A plethora of experiments are being performed to study the mechanisms that underlie synapse formation and the way in which information transfer occurs across the synapse. The wealth of experimental data that is beginning to emerge must be understood within a mechanistic framework if it is to prove useful in developing modalities to control the immune response. Quantitative models can complement experiments in the quest for such a mechanistic understanding by suggesting experimentally testable hypotheses. Here, a quantitative synapse assembly model is described. The model uses concepts developed in physical chemistry and cell biology and is able to predict the spatiotemporal evolution of cell shape and receptor protein patterns observed during synapse formation. Attention is directed to how the juxtaposition of model predictions and experimental data has led to intriguing hypotheses regarding the role of null and self peptides during synapse assembly, as well as correlations between T cell effector functions and the robustness of synapse assembly. We remark on some ways in which synergistic experiments and modeling studies can improve current models, and we take steps toward a better understanding of information transfer across the T cell-APC junction.

Cell-free microRNAs as diagnostic, prognostic, and predictive biomarkers for lung cancer.

PubMed

Zandberga, Elīna; Kozirovskis, Viktors; Ābols, Artūrs; Andrējeva, Diāna; Purkalne, Gunta; Linē, Aija

2013-04-01

Lung cancer is the most common cancer worldwide, accounting for over 1.37 million deaths annually. The clinical outcome and management of lung cancer patients could be substantially improved by the implementation of non-invasive biomarker assays for the early detection, prognosis as well as prediction and monitoring of treatment response. MicroRNAs (miRNAs) have been implicated in the regulation of virtually all signaling circuits within a cell and their dysregulation has been shown to play an essential role in the development and progression of cancer. Recently, miRNAs were found to be released into the circulation and to exist there in a remarkably stable form. Furthermore, various cancers were shown to leave specific miRNA fingerprints in the blood of patients suggesting that cell-free miRNAs could serve as non-invasive biomarkers for the detection or monitoring of cancer and putative therapeutic targets. Since that, a considerable effort has been devoted to decode the information carried by circulating miRNAs. In the current review, we give an insight into the mechanisms of miRNA release into the bloodstream, their putative functional significance and systematically review the studies focused on the identification of cell-free miRNAs with the diagnostic, prognostic, and predictive significance in lung cancer and discuss their potential clinical utility. Copyright © 2012 Wiley Periodicals, Inc.

Biomarkers Predict Prognosis of Esophageal Cancer Patients | Center for Cancer Research

Cancer.gov

New treatment strategies are needed to improve outcomes for patients with esophageal cancer. With five-year survival rates less than 25 percent, this is one of the deadliest forms of cancer. There are two main types of esophageal cancer—squamous cell carcinoma and adenocarcinoma. Esophageal adenocarcinoma is frequently preceded by Barrett’s esophagus, a chronic inflammatory

The Role of Oxysterols in a Computational Steroidogenesis Model of Human H295R Cells to Improve Predictability of Biochemical Responses to Endocrine Disruptors

EPA Science Inventory

Steroids, which have an important role in a wide range of physiological processes, are synthesized primarily in the gonads and adrenal glands through a series of enzyme mediated reactions. The activity of steroidogenic enzymes can be altered by a variety of endocrine disruptors (...

Evaluation of In Vitro Biotransformation Using HepaRG Cells to Improve High-Throughput Chemical Hazard Prediction: A Toxicogenomics Analysis (SOT)

EPA Science Inventory

The US EPA’s ToxCast program has generated a wealth of data in >600 in vitro assayson a library of 1060 environmentally relevant chemicals and failed pharmaceuticals to facilitate hazard identification. An inherent criticism of many in vitro-based strategies is the inability of a...

Targeting Metabolic Plasticity in Breast Cancer Cells via Mitochondrial Complex I Modulation

PubMed Central

Xu, Qijin; Biener-Ramanujan, Eva; Yang, Wei; Ramanujan, V Krishnan

2016-01-01

Purpose Heterogeneity commonly observed in clinical tumors stems both from the genetic diversity as well as from the differential metabolic adaptation of multiple cancer types during their struggle to maintain uncontrolled proliferation and invasion in vivo. This study aims to identify a potential metabolic window of such adaptation in aggressive human breast cancer cell lines. Methods With a multidisciplinary approach using high resolution imaging, cell metabolism assays, proteomic profiling and animal models of human tumor xenografts and via clinically-relevant, pharmacological approach for modulating mitochondrial complex I function in human breast cancer cell lines, we report a novel route to target metabolic plasticity in human breast cancer cells. Results By a systematic modulation of mitochondrial function and by mitigating metabolic switch phenotype in aggressive human breast cancer cells, we demonstrate that the resulting metabolic adaptation signatures can predictably decrease tumorigenic potential in vivo. Proteomic profiling of the metabolic adaptation in these cells further revealed novel protein-pathway interactograms highlighting the importance of antioxidant machinery in the observed metabolic adaptation. Conclusions Improved metabolic adaptation potential in aggressive human breast cancer cells contribute to improving mitochondrial function and reducing metabolic switch phenotype –which may be vital for targeting primary tumor growth in vivo. PMID:25677747

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

PubMed Central

Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius

2016-01-01

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations. PMID:26977807

Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification

PubMed Central

Liu, Yan; Li, Xiaohong; Johnson, Margaret; Smith, Collette; Kamarulzaman, Adeeba bte; Montaner, Julio; Mounzer, Karam; Saag, Michael; Cahn, Pedro; Cesar, Carina; Krolewiecki, Alejandro; Sanne, Ian; Montaner, Luis J.

2012-01-01

Background Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation. Methods and Findings Using a prospective cohort of HIV-infected patients (n = 1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD = 4.2%) in CD4 testing capacity. A capacity savings of 34% (SD = 3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n = 619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold. Conclusions Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings. Please see later in the article for the Editors' Summary PMID:22529752

Paramagnetic fluorinated nanoemulsions for sensitive cellular fluorine-19 magnetic resonance imaging

PubMed Central

Kislukhin, Alexander A.; Xu, Hongyan; Adams, Stephen R.; Narsinh, Kazim H.; Tsien, Roger Y.; Ahrens, Eric T.

2016-01-01

Fluorine-19 magnetic resonance imaging (19F MRI) probes enable quantitative in vivo detection of cell therapies and inflammatory cells. Here, we describe the formulation of perfluorocarbon-based nanoemulsions with improved sensitivity for cellular MRI. Reduction of the 19F spin-lattice relaxation time (T1) enables rapid imaging and an improved signal-to-noise ratio, thereby improving cell detection sensitivity. We synthesized metal-binding β-diketones conjugated to linear perfluoropolyether (PFPE), formulated these fluorinated ligands as aqueous nanoemulsions, and then metalated them with various transition and lanthanide ions in the fluorous phase. Iron(III) tris-β-diketonate ('FETRIS') nanoemulsions with PFPE have low cytotoxicity (<20%) and superior MRI properties. Moreover, the 19F T1 can readily be reduced by an order of magnitude and tuned by stoichiometric modulation of the iron concentration. The resulting 19F MRI detection sensitivity is enhanced by 3-to-5 fold over previously used tracers at 11.7 T, and is predicted to increase by at least 8-fold at clinical field strength of 3 T. PMID:26974409

Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis.

PubMed

Lee, Sang-Woo; Morishita, Yoshihiro

2017-07-01

Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated. With the progress in identification of related molecules, some reports described the relevance of cell mechanics during elimination. Furthermore, recent live imaging studies have shown that even in tissues composed of genetically identical cells, a non-negligible number of cells are eliminated during growth. Thus, mechanical cell elimination (MCE) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon. Here, we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis. First, we propose two quantitative measures, cell and tissue fitness, to evaluate cellular competitiveness and tissue growth efficiency, respectively. By mechanical tissue simulation in a pure population where all cells have the same mechanical traits, we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters. In particular, we found that geometrical (specifically, cell size) and mechanical (stress magnitude) heterogeneities are common determinants of the elimination rate. Based on these results, we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Moreover, when cells with different mechanical traits are mixed (e.g., in the presence of phenotypic variation), we show that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits, i.e. intra-tissue "evolution". Along with the improvement of growth efficiency, cell density, stress state, and phenotype (mechanical traits) were also shown to be homogenized through growth. More theoretically, we propose a mathematical model that approximates cell competition dynamics, by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model.

Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis

PubMed Central

2017-01-01

Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated. With the progress in identification of related molecules, some reports described the relevance of cell mechanics during elimination. Furthermore, recent live imaging studies have shown that even in tissues composed of genetically identical cells, a non-negligible number of cells are eliminated during growth. Thus, mechanical cell elimination (MCE) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon. Here, we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis. First, we propose two quantitative measures, cell and tissue fitness, to evaluate cellular competitiveness and tissue growth efficiency, respectively. By mechanical tissue simulation in a pure population where all cells have the same mechanical traits, we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters. In particular, we found that geometrical (specifically, cell size) and mechanical (stress magnitude) heterogeneities are common determinants of the elimination rate. Based on these results, we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Moreover, when cells with different mechanical traits are mixed (e.g., in the presence of phenotypic variation), we show that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits, i.e. intra-tissue “evolution”. Along with the improvement of growth efficiency, cell density, stress state, and phenotype (mechanical traits) were also shown to be homogenized through growth. More theoretically, we propose a mathematical model that approximates cell competition dynamics, by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model. PMID:28704373

The Role of microRNA Markers in the Diagnosis, Treatment, and Outcome Prediction of Spinal Cord Injury.

PubMed

Martirosyan, Nikolay L; Carotenuto, Alessandro; Patel, Arpan A; Kalani, M Yashar S; Yagmurlu, Kaan; Lemole, G Michael; Preul, Mark C; Theodore, Nicholas

2016-01-01

Spinal cord injury (SCI) is a devastating condition that affects many people worldwide. Treatment focuses on controlling secondary injury cascade and improving regeneration. It has recently been suggested that both the secondary injury cascade and the regenerative process are heavily regulated by microRNAs (miRNAs). The measurement of specific biomarkers could improve our understanding of the disease processes, and thereby provide clinicians with the opportunity to guide treatment and predict clinical outcomes after SCI. A variety of miRNAs exhibit important roles in processes of inflammation, cell death, and regeneration. These miRNAs can be used as diagnostic tools for predicting outcome after SCI. In addition, miRNAs can be used in the treatment of SCI and its symptoms. Significant laboratory and clinical evidence exist to show that miRNAs could be used as robust diagnostic and therapeutic tools for the treatment of patients with SCI. Further clinical studies are warranted to clarify the importance of each subtype of miRNA in SCI management.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes.

PubMed

Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo

2017-07-03

Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Hot-spot investigations of utility scale panel configurations

NASA Technical Reports Server (NTRS)

Arnett, J. C.; Dally, R. B.; Rumburg, J. P.

1984-01-01

The causes of array faults and efforts to mitigate their effects are examined. Research is concentrated on the panel for the 900 kw second phase of the Sacramento Municipal Utility District (SMUD) project. The panel is designed for hot spot tolerance without comprising efficiency under normal operating conditions. Series/paralleling internal to each module improves tolerance in the power quadrant to cell short or open circuits. Analtyical methods are developed for predicting worst case shade patterns and calculating the resultant cell temperature. Experiments conducted on a prototype panel support the analytical calculations.

Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers

PubMed Central

Kwon, Andrew T.; Chou, Alice Yi; Arenillas, David J.; Wasserman, Wyeth W.

2011-01-01

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions. PMID:22144875

Genomics and epigenomics of clear cell renal cell carcinoma: recent developments and potential applications.

PubMed

Rydzanicz, Małgorzata; Wrzesiński, Tomasz; Bluyssen, Hans A R; Wesoły, Joanna

2013-12-01

Majority of clear cell renal cell carcinomas (ccRCCs) are diagnosed in the advanced metastatic stage resulting in dramatic decrease of patient survival. Thereby, early detection and monitoring of the disease may improve prognosis and treatment results. Recent technological advances enable the identification of genetic events associated with ccRCC and reveal significant molecular heterogeneity of ccRCC tumors. This review summarizes recent findings in ccRCC genomics and epigenomics derived from chromosomal aberrations, DNA sequencing and methylation, mRNA, miRNA expression profiling experiments. We provide a molecular insight into ccRCC pathology and recapitulate possible clinical applications of genomic alterations as predictive and prognostic biomarkers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Receptor-Mediated Uptake and Intracellular Sorting of Multivalent Lipid Nanoparticles Against the Epidermal Growth Factor Receptor (EGFR) and the Human EGFR 2 (HER2)

NASA Astrophysics Data System (ADS)

Tran, David Tu

In the area of receptor-targeted lipid nanoparticles for drug delivery, efficiency has been mainly focused on cell-specificity, endocytosis, and subsequently effects on bioactivity such as cell growth inhibition. Aspects of targeted liposomal uptake and intracellular sorting are not well defined. This dissertation assessed a series of ligands as targeted functional groups against HER2 and EGFR for liposomal drug delivery. Receptor-mediated uptake, both mono-targeted and dual-targeted to multiple receptors of different ligand valence, and the intracellular sorting of lipid nanoparticles were investigated to improve the delivery of drugs to cancer cells. Lipid nanoparticles were functionalized through a new sequential micelle transfer---conjugation method, while the micelle transfer method was extended to growth factors. Through a combination of both techniques, anti-HER2 and anti-EGFR dual-targeted immunoliposomes with different combinations of ligand valence were developed for comparative studies. With the array of lipid nanoparticles, the uptake and cytotoxicity of lipid nanoparticles in relationship to ligand valence, both mono-targeting and dual-targeting, were evaluated on a small panel of breast cancer cell lines that express HER2 and EGFR of varying levels. Comparable uptake ratios of ligand to expressed receptor and apparent cooperativity were observed. For cell lines that express both receptors, additive dose-uptake effects were also observed with dual-targeted immunoliposomes, which translated to marginal improvements in cell growth inhibition with doxorubicin delivery. Colocalization analysis revealed that ligand-conjugated lipid nanoparticles settle to endosomal compartments similar to their attached ligands. Pathway transregulation and pathway saturation were also observed to affect trafficking. In the end, liposomes routed to the recycling endosomes were never observed to traffic beyond the endosomes nor to be exocytose like recycled ligands. Based on the experimental data, models were developed to help interpret and predict the binding and trafficking of lipid nanoparticles. The crosslink multivalent binding model of lipid nanoparticles to monovalent receptors was able to predict ligand valence for optimum binding, cell association concentrations, offer explanations to the antagonistic effects observed from high ligand valence, and predict the binding limitations of both ligand valence and ligand affinity. Hopefully, the models will serve as valuable tools for future optimizations in targeted liposomal drug delivery.

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

PubMed

Ahadi, Alireza; Sablok, Gaurav; Hutvagner, Gyorgy

2017-04-07

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Large splenic volume may be a useful predictor for partial splenic embolization-induced liver functional improvement in cirrhotic patients.

PubMed

Hayashi, Hiromitsu; Beppu, Toru; Masuda, Toshiro; Okabe, Hirohisa; Imai, Katsunori; Hashimoto, Daisuke; Ikuta, Yoshiaki; Chikamoto, Akira; Watanabe, Masayuki; Baba, Hideo

2014-01-01

Partial splenic embolization (PSE) for cirrhotic patients has been reported not only to achieve an improvement in thrombocytopenia and portal hypertension, but also to induce PSE-associated fringe benefit such as individual liver functional improvement. The purpose of this study was to clarify the predictive marker of liver functional improvement due from PSE in cirrhotic patients. From April 1999 to January 2009, 83 cirrhotic patients with hypersplenism-induced thrombocytopenia (platelet count <10 × 10(4)/μl) underwent PSE. Of them, 71 patients with follow-up for more than one year after PSE were retrospectively investigated. In liver tissues after PSE, proliferating cell nuclear antigen (PCNA)-positive hepatocytes were remarkably increased, speculating that PSE induced liver regenerative response. Indeed, serum albumin and cholinesterase levels increased to 104 ± 14% and 130 ± 65% each of the pretreatment level at one year after PSE. In a multiple linear regression analysis, preoperative splenic volume was extracted as the predictive factor for the improvement in cholinesterase level after PSE. Cirrhotic patients with preoperative splenic volume >600 ml obtained significantly higher serum albumin and cholinesterase levels at one year after PSE compared to those with less than 600 ml (P-values were 0.029 in both). A large preoperative splenic volume was the useful predictive marker for an effective PSE-induced liver functional improvement. © 2013 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Understanding and Improving the Elastic Compressive Modulus of Fibre Reinforced Soy-Based Polyurethane Foams

NASA Astrophysics Data System (ADS)

Hussain, Sadakat

Soy-based polyurethane foams (PUFs) were reinforced with fibres of different aspect ratios to improve the compressive modulus. Each of the three fibre types reinforced PUF differently. Shorter micro-crystalline cellulose fibres were found embedded inside the cell struts of PUF and reinforced them. The reinforcement was attributed to be stress transfer from the matrix to the fibre by comparing the experimental results to those predicted by micro-mechanical models for short fibre reinforced composites. The reinforced cell struts increased the overall compressive modulus of the foam. Longer glass fibres (470 microns, length) provided the best reinforcement. These fibres were found to be larger than the cell diameters. The micro-mechanical models could not predict the reinforcement provided by the longer glass fibres. The models predicted negligible reinforcement because the very low modulus PUF should not transfer load to the higher modulus fibres. However, using a finite element model, it was determined that the fibres were providing reinforcement through direct fibre interaction with each other. Intermediate length glass fibres (260 microns, length) were found to poorly reinforce the PUF and should be avoided. These fibres were too short to interact with each other and were on average too large to embed and reinforce cell struts. In order to produce natural fibre reinforced PUFs in the future, a novel device was invented. The purpose of the device is to deliver natural fibres at a constant mass flow rate. The device was found to consistently meter individual loose natural fibre tufts at a mass flow rate of 2 grams per second. However, the device is not robust and requires further development to deliver a fine stream of natural fibre that can mix and interact with the curing polymeric components of PUF. A design plan was proposed to address the remaining issues with the device.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

PubMed

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013

Data-Driven prioritisation of antibody-drug conjugate targets in head and neck squamous cell carcinoma.

PubMed

Hanemaaijer, Saskia H; van Gijn, Stephanie E; Oosting, Sjoukje F; Plaat, Boudewijn E C; Moek, Kirsten L; Schuuring, Ed M; van der Laan, Bernard F A M; Roodenburg, Jan L N; van Vugt, Marcel A T M; van der Vegt, Bert; Fehrmann, Rudolf S N

2018-05-01

For patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) palliative treatment options that improve overall survival are limited. The prognosis in this group remains poor and there is an unmet need for new therapeutic options. An emerging class of therapeutics, targeting tumor-specific antigens, are antibodies bound to a cytotoxic agent, known as antibody-drug conjugates (ADCs). The aim of this study was to prioritize ADC targets in HNSCC. With a systematic search, we identified 55 different ADC targets currently targeted by registered ADCs and ADCs under clinical evaluation. For these 55 ADC targets, protein overexpression was predicted in a dataset containing 344 HNSCC mRNA expression profiles by using a method called functional genomic mRNA profiling. The ADC target with the highest predicted overexpression was validated by performing immunohistochemistry (IHC) on an independent tissue microarray containing 414 HNSCC tumors. The predicted top 5 overexpressed ADC targets in HNSCC were: glycoprotein nmb (GPNMB), SLIT and NTRK-like family member 6, epidermal growth factor receptor, CD74 and CD44. IHC validation showed combined cytoplasmic and membranous GPNMB protein expression in 92.0% of the cases. Strong expression was seen in 65.9% of the cases. In addition, 86.5% and 67.7% of cases showed ≥5% and >25% GPNMB positive tumor cells, respectively. This study provides a data-driven prioritization of ADCs targets that will facilitate clinicians and drug developers in deciding which ADC should be taken for further clinical evaluation in HNSCC. This might help to improve disease outcome of HNSCC patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

Endothelial Progenitor Cell Levels Predict Future Physical Function: An Exploratory Analysis From the VA Enhanced Fitness Study.

PubMed

Povsic, Thomas J; Sloane, Richard; Pieper, Carl F; Pearson, Megan P; Peterson, Eric D; Cohen, Harvey J; Morey, Miriam C

2016-03-01

Levels of circulating progenitor cells (CPCs) are depleted with aging and chronic injury and are associated with level of physical functioning; however, little is known about the correlation of CPCs with longer-term measures of physical capabilities. We sought to determine the association of CPCs with future levels of physical function and with changes in physical function over time. CPCs were measured in 117 participants with impaired glucose tolerance in the Enhanced Fitness clinical trial based on the cell surface markers CD34 and CD133 and aldehyde dehydrogenase (ALDH) activity at baseline, 3 months, and 12 months. Physical function was assessed using usual and rapid gait speed, 6-minute walk distance, chair stand time, and SF-36 physical functioning score and reassessed at 3 and 12 months after clinical intervention. Higher baseline levels of CD133(+), CD34(+), CD133(+)CD34(+), and ALDH(br) were each highly predictive of faster gait speed and longer distance walked in 6 minutes at both 3 and 12 months. These associations remained robust after adjustment for age, body mass index, baseline covariates, and inflammation and were independent of interventions to improve physical fitness. Further, higher CPC levels predicted greater improvements in usual and rapid gait speed over 1 year. Baseline CPC levels are associated not only with baseline mobility but also with future physical function, including changes in gait speed. These findings suggest that CPC measurement may be useful as a marker of both current and future physiologic aging and functional decline. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The predictive value of 53BP1 and BRCA1 mRNA expression in advanced non-small-cell lung cancer patients treated with first-line platinum-based chemotherapy

PubMed Central

Bonanno, Laura; Costa, Carlota; Majem, Margarita; Sanchez, Jose Javier; Gimenez-Capitan, Ana; Rodriguez, Ignacio; Vergenegre, Alain; Massuti, Bartomeu; Favaretto, Adolfo; Rugge, Massimo; Pallares, Cinta; Taron, Miquel; Rosell, Rafael

2013-01-01

Platinum-based chemotherapy is the standard first-line treatment for non-oncogene-addicted non-small cell lung cancers (NSCLCs) and the analysis of multiple DNA repair genes could improve current models for predicting chemosensitivity. We investigated the potential predictive role of components of the 53BP1 pathway in conjunction with BRCA1. The mRNA expression of BRCA1, MDC1, CASPASE3, UBC13, RNF8, 53BP1, PIAS4, UBC9 and MMSET was analyzed by real-time PCR in 115 advanced NSCLC patients treated with first-line platinum-based chemotherapy. Patients expressing low levels of both BRCA1 and 53BP1 obtained a median progression-free survival of 10.3 months and overall survival of 19.3 months, while among those with low BRCA1 and high 53BP1 progression-free survival was 5.9 months (P <0.0001) and overall survival was 8.2 months (P=0.001). The expression of 53BP1 refines BRCA1-based predictive modeling to identify patients most likely to benefit from platinum-based chemotherapy. PMID:24197907

Diagnosis and predictive molecular analysis of non-small-cell lung cancer in the Africa-Middle East region: challenges and strategies for improvement.

PubMed

Slavik, Tomas; Asselah, Fatima; Fakhruddin, Najla; El Khodary, Ahmed; Torjman, Fairouz; Anis, Elia; Quinn, Martin; Khankan, Azzam; Kerr, Keith M

2014-11-01

The identification of tumor biomarkers provides information on the prognosis and guides the implementation of appropriate treatment in patients with many different cancer types. In non-small cell lung cancer (NSCLC), targeted treatment plans based on biomarker identification have already been used in the clinic. However, such predictive molecular testing is not currently a universally used practice. This is the case, in particular, in developing countries where lung cancer is increasingly prevalent. In September 2012 and November 2013, a committee of 16 lung cancer experts from Africa and the Middle East met to discuss key issues related to diagnosis and biomarker testing in NSCLC and the implementation of personalized medicine in the region. The committee identified current challenges for effective diagnosis and predictive analysis in Africa and the Middle East. Moreover, strategies to encourage the implementation of biomarker testing were discussed. A practical approach for the effective diagnosis and predictive molecular testing of NSCLC in these regions was derived. We present the key issues and recommendations arising from the meetings. Copyright © 2014 Elsevier Inc. All rights reserved.

Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics.

PubMed

Sarkar, Aurijit; Anderson, Kelcey C; Kellogg, Glen E

2012-06-01

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes

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

Strigun, Alexander; Wahrheit, Judith; Beckers, Simone

Along with hepatotoxicity, cardiotoxic side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for cardiotoxicity are insufficient. High content screening comprising of not only electrophysiological characterization but also cellular molecular alterations are expected to improve the cardiotoxicity prediction potential. Metabolomic approaches recently have become an important focus of research in pharmacological testing and prediction. In this study, the culture medium supernatants from HL-1 cardiomyocytes after exposure to drugs from different classes (analgesics, antimetabolites, anthracyclines, antihistamines, channel blockers) were analyzed to determine specific metabolic footprints in response to the tested drugs. Since most drugsmore » influence energy metabolism in cardiac cells, the metabolite 'sub-profile' consisting of glucose, lactate, pyruvate and amino acids was considered. These metabolites were quantified using HPLC in samples after exposure of cells to test compounds of the respective drug groups. The studied drug concentrations were selected from concentration response curves for each drug. The metabolite profiles were randomly split into training/validation and test set; and then analysed using multivariate statistics (principal component analysis and discriminant analysis). Discriminant analysis resulted in clustering of drugs according to their modes of action. After cross validation and cross model validation, the underlying training data were able to predict 50%-80% of conditions to the correct classification group. We show that HPLC based characterisation of known cell culture medium components is sufficient to predict a drug's potential classification according to its mode of action.« less

In Vivo Validation of Predicted and Conserved T Cell Epitopes in a Swine Influenza Model

PubMed Central

Gutiérrez, Andres H.; Loving, Crystal; Moise, Leonard; Terry, Frances E.; Brockmeier, Susan L.; Hughes, Holly R.; Martin, William D.; De Groot, Anne S.

2016-01-01

Swine influenza is a highly contagious respiratory viral infection in pigs that is responsible for significant financial losses to pig farmers annually. Current measures to protect herds from infection include: inactivated whole-virus vaccines, subunit vaccines, and alpha replicon-based vaccines. As is true for influenza vaccines for humans, these strategies do not provide broad protection against the diverse strains of influenza A virus (IAV) currently circulating in U.S. swine. Improved approaches to developing swine influenza vaccines are needed. Here, we used immunoinformatics tools to identify class I and II T cell epitopes highly conserved in seven representative strains of IAV in U.S. swine and predicted to bind to Swine Leukocyte Antigen (SLA) alleles prevalent in commercial swine. Epitope-specific interferon-gamma (IFNγ) recall responses to pooled peptides and whole virus were detected in pigs immunized with multi-epitope plasmid DNA vaccines encoding strings of class I and II putative epitopes. In a retrospective analysis of the IFNγ responses to individual peptides compared to predictions specific to the SLA alleles of cohort pigs, we evaluated the predictive performance of PigMatrix and demonstrated its ability to distinguish non-immunogenic from immunogenic peptides and to identify promiscuous class II epitopes. Overall, this study confirms the capacity of PigMatrix to predict immunogenic T cell epitopes and demonstrate its potential for use in the design of epitope-driven vaccines for swine. Additional studies that match the SLA haplotype of animals with the study epitopes will be required to evaluate the degree of immune protection conferred by epitope-driven DNA vaccines in pigs. PMID:27411061

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

NASA Astrophysics Data System (ADS)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

Cell wall proteome of sugarcane stems: comparison of a destructive and a non-destructive extraction method showed differences in glycoside hydrolases and peroxidases.

PubMed

Calderan-Rodrigues, Maria Juliana; Jamet, Elisabeth; Douché, Thibaut; Bonassi, Maria Beatriz Rodrigues; Cataldi, Thaís Regiani; Fonseca, Juliana Guimarães; San Clemente, Hélène; Pont-Lezica, Rafael; Labate, Carlos Alberto

2016-01-11

Sugarcane has been used as the main crop for ethanol production for more than 40 years in Brazil. Recently, the production of bioethanol from bagasse and straw, also called second generation (2G) ethanol, became a reality with the first commercial plants started in the USA and Brazil. However, the industrial processes still need to be improved to generate a low cost fuel. One possibility is the remodeling of cell walls, by means of genetic improvement or transgenesis, in order to make the bagasse more accessible to hydrolytic enzymes. We aimed at characterizing the cell wall proteome of young sugarcane culms, to identify proteins involved in cell wall biogenesis. Proteins were extracted from the cell walls of 2-month-old culms using two protocols, non-destructive by vacuum infiltration vs destructive. The proteins were identified by mass spectrometry and bioinformatics. A predicted signal peptide was found in 84 different proteins, called cell wall proteins (CWPs). As expected, the non-destructive method showed a lower percentage of proteins predicted to be intracellular than the destructive one (33% vs 44%). About 19% of CWPs were identified with both methods, whilst the infiltration protocol could lead to the identification of 75% more CWPs. In both cases, the most populated protein functional classes were those of proteins related to lipid metabolism and oxido-reductases. Curiously, a single glycoside hydrolase (GH) was identified using the non-destructive method whereas 10 GHs were found with the destructive one. Quantitative data analysis allowed the identification of the most abundant proteins. The results highlighted the importance of using different protocols to extract proteins from cell walls to expand the coverage of the cell wall proteome. Ten GHs were indicated as possible targets for further studies in order to obtain cell walls less recalcitrant to deconstruction. Therefore, this work contributed to two goals: enlarge the coverage of the sugarcane cell wall proteome, and provide target proteins that could be used in future research to facilitate 2G ethanol production.

Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors.

PubMed

Marini, C; Fossa, F; Paoli, C; Bellingeri, M; Gnone, G; Vassallo, P

2015-03-01

Habitat modeling is an important tool to investigate the quality of the habitat for a species within a certain area, to predict species distribution and to understand the ecological processes behind it. Many species have been investigated by means of habitat modeling techniques mainly to address effective management and protection policies and cetaceans play an important role in this context. The bottlenose dolphin (Tursiops truncatus) has been investigated with habitat modeling techniques since 1997. The objectives of this work were to predict the distribution of bottlenose dolphin in a coastal area through the use of static morphological features and to compare the prediction performances of three different modeling techniques: Generalized Linear Model (GLM), Generalized Additive Model (GAM) and Random Forest (RF). Four static variables were tested: depth, bottom slope, distance from 100 m bathymetric contour and distance from coast. RF revealed itself both the most accurate and the most precise modeling technique with very high distribution probabilities predicted in presence cells (90.4% of mean predicted probabilities) and with 66.7% of presence cells with a predicted probability comprised between 90% and 100%. The bottlenose distribution obtained with RF allowed the identification of specific areas with particularly high presence probability along the coastal zone; the recognition of these core areas may be the starting point to develop effective management practices to improve T. truncatus protection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Physiologically based pharmacokinetic modelling of methotrexate and 6-mercaptopurine in adults and children. Part 2: 6-mercaptopurine and its interaction with methotrexate.

PubMed

Ogungbenro, Kayode; Aarons, Leon

2014-04-01

6-mercaptopurine (6-MP) is a purine antimetabolite and prodrug that undergoes extensive intracellular metabolism to produce thionucleotides, active metabolites which have cytotoxic and immunosuppressive properties. Combination therapies involving 6-MP and methotrexate have shown remarkable results in the cure of childhood acute lymphoblastic leukaemia (ALL) in the last 30 years. 6-MP undergoes very extensive intestinal and hepatic metabolism following oral dosing due to the activity of xanthine oxidase leading to very low and highly variable bioavailability and methotrexate has been demonstrated as an inhibitor of xanthine oxidase. Despite the success recorded in the use of 6-MP in ALL, there is still lack of effect and life threatening toxicity in some patients due to variability in the pharmacokinetics of 6-MP. Also, dose adjustment during treatment is still based on toxicity. The aim of the current work was to develop a mechanistic model that can be used to simulate trial outcomes and help to improve dose individualisation and dosage regimen optimisation. A physiological based pharmacokinetic model was proposed for 6-MP, this model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle, rest of body and red blood cells. The model was based on the assumption of the same elimination pathways in adults and children. Parameters of the model include physiological parameters and drug-specific parameter which were obtained from the literature or estimated using plasma and red blood cell concentration data. Age-dependent changes in parameters were implemented for scaling and variability was also introduced on the parameters for prediction. Inhibition of 6-MP first-pass effect by methotrexate was implemented to predict observed clinical interaction between the two drugs. The model was developed successfully and plasma and red blood cell concentrations were adequately predicted both in terms of mean prediction and variability. The predicted interaction between 6-MP and methotrexate was slightly lower than the reported clinical interaction between the two drugs. The model can be used to predict plasma and tissue concentration in adults and children following oral and intravenous dosing and may ultimately help to improve treatment outcome in childhood ALL patients.

A rational approach to improving productivity in recombinant Pichia pastoris fermentation.

PubMed

d'Anjou, M C; Daugulis, A J

2001-01-05

A Mut(S) Pichia pastoris strain that had been genetically modified to produce and secrete sea raven antifreeze protein was used as a model system to demonstrate the implementation of a rational, model-based approach to improve process productivity. A set of glycerol/methanol mixed-feed continuous stirred-tank reactor (CSTR) experiments was performed at the 5-L scale to characterize the relationship between the specific growth rate and the cell yield on methanol, the specific methanol consumption rate, the specific recombinant protein formation rate, and the productivity based on secreted protein levels. The range of dilution rates studied was 0. 01 to 0.10 h(-1), and the residual methanol concentration was kept constant at approximately 2 g/L (below the inhibitory level). With the assumption that the cell yield on glycerol was constant, the cell yield on methanol increased from approximately 0.5 to 1.5 over the range studied. A maximum specific methanol consumption rate of 20 mg/g. h was achieved at a dilution rate of 0.06 h(-1). The specific product formation rate and the volumetric productivity based on product continued to increase over the range of dilution rates studied, and the maximum values were 0.06 mg/g. h and 1.7 mg/L. h, respectively. Therefore, no evidence of repression by glycerol was observed over this range, and operating at the highest dilution rate studied maximized productivity. Fed-batch mass balance equations, based on Monod-type kinetics and parameters derived from data collected during the CSTR work, were then used to predict cell growth and recombinant protein production and to develop an exponential feeding strategy using two carbon sources. Two exponential fed-batch fermentations were conducted according to the predicted feeding strategy at specific growth rates of 0.03 h(-1) and 0.07 h(-1) to verify the accuracy of the model. Cell growth was accurately predicted in both fed-batch runs; however, the model underestimated recombinant product concentration. The overall volumetric productivity of both runs was approximately 2.2 mg/L. h, representing a tenfold increase in the productivity compared with a heuristic feeding strategy. Copyright 2001 John Wiley & Sons, Inc.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part I. Low-order thermal modelling

NASA Astrophysics Data System (ADS)

Richardson, Robert R.; Zhao, Shi; Howey, David A.

2016-09-01

Estimating the temperature distribution within Li-ion batteries during operation is critical for safety and control purposes. Although existing control-oriented thermal models - such as thermal equivalent circuits (TEC) - are computationally efficient, they only predict average temperatures, and are unable to predict the spatially resolved temperature distribution throughout the cell. We present a low-order 2D thermal model of a cylindrical battery based on a Chebyshev spectral-Galerkin (SG) method, capable of predicting the full temperature distribution with a similar efficiency to a TEC. The model accounts for transient heat generation, anisotropic heat conduction, and non-homogeneous convection boundary conditions. The accuracy of the model is validated through comparison with finite element simulations, which show that the 2-D temperature field (r, z) of a large format (64 mm diameter) cell can be accurately modelled with as few as 4 states. Furthermore, the performance of the model for a range of Biot numbers is investigated via frequency analysis. For larger cells or highly transient thermal dynamics, the model order can be increased for improved accuracy. The incorporation of this model in a state estimation scheme with experimental validation against thermocouple measurements is presented in the companion contribution (http://www.sciencedirect.com/science/article/pii/S0378775316308163)

Gankyrin is a predictive and oncogenic factor in well-differentiated and dedifferentiated liposarcoma

PubMed Central

Hong, Doo-Pyo; Joo, Sung-Yeon; Choi, Yoon-La; Park, Joo-Hung; Lazar, Alexander J.; Pollock, Raphael E.; Lev, Dina; Kim, Sung Joo

2014-01-01

Liposarcoma is one of the most common histologic types of soft tissue sarcoma and is frequently an aggressive cancer with poor outcome. Hence, alternative approaches other than surgical excision are necessary to improve treatment of well-differentiated/dedifferentiated liposarcoma (WDLPS/DDLPS). For this reason, we performed a two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry/mass spectrometry (MALDI-TOF/MS) analysis to identify new factors for WDLPS and DDLPS. Among the selected candidate proteins, gankyrin, known to be an oncoprotein, showed a significantly high level of expression pattern and inversely low expression of p53/p21 in WDLPS and DDLPS tissues, suggesting possible utility as a new predictive factor. Moreover, inhibition of gankyrin not only led to reduction of in vitro cell growth ability including cell proliferation, colony-formation, and migration, but also in vivo DDLPS cell tumorigenesis, perhaps via downregulation of the p53 tumor suppressor gene and its p21 target and also reduction of AKT/mTOR signal activation. This study identifies gankyrin, for the first time, as new potential predictive and oncogenic factor of WDLPS and DDLPS, suggesting the potential for service as a future LPS therapeutic approach. PMID:25238053

Diagnostic value of highly-sensitive chimerism analysis after allogeneic stem cell transplantation.

PubMed

Sellmann, Lea; Rabe, Kim; Bünting, Ivonne; Dammann, Elke; Göhring, Gudrun; Ganser, Arnold; Stadler, Michael; Weissinger, Eva M; Hambach, Lothar

2018-05-02

Conventional analysis of host chimerism (HC) frequently fails to detect relapse before its clinical manifestation in patients with hematological malignancies after allogeneic stem cell transplantation (allo-SCT). Quantitative PCR (qPCR)-based highly-sensitive chimerism analysis extends the detection limit of conventional (short tandem repeats-based) chimerism analysis from 1 to 0.01% host cells in whole blood. To date, the diagnostic value of highly-sensitive chimerism analysis is hardly defined. Here, we applied qPCR-based chimerism analysis to 901 blood samples of 71 out-patients with hematological malignancies after allo-SCT. Receiver operating characteristics (ROC) curves were calculated for absolute HC values and for the increments of HC before relapse. Using the best cut-offs, relapse was detected with sensitivities of 74 or 85% and specificities of 69 or 75%, respectively. Positive predictive values (PPVs) were only 12 or 18%, but the respective negative predictive values were 98 or 99%. Relapse was detected median 38 or 45 days prior to clinical diagnosis, respectively. Considering also durations of steadily increasing HC of more than 28 days improved PPVs to more than 28 or 59%, respectively. Overall, highly-sensitive chimerism analysis excludes relapses with high certainty and predicts relapses with high sensitivity and specificity more than a month prior to clinical diagnosis.

The Impact of Epidemiological Factors and Treatment Interventions on Survival in Patients With Signet Ring Cell Carcinoma of the Pancreas.

PubMed

Patel, Mausam; Hans, Harliv S; Pan, Kelsey; Khan, Humza; Donath, Elie; Caldera, Humberto

2018-04-18

Primary pancreatic signet ring cell carcinoma (SRCC) is a rare histologic variant of pancreatic carcinoma. A population-based analysis of pancreatic SRCC was performed to determine the predictive effects of epidemiological factors and treatment interventions on overall survival (OS) and disease-specific survival (DSS). The Surveillance, Epidemiology, and End Results registry was searched for pancreatic SRCC cases diagnosed between January 1, 1973 and December 31, 2013. Statistical analysis was performed using the Fisher exact test, χ analysis, Kaplan-Meier method, log-rank test, and Cox proportional hazards regression. The mean age among 497 patients was 66.6 years (SD, 11.9). Most patients were white (82.7%) and male (54.5%). The 1-, 2-, and 5-year OS rates were 17%, 9%, and 4%, respectively, while the corresponding 1-, 2-, and 5-year rates for DSS were 18%, 10%, and 5%, respectively. On univariable analysis; age, site, grade, stage, and treatment were predictive of OS and DSS (P<0.05). On multivariable analysis; radiation improved OS and DSS (adjusted hazard ratio [aHR], 0.592 and 0.589, respectively), pancreatectomy improved OS and DSS (aHR, 0.360 and 0.355, respectively), and combination therapy improved OS and DSS (aHR, 0.295 and 0.286, respectively). Age, site, and stage were also independent predictors of OS and DSS. Subgroup analysis demonstrated treatment to be an independent predictor of OS and DSS in localized/regional disease, in distant disease, and in patients diagnosed between 2000 and 2013. Age, site, stage, and treatment independently predict OS and DSS in pancreatic SRCC.

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

PubMed

Carrasco Pro, S; Zimic, M; Nielsen, M

2014-02-01

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Treatment outcomes of patients with primary squamous cell carcinoma of the retromolar trigone.

PubMed

Rizvi, Zain H; Alonso, Jose E; Kuan, Edward C; St John, Maie A

2018-05-14

Squamous cell carcinoma of the retromolar trigone (RMT SCC) is a relatively uncommon primary site for oral cavity malignancy. However, given its proximity to the mandible and buccal mucosa, RMT SCC typically exhibits early invasion and generally presents at an advanced stage. Large-sample studies are needed to assess the epidemiology and clinical outcomes of this tumor. Our aim was to describe the determinants of survival in patients with RMT SCC. Retrospective cohort study. Retrospective, population-based cohort study of patients in the Surveillance, Epidemiology, and End Results tumor registry who were diagnosed with RMT SCC from 1973 to 2012. Primary endpoints were overall survival (OS) and disease-specific survival (DSS). A total of 4,022 cases of RMT SCC were identified. The mean age at diagnosis was 65 years. Thirty-nine percent of cases presented with stage IV disease. The median OS by stages I to IV were 73.7, 52.4, 27.5, and 23.4 months, respectively (P < .05). Overall, 34.3% of patients underwent surgery, 23.5% received radiation therapy, and 34.1% had both surgical and radiation therapy. On multivariate analysis, advanced age, greater tumor size, and advanced stage were associated with worse OS and DSS (P < .05), surgery predicted improved OS and DSS (P < .05), and radiation therapy predicted improved OS only (P < .05). RMT SCC is an aggressive malignancy that portends a poor prognosis, though early-stage tumors (stages I and II) have significantly improved survival. Any surgical intervention independently predicted higher survival outcomes. There may be a role of dual modality approaches, particularly for larger tumors. 4. Laryngoscope, 2018. © 2018 The American Laryngological, Rhinological and Otological Society, Inc.

LiDAR-based TWI and terrain attributes in improving parametric predictor for tree growth in southeast Finland

NASA Astrophysics Data System (ADS)

Mohamedou, Cheikh; Tokola, Timo; Eerikäinen, Kalle

2017-10-01

The effect of soil moisture content on vegetation and therefore on growth is well known. Information about the growth of forest stands is key in forest planning and management, and is the concern of various stakeholders. One way to assess moisture content and its impacts on forest growth is to apply the Topographic Wetness Index (TWI) and the derived terrain attributes from the Digital Terrain Model (DTM). The TWI is an important terrain attribute, used in various ecological studies. In the current study, a total of 9987 tally trees within 197 sample plots in southeastern Finland and LiDAR (Light Detection and Ranging) -based TWI were selected to examine: 1) the effect of cell resolutions and focal statistics of neighborhood cells of DTM, on tree diameter increment, and 2) possibilities to improve the prediction accuracy of an existing single-tree growth model using the terrain attributes and TWI with the combined effects of three characteristics (i.e., cell resolutions, neighborhood cells and terrain attributes). The results suggest that the TWI with terrain attributes improved the growth estimation significantly, and within different site types the Root Mean Square Errors (RMSE) were lowered substantially. The best results were obtained for birch trees. The higher resolution of the DTM and the lower focal neighborhood cells were found to be the best alternative in computing the TWI.

Shortening and Improving the Embryonic Stem Cell Test through the Use of Gene Biomarkers of Differentiation

PubMed Central

Romero, Andrea C.; Vilanova, Eugenio; Sogorb, Miguel A.

2011-01-01

The embryonic Stem cell Test (EST) is a validated assay for testing embryotoxicity in vitro. The total duration of this protocol is 10 days, and its main end-point is based on histological determinations. It is suggested that improvements on EST must be focused toward molecular end-points and, if possible, to reduce the total assay duration. Five days of exposure of D3 cells in monolayers under spontaneous differentiation to 50 ng/mL of the strong embryotoxic 5-fluorouracil or to 75 μg/mL of the weak embryotoxic 5,5-diphenylhydeantoin caused between 20 and 74% of reductions in the expression of the following genes: Pnpla6, Afp, Hdac7, Vegfa, and Nes. The exposure to 1 mg/mL of nonembryotoxic saccharin only caused statistically significant reductions in the expression of Nes. These exposures reduced cell viability of D3 cells by 15, 28, and 34%. We applied these records to the mathematical discriminating function of the EST method to find that this approach is able to correctly predict the embryotoxicity of all three above-mentioned chemicals. Therefore, this work proposes the possibility of improve EST by reducing its total duration and by introducing gene expression as biomarker of differentiation, which might be very interesting for in vitro risk assessment embryotoxicity. PMID:21876691

Development of an in vitro skin sensitization test using human cell lines; human Cell Line Activation Test (h-CLAT). II. An inter-laboratory study of the h-CLAT.

PubMed

Sakaguchi, H; Ashikaga, T; Miyazawa, M; Yoshida, Y; Ito, Y; Yoneyama, K; Hirota, M; Itagaki, H; Toyoda, H; Suzuki, H

2006-08-01

Recent regulatory changes have placed a major emphasis on in vitro safety testing and alternative models. In regard to skin sensitization tests, dendritic cells (DCs) derived from human peripheral blood have been considered in the development of new in vitro alternatives. Human cell lines have been also reported recently. In our previous study, we suggested that measuring CD86 and/or CD54 expression on THP-1 cells (human monocytic leukemia cell line) could be used as an in vitro skin sensitization method. An inter-laboratory study among two laboratories was undertaken in Japan in order to further develop an in vitro skin sensitization model. In the present study, we used two human cell lines: THP-1 and U-937 (human histiocytic lymphoma cell line). First we optimized our test protocol (refer to the related paper entitled "optimization of the h-CLAT protocol" within this journal) and then we did an inter-laboratory validation with nine chemicals using the optimized protocol. We measured the expression of CD86 and CD54 on the above cells using flow cytometry after a 24h and 48h exposure to six known allergens (e.g., DNCB, pPD, NiSO(4)) and three non-allergens (e.g., SLS, tween 80). For the sample test concentration, four doses (0.1x, 0.5x, 1x, and 2x of the 50% inhibitory concentration (IC(50))) were evaluated. IC(50) was calculated using MTT assay. We found that allergens/non-allergens were better predicted using THP-1 cells compared to U-937 cells following a 24 h and a 48 h exposure. We also found that the 24h treatment time tended to have a better accuracy than the 48 h treatment time for THP-1 cells. Expression of CD86 and CD54 were good predictive markers for THP-1 cells, but for U-937 cells, expression of CD86 was a better predictor than CD54, at the 24h and the 48 h treatment time. The accuracy also improved when both markers (CD86 and CD54) were used as compared with a single marker for THP-1 cells. Both laboratories gave a good prediction of allergen/non-allergen, especially using THP-1 cells. These results suggest that our method, human Cell Line Activation Test (h-CLAT), using human cell lines THP-1 and U-937, but especially THP-1 cells at 24h treatment, may be a useful in vitro skin sensitization model to predict various contact allergens.

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

NASA Astrophysics Data System (ADS)

Luna, Julio; Jemei, Samir; Yousfi-Steiner, Nadia; Husar, Attila; Serra, Maria; Hissel, Daniel

2016-10-01

In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.

Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity

PubMed Central

Zandvakili, Arya; Campbell, Ian; Weirauch, Matthew T.

2018-01-01

Cells use thousands of regulatory sequences to recruit transcription factors (TFs) and produce specific transcriptional outcomes. Since TFs bind degenerate DNA sequences, discriminating functional TF binding sites (TFBSs) from background sequences represents a significant challenge. Here, we show that a Drosophila regulatory element that activates Epidermal Growth Factor signaling requires overlapping, low-affinity TFBSs for competing TFs (Pax2 and Senseless) to ensure cell- and segment-specific activity. Testing available TF binding models for Pax2 and Senseless, however, revealed variable accuracy in predicting such low-affinity TFBSs. To better define parameters that increase accuracy, we developed a method that systematically selects subsets of TFBSs based on predicted affinity to generate hundreds of position-weight matrices (PWMs). Counterintuitively, we found that degenerate PWMs produced from datasets depleted of high-affinity sequences were more accurate in identifying both low- and high-affinity TFBSs for the Pax2 and Senseless TFs. Taken together, these findings reveal how TFBS arrangement can be constrained by competition rather than cooperativity and that degenerate models of TF binding preferences can improve identification of biologically relevant low affinity TFBSs. PMID:29617378

Low Tumor Infiltrating Mast Cell Density Confers Prognostic Benefit and Reflects Immunoactivation in Colorectal Cancer.

PubMed

Mao, Yihao; Feng, Qingyang; Zheng, Peng; Yang, Liangliang; Zhu, Dexiang; Chang, Wenju; Ji, Meiling; He, Guodong; Xu, Jianmin

2018-06-06

The role of mast cells (MCs) in colorectal cancer (CRC) progression was controversial. Thus, this study was designed to evaluate the prognostic value of MCs as well as their correlation with immune microenvironment. A retrospective cohort of CRC patients of stage I-IV was enrolled in this study. 854 consecutive patients were divided into training set (427 patients) and validation set (427 patients) randomly. The findings were further validated in a GEO cohort, GSE39582 (556 patients). The mast cell density (MCD) was measured by immunohistochemical staining of tryptase or by CIBERSORT algorithm. Low MCD predicted prolonged overall survival (OS) in training and validation set. Moreover, MCD was identified as an independent prognostic indicator in both sets. Better stratification for CRC prognosis can be achieved by building a MCD based nomogram. The prognostic role of MCD was further validated in GSE39582. In addition, MCD predicted improved survival in stage II and III CRC patients receiving adjuvant chemotherapy (ACT). Multiple immune pathways were enriched in low MCD group while cytokines/chemokines promoting anti-tumor immunity were highly expressed in such group. Furthermore, MCD was negatively correlated with CD8+ T cells infiltration. In conclusion, MCD was identified as an independent prognostic factor, as well as a potential biomarker for ACT benefit in stage II and III CRC. Better stratification of CRC prognosis could be achieved by building a MCD based nomogram. Moreover, immunoactivation in low MCD tumors may contributed to improved prognosis. This article is protected by copyright. All rights reserved. © 2018 UICC.

Twenty-year follow-up study of long-term survival of limited-stage small-cell lung cancer and overview of prognostic and treatment factors.

PubMed

Tai, Patricia; Tonita, Jon; Yu, Edward; Skarsgard, David

2003-07-01

To predict the long-term survival results of clinical trials earlier than using actuarial methods and to assess the factors predictive of long-term cure in patients with limited-stage small-cell lung cancer. Between 1981 and 1998, 1417 new cases of small-cell lung cancer were diagnosed in Saskatchewan, Canada, of which 244 were limited stage and treated with curative intent. They were followed to the end of February 2002. A parametric lognormal statistical model was retrospectively validated to determine whether long-term survival rates could be estimated several years earlier than is possible using the standard life-table actuarial method. The survival time of the uncured group followed a lognormal distribution. Four 2-year periods of diagnosis were combined, and patients were followed as a cohort for an additional 2 years. The estimated 10-year cause-specific survival rate was 13% by the lognormal model. The Kaplan-Meier calculation for 10-year cause-specific survival rate was 15% +/- 3%. The data also showed that the absence of mediastinal lymphadenopathy and higher chest radiotherapy dose were significant prognostic factors on multivariate analysis (p < 0.05). Among the 163 patients given prophylactic cranial irradiation, a higher biologically effective dose to the brain did not improve survival or decrease the incidence of brain metastases. The lognormal model has been validated for the estimation of survival in patients with limited-stage small-cell lung cancer. A higher biologically effective dose to the brain did not improve survival or decrease the incidence of brain metastases.

A comparative evaluation of in vitro skin sensitisation tests: the human cell-line activation test (h-CLAT) versus the local lymph node assay (LLNA).

PubMed

Ashikaga, Takao; Sakaguchi, Hitoshi; Sono, Sakiko; Kosaka, Nanae; Ishikawa, Makie; Nukada, Yuko; Miyazawa, Masaaki; Ito, Yuichi; Nishiyama, Naohiro; Itagaki, Hiroshi

2010-08-01

We previously developed the human cell-line activation test (h-CLAT) in vitro skin sensitisation test, based on our reported finding that a 24-hour exposure of THP-1 cells (a human monocytic leukaemia cell line) to sensitisers is sufficient to induce the augmented expression of CD86 and CD54. The aim of this study is to confirm the predictive value of h-CLAT for skin sensitisation activity by employing a larger number of test chemicals. One hundred chemicals were selected, according to their categorisation in the local lymph node assay (LLNA), as being: extreme, strong, moderate and weak sensitisers, and non-sensitisers. The correlation of the h-CLAT results with the LLNA results was 84%. There were some false negatives (e.g. benzoyl peroxide, hexyl cinnamic aldehyde) and some false positives (e.g. 1-bromobutane, diethylphthalate). Eight out of the 9 false negatives (89%) were water-insoluble chemicals. The h-CLAT could positively predict not only extreme and strong sensitisers, but also moderate and weak sensitisers, though the detection rates of weak sensitisers and non-sensitisers were comparatively low. Some sensitisers enhanced both CD86 and CD54 levels, and some enhanced the level of only one of them. The use of the combination of CD86 and CD54 induction as a positive indicator, improved the accuracy of the test. In conclusion, the h-CLAT is expected to be a useful cell-based in vitro method for predicting skin sensitisation potential. 2010 FRAME.

Pathobiologic Markers of the Ewing Sarcoma Family of Tumors: State of the Art and Prediction of Behaviour

PubMed Central

Pinto, Alfredo; Dickman, Paul; Parham, David

2011-01-01

Over the past three decades, the outcome of Ewing sarcoma family tumor (ESFT) patients who are nonmetastatic at presentation has improved considerably. The prognosis of patients with metastatic disease at the time of diagnosis and recurrence after therapy remains dismal. Drug-resistant disease at diagnosis or at relapse remains a major cause of mortality among patients diagnosed with ESFT. In order to improve the outcome for patients with potential relapse, there is an urgent need to find reliable markers that either predict tumor behaviour at diagnosis or identify therapeutic molecular targets at the time of recurrence. An improved understanding of the cell of origin and the molecular pathways that regulate tumorigenicity in ESFT should aid us in the search for novel therapies for ESFT. The purpose of this paper is thus to outline current concepts of sarcomagenesis in ESFT and to discuss ESFT patterns of differentiation and molecular markers that might affect prognosis or direct future therapeutic development. PMID:20981347

Nuclear factor one B (NFIB) encodes a subtype-specific tumour suppressor in glioblastoma

PubMed Central

Stringer, Brett W.; Bunt, Jens; Day, Bryan W.; Barry, Guy; Jamieson, Paul R.; Ensbey, Kathleen S.; Bruce, Zara C.; Goasdoué, Kate; Vidal, Hélène; Charmsaz, Sara; Smith, Fiona M.; Cooper, Leanne T.; Piper, Michael

2016-01-01

Glioblastoma (GBM) is an essentially incurable and rapidly fatal cancer, with few markers predicting a favourable prognosis. Here we report that the transcription factor NFIB is associated with significantly improved survival in GBM. NFIB expression correlates inversely with astrocytoma grade and is lowest in mesenchymal GBM. Ectopic expression of NFIB in low-passage, patient-derived classical and mesenchymal subtype GBM cells inhibits tumourigenesis. Ectopic NFIB expression activated phospho-STAT3 signalling only in classical and mesenchymal GBM cells, suggesting a mechanism through which NFIB may exert its context-dependent tumour suppressor activity. Finally, NFIB expression can be induced in GBM cells by drug treatment with beneficial effects. PMID:27083054

Cytomics in predictive medicine

NASA Astrophysics Data System (ADS)

Tarnok, Attila; Valet, Guenther K.

2004-07-01

Predictive Medicine aims at the detection of changes in patient's disease state prior to the manifestation of deterioration or improvement of the current status. Patient-specific, disease-course predictions with >95% or >99% accuracy during therapy would be highly valuable for everyday medicine. If these predictors were available, disease aggravation or progression, frequently accompanied by irreversible tissue damage or therapeutic side effects, could then potentially be avoided by early preventive therapy. The molecular analysis of heterogeneous cellular systems (Cytomics) by cytometry in conjunction with pattern-oriented bioinformatic analysis of the multiparametric cytometric and other data provides a promising approach to individualized or personalized medical treatment or disease management. Predictive medicine is best implemented by cell oriented measurements e.g. by flow or image cytometry. Cell oriented gene or protein arrays as well as bead arrays for the capture of solute molecules form serum, plasma, urine or liquor are equally of high value. Clinical applications of predictive medicine by Cytomics will include multi organ failure in sepsis or non infectious posttraumatic shock in intensive care, or the pretherapeutic identification of high risk patients in cancer cytostatic. Early individualized therapy may provide better survival chances for individual patient at concomitant cost containment. Predictive medicine guided early reduction or stop of therapy may lower or abrogate potential therapeutic side effects. Further important aspects of predictive medicine concern the preoperative identification of patients with a tendency for postoperative complications or coronary artery disease patients with an increased tendency for restenosis. As a consequence, better patient care and new forms of inductive scientific hypothesis development based on the interpretation of predictive data patterns are at reach.

MiRNA Expression Analysis of Pretreatment Biopsies Predicts the Pathological Response of Esophageal Squamous Cell Carcinomas to Neoadjuvant Chemoradiotherapy.

PubMed

Wen, Jing; Luo, Kongjia; Liu, Hui; Liu, Shiliang; Lin, Guangrong; Hu, Yi; Zhang, Xu; Wang, Geng; Chen, Yuping; Chen, Zhijian; Li, Yi; Lin, Ting; Xie, Xiuying; Liu, Mengzhong; Wang, Huiyun; Yang, Hong; Fu, Jianhua

2016-05-01

To identify miRNA markers useful for esophageal squamous cell carcinoma (ESCC) neoadjuvant chemoradiotherapy (neo-CRT) response prediction. Neo-CRT followed by surgery improves ESCC patients' survival compared with surgery alone. However, CRT outcomes are heterogeneous, and no current methods can predict CRT responses. Differentially expressed miRNAs between ESCC pathological responders and nonresponders after neo-CRT were identified by miRNA profiling and verified by real-time quantitative polymerase chain reaction (qPCR) of 27 ESCCs in the training set. Several class prediction algorithms were used to build the response-classifying models with the qPCR data. Predictive powers of the models were further assessed with a second set of 79 ESCCs. Ten miRNAs with greater than a 1.5-fold change between pathological responders and nonresponders were identified and verified, respectively. A support vector machine (SVM) prediction model, composed of 4 miRNAs (miR-145-5p, miR-152, miR-193b-3p, and miR-376a-3p), were developed. It provided overall accuracies of 100% and 87.3% for discriminating pathological responders and nonresponders in the training and external validation sets, respectively. In multivariate analysis, the subgroup determined by the SVM model was the only independent factor significantly associated with neo-CRT response in the external validation sets. Combined qPCR of the 4 miRNAs provides the possibility of ESCC neo-CRT response prediction, which may facilitate individualized ESCC treatment. Further prospective validation in larger independent cohorts is necessary to fully assess its predictive power.

Improving stability of prediction models based on correlated omics data by using network approaches.

PubMed

Tissier, Renaud; Houwing-Duistermaat, Jeanine; Rodríguez-Girondo, Mar

2018-01-01

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

White butterflies as solar photovoltaic concentrators

PubMed Central

Shanks, Katie; Senthilarasu, S.; ffrench-Constant, Richard H.; Mallick, Tapas K.

2015-01-01

Man’s harvesting of photovoltaic energy requires the deployment of extensive arrays of solar panels. To improve both the gathering of thermal and photovoltaic energy from the sun we have examined the concept of biomimicry in white butterflies of the family Pieridae. We tested the hypothesis that the V-shaped posture of basking white butterflies mimics the V-trough concentrator which is designed to increase solar input to photovoltaic cells. These solar concentrators improve harvesting efficiency but are both heavy and bulky, severely limiting their deployment. Here, we show that the attachment of butterfly wings to a solar cell increases its output power by 42.3%, proving that the wings are indeed highly reflective. Importantly, and relative to current concentrators, the wings improve the power to weight ratio of the overall structure 17-fold, vastly expanding their potential application. Moreover, a single mono-layer of scale cells removed from the butterflies’ wings maintained this high reflectivity showing that a single layer of scale cell-like structures can also form a useful coating. As predicted, the wings increased the temperature of the butterflies’ thorax dramatically, showing that the V-shaped basking posture of white butterflies has indeed evolved to increase the temperature of their flight muscles prior to take-off. PMID:26227341

Modeling photovoltaic performance in periodic patterned colloidal quantum dot solar cells.

PubMed

Fu, Yulan; Dinku, Abay G; Hara, Yukihiro; Miller, Christopher W; Vrouwenvelder, Kristina T; Lopez, Rene

2015-07-27

Colloidal quantum dot (CQD) solar cells have attracted tremendous attention mostly due to their wide absorption spectrum window and potentially low processability cost. The ultimate efficiency of CQD solar cells is highly limited by their high trap state density. Here we show that the overall device power conversion efficiency could be improved by employing photonic structures that enhance both charge generation and collection efficiencies. By employing a two-dimensional numerical model, we have calculated the characteristics of patterned CQD solar cells based of a simple grating structure. Our calculation predicts a power conversion efficiency as high as 11.2%, with a short circuit current density of 35.2 mA/cm2, a value nearly 1.5 times larger than the conventional flat design, showing the great potential value of patterned quantum dot solar cells.

Cardiotoxicity evaluation using human embryonic stem cells and induced pluripotent stem cell-derived cardiomyocytes.

PubMed

Zhao, Qi; Wang, Xijie; Wang, Shuyan; Song, Zheng; Wang, Jiaxian; Ma, Jing

2017-03-09

Cardiotoxicity remains an important concern in drug discovery. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have become an attractive platform to evaluate cardiotoxicity. However, the consistency between human embryonic stem cell-derived cardiomyocytes (hESC-CMs) and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in prediction of cardiotoxicity has yet to be elucidated. Here we screened the toxicities of four representative drugs (E-4031, isoprenaline, quinidine, and haloperidol) using both hESC-CMs and hiPSC-CMs, combined with an impedance-based bioanalytical method. It showed that both hESC-CMs and hiPSC-CMs can recapitulate cardiotoxicity and identify the effects of well-characterized compounds. The combined platform of hPSC-CMs and an impedance-based bioanalytical method could improve preclinical cardiotoxicity screening, holding great potential for increasing drug development accuracy.

AMTEC radioisotope power system for the Pluto Express mission

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

Ivanenok, J.F. III; Sievers, R.K.

1995-12-31

The Alkali Metal Thermal to Electric Converter (AMTEC) technology has made substantial advances in the last 3 years through design improvements and technical innovations. In 1993 programs began to produce an AMTEC cell specifically for the NASA Pluto Express Mission. A set of efficiency goals was established for this series of cells to be developed. According to this plan, cell {number_sign}8 would be 17% efficient but was actually 18% efficient. Achieving this goal, as well as design advances that allow the cell to be compact, has resulted in pushing the cell from an unexciting 2 W/kg and 2% efficiency tomore » very attractive 40 W/kg and 18% measured efficiency. This paper will describe the design and predict the performance of a radioisotope powered AMTEC system for the Pluto Express mission.« less

Dissecting Germ Cell Metabolism through Network Modeling.

PubMed

Whitmore, Leanne S; Ye, Ping

2015-01-01

Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.

Use of a small molecule cell cycle inhibitor to control cell growth and improve specific productivity and product quality of recombinant proteins in CHO cell cultures.

PubMed

Du, Zhimei; Treiber, David; McCarter, John D; Fomina-Yadlin, Dina; Saleem, Ramsey A; McCoy, Rebecca E; Zhang, Yuling; Tharmalingam, Tharmala; Leith, Matthew; Follstad, Brian D; Dell, Brad; Grisim, Brent; Zupke, Craig; Heath, Carole; Morris, Arvia E; Reddy, Pranhitha

2015-01-01

The continued need to improve therapeutic recombinant protein productivity has led to ongoing assessment of appropriate strategies in the biopharmaceutical industry to establish robust processes with optimized critical variables, that is, viable cell density (VCD) and specific productivity (product per cell, qP). Even though high VCD is a positive factor for titer, uncontrolled proliferation beyond a certain cell mass is also undesirable. To enable efficient process development to achieve consistent and predictable growth arrest while maintaining VCD, as well as improving qP, without negative impacts on product quality from clone to clone, we identified an approach that directly targets the cell cycle G1-checkpoint by selectively inhibiting the function of cyclin dependent kinases (CDK) 4/6 with a small molecule compound. Results from studies on multiple recombinant Chinese hamster ovary (CHO) cell lines demonstrate that the selective inhibitor can mediate a complete and sustained G0/G1 arrest without impacting G2/M phase. Cell proliferation is consistently and rapidly controlled in all recombinant cell lines at one concentration of this inhibitor throughout the production processes with specific productivities increased up to 110 pg/cell/day. Additionally, the product quality attributes of the mAb, with regard to high molecular weight (HMW) and glycan profile, are not negatively impacted. In fact, high mannose is decreased after treatment, which is in contrast to other established growth control methods such as reducing culture temperature. Microarray analysis showed major differences in expression of regulatory genes of the glycosylation and cell cycle signaling pathways between these different growth control methods. Overall, our observations showed that cell cycle arrest by directly targeting CDK4/6 using selective inhibitor compound can be utilized consistently and rapidly to optimize process parameters, such as cell growth, qP, and glycosylation profile in recombinant antibody production cultures. © 2014 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

The non-small cell lung cancer immune landscape: emerging complexity, prognostic relevance and prospective significance in the context of immunotherapy.

PubMed

Anichini, Andrea; Tassi, Elena; Grazia, Giulia; Mortarini, Roberta

2018-06-01

Immunotherapy of non-small cell lung cancer (NSCLC), by immune checkpoint inhibitors, has profoundly improved the clinical management of advanced disease. However, only a fraction of patients respond and no effective predictive factors have been defined. Here, we discuss the prospects for identification of such predictors of response to immunotherapy, by fostering an in-depth analysis of the immune landscape of NSCLC. The emerging picture, from several recent studies, is that the immune contexture of NSCLC lesions is a complex and heterogeneous feature, as documented by analysis for frequency, phenotype and spatial distribution of innate and adaptive immune cells, and by characterization of functional status of inhibitory receptor + T cells. The complexity of the immune landscape of NSCLC stems from the interaction of several factors, including tumor histology, molecular subtype, main oncogenic drivers, nonsynonymous mutational load, tumor aneuploidy, clonal heterogeneity and tumor evolution, as well as the process of epithelial-mesenchymal transition. All these factors contribute to shape NSCLC immune profiles that have clear prognostic significance. An integrated analysis of the immune and molecular profile of the neoplastic lesions may allow to define the potential predictive role of the immune landscape for response to immunotherapy.

Simulator of Non-homogenous Alumina and Current Distribution in an Aluminum Electrolysis Cell to Predict Low-Voltage Anode Effects

NASA Astrophysics Data System (ADS)

Dion, Lukas; Kiss, László I.; Poncsák, Sándor; Lagacé, Charles-Luc

2018-04-01

Perfluorocarbons are important contributors to aluminum production greenhouse gas inventories. Tetrafluoromethane and hexafluoroethane are produced in the electrolysis process when a harmful event called anode effect occurs in the cell. This incident is strongly related to the lack of alumina and the current distribution in the cell and can be classified into two categories: high-voltage and low-voltage anode effects. The latter is hard to detect during the normal electrolysis process and, therefore, new tools are necessary to predict this event and minimize its occurrence. This paper discusses a new approach to model the alumina distribution behavior in an electrolysis cell by dividing the electrolytic bath into non-homogenous concentration zones using discrete elements. The different mechanisms related to the alumina distribution are discussed in detail. Moreover, with a detailed electrical model, it is possible to calculate the current distribution among the different anodic assemblies. With this information, the model can evaluate if low-voltage emissions are likely to be present under the simulated conditions. Using the simulator will help the understanding of the role of the alumina distribution which, in turn, will improve the cell energy consumption and stability while reducing the occurrence of high- and low-voltage anode effects.

Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models.

PubMed

Vermaat, J S; van der Tweel, I; Mehra, N; Sleijfer, S; Haanen, J B; Roodhart, J M; Engwegen, J Y; Korse, C M; Langenberg, M H; Kruit, W; Groenewegen, G; Giles, R H; Schellens, J H; Beijnen, J H; Voest, E E

2010-07-01

In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.

How long will my mouse live? Machine learning approaches for prediction of mouse life span.

PubMed

Swindell, William R; Harper, James M; Miller, Richard A

2008-09-01

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (+/-0.10%). This result provides a new benchmark for the development of life-span-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

Effect of membranes on oxygen transfer rate and consumption within a newly developed three-compartment bioartificial liver device: Advanced experimental and theoretical studies.

PubMed

Hilal-Alnaqbi, Ali; Mourad, Abdel-Hamid I; Yousef, Basem F

2014-01-01

A mathematical model is developed to predict oxygen transfer in the fiber-in-fiber (FIF) bioartificial liver device. The model parameters are taken from the constructed and tested FIF modules. We extended the Krogh cylinder model by including one more zone for oxygen transfer. Cellular oxygen uptake was based on Michaelis-Menten kinetics. The effect of varying a number of important model parameters is investigated, including (1) oxygen partial pressure at the inlet, (2) the hydraulic permeability of compartment B (cell region), (3) the hydraulic permeability of the inner membrane, and (4) the oxygen diffusivity of the outer membrane. The mathematical model is validated by comparing its output against the experimentally acquired values of an oxygen transfer rate and the hydrostatic pressure drop. Three governing simultaneous linear differential equations are derived to predict and validate the experimental measurements, e.g., the flow rate and the hydrostatic pressure drop. The model output simulated the experimental measurements to a high degree of accuracy. The model predictions show that the cells in the annulus can be oxygenated well even at high cell density or at a low level of gas phase PG if the value of the oxygen diffusion coefficient Dm is 16 × 10(-5) . The mathematical model also shows that the performance of the FIF improves by increasing the permeability of polypropylene membrane (inner fiber). Moreover, the model predicted that 60% of plasma has access to the cells in the annulus within the first 10% of the FIF bioreactor axial length for a specific polypropylene membrane permeability and can reach 95% within the first 30% of its axial length. © 2013 International Union of Biochemistry and Molecular Biology, Inc.

A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

NASA Astrophysics Data System (ADS)

Garciá-Arteaga, Juan D.; Corredor, Germán.; Wang, Xiangxue; Velcheti, Vamsidhar; Madabhushi, Anant; Romero, Eduardo

2017-11-01

Tumor-infiltrating lymphocytes occurs when various classes of white blood cells migrate from the blood stream towards the tumor, infiltrating it. The presence of TIL is predictive of the response of the patient to therapy. In this paper, we show how the automatic detection of lymphocytes in digital H and E histopathological images and the quantitative evaluation of the global lymphocyte configuration, evaluated through global features extracted from non-parametric graphs, constructed from the lymphocytes' detected positions, can be correlated to the patient's outcome in early-stage non-small cell lung cancer (NSCLC). The method was assessed on a tissue microarray cohort composed of 63 NSCLC cases. From the evaluated graphs, minimum spanning trees and K-nn showed the highest predictive ability, yielding F1 Scores of 0.75 and 0.72 and accuracies of 0.67 and 0.69, respectively. The predictive power of the proposed methodology indicates that graphs may be used to develop objective measures of the infiltration grade of tumors, which can, in turn, be used by pathologists to improve the decision making and treatment planning processes.

Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells

PubMed Central

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xiaohong; Molinelli, Evan J; Babur, Özgün; Bemis, Debra L; Onur Sumer, Selcuk; Solit, David B; Pratilas, Christine A; Sander, Chris

2015-01-01

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs. DOI: http://dx.doi.org/10.7554/eLife.04640.001 PMID:26284497

Biomarkers of head and neck cancer, tools or a gordian knot?

PubMed

Lampri, Evangeli S; Chondrogiannis, Georgios; Ioachim, Elli; Varouktsi, Anna; Mitselou, Antigoni; Galani, Aggeliki; Briassoulis, Evangelos; Kanavaros, Panagiotis; Galani, Vasiliki

2015-01-01

Head and neck tumors comprise a wide spectrum of heterogeneous neoplasms for which biomarkers are needed to aid in earlier diagnosis, risk assessment and therapy response. Personalized medicine based on predictive markers linked to drug response, it is hoped, will lead to improvements in outcomes and avoidance of unnecessary treatment in carcinoma of the head and neck. Because of the heterogeneity of head and neck tumors, the integration of multiple selected markers in association with the histopathologic features is advocated for risk assessment. Validation of each biomarker in the context of clinical trials will be required before a specific marker can be incorporated into daily practice. Furthermore, we will give evidence that some proteins implicated in cell-cell interaction, such as CD44 may be involved in the multiple mechanism of the development and progression of laryngeal lesions and may help to predict the risk of transformation of the benign or precancerous lesions to cancer.

Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells

PubMed Central

Passante, E; Würstle, M L; Hellwig, C T; Leverkus, M; Rehm, M

2013-01-01

Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein–protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future. PMID:23933815

Papilla regeneration by injectable stem cell therapy with regenerative medicine: long-term clinical prognosis.

PubMed

Yamada, Yoichi; Nakamura, Sayaka; Ueda, Minoru; Ito, Kenji

2015-03-01

Black triangle (BT), an open interproximal space between teeth, can cause aesthetic concerns, food impaction, phonetic difficulties and periodontitis. The aim of this study was to determine the possibility and long-term prognosis of novel papilla regeneration with regenerative medicine, i.e. tissue-engineered papilla (TEP), and to investigate the potential of a tissue-engineering method for soft-tissue augmentation, especially aesthetic improvement of BT, with mesenchymal stem cells (MSCs) as the isolated cells, platelet-rich plasma (PRP) as the growth factor and hyaluronic acid (HA) as the scaffold. The parameters were assessed from a clinical point of view by measuring the distance from the tip of the interproximal papilla to the base of the contact area in each study region. The mean volumes, operation times and follow-up periods of TEP were 1.32 ± 0.25 ml, 2.2 ± 1.62 times and 55.3 ± 17.7 months; the mean improved BT values were 2.55 ± 0.89 mm. An aesthetic improvement was achieved. TEP was able to provide aesthetic improvement of black triangle and predictable results, and could emerge as another novel option for periodontal regenerative therapy in periodontal diseases. Copyright © 2013 John Wiley & Sons, Ltd.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

PubMed Central

Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

2011-01-01

Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

A novel genotoxin-specific qPCR array based on the metabolically competent human HepaRG™ cell line as a rapid and reliable tool for improved in vitro hazard assessment.

PubMed

Ates, Gamze; Mertens, Birgit; Heymans, Anja; Verschaeve, Luc; Milushev, Dimiter; Vanparys, Philippe; Roosens, Nancy H C; De Keersmaecker, Sigrid C J; Rogiers, Vera; Doktorova, Tatyana Y

2018-04-01

Although the value of the regulatory accepted batteries for in vitro genotoxicity testing is recognized, they result in a high number of false positives. This has a major impact on society and industries developing novel compounds for pharmaceutical, chemical, and consumer products, as afflicted compounds have to be (prematurely) abandoned or further tested on animals. Using the metabolically competent human HepaRG ™ cell line and toxicogenomics approaches, we have developed an upgraded, innovative, and proprietary gene classifier. This gene classifier is based on transcriptomic changes induced by 12 genotoxic and 12 non-genotoxic reference compounds tested at sub-cytotoxic concentrations, i.e., IC10 concentrations as determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The resulting gene classifier was translated into an easy-to-handle qPCR array that, as shown by pathway analysis, covers several different cellular processes related to genotoxicity. To further assess the predictivity of the tool, a set of 5 known positive and 5 known negative test compounds for genotoxicity was evaluated. In addition, 2 compounds with debatable genotoxicity data were tested to explore how the qPCR array would classify these. With an accuracy of 100%, when equivocal results were considered positive, the results showed that combining HepaRG ™ cells with a genotoxin-specific qPCR array can improve (geno)toxicological hazard assessment. In addition, the developed qPCR array was able to provide additional information on compounds for which so far debatable genotoxicity data are available. The results indicate that the new in vitro tool can improve human safety assessment of chemicals in general by basing predictions on mechanistic toxicogenomics information.

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy.

PubMed

Kim, Eunjung; Kim, Jae-Young; Smith, Matthew A; Haura, Eric B; Anderson, Alexander R A

2018-03-01

During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.

Extended use of superconducting magnets for bio-medical development

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

Stoynev, Stoyan E.

Magnetic fields interact with biological cells affecting them in variety of ways which are usually hard to predict. Among them, it was observed that strong fields can align dividing cells in a preferred direction. It was also demonstrated that dividing cancer cells are effectively destroyed by applying electric fields in vivo with a success rate dependent on the cell-to-field orientation. Based on these facts, the present note aims to suggest the use of magnetic and electric fields for improved cancer treatment. Several possibilities of generating the electric fields inside the magnetic field volume are reviewed, main tentative approaches are describedmore » and discussed. Most if not all of them require special magnet configuration research which can be based on existing magnet systems in operation or in development.« less

Effect of pore architecture on oxygen diffusion in 3D scaffolds for tissue engineering.

PubMed

Ahn, Geunseon; Park, Jeong Hun; Kang, Taeyun; Lee, Jin Woo; Kang, Hyun-Wook; Cho, Dong-Woo

2010-10-01

The aim of this study was to maximize oxygen diffusion within a three-dimensional scaffold in order to improve cell viability and proliferation. To evaluate the effect of pore architecture on oxygen diffusion, we designed a regular channel shape with uniform diameter, referred to as cylinder shaped, and a new channel shape with a channel diameter gradient, referred to as cone shaped. A numerical analysis predicted higher oxygen concentration in the cone-shaped channels than in the cylinder-shaped channels, throughout the scaffold. To confirm these numerical results, we examined cell proliferation and viability in 2D constructs and 3D scaffolds. Cell culture experiments revealed that cell proliferation and viability were superior in the constructs and scaffolds with cone-shaped channels.

Mathematical modeling of prostate cancer progression in response to androgen ablation therapy.

PubMed

Jain, Harsh Vardhan; Clinton, Steven K; Bhinder, Arvinder; Friedman, Avner

2011-12-06

Prostate cancer progression depends in part on the complex interactions between testosterone, its active metabolite DHT, and androgen receptors. In a metastatic setting, the first line of treatment is the elimination of testosterone. However, such interventions are not curative because cancer cells evolve via multiple mechanisms to a castrate-resistant state, allowing progression to a lethal outcome. It is hypothesized that administration of antiandrogen therapy in an intermittent, as opposed to continuous, manner may bestow improved disease control with fewer treatment-related toxicities. The present study develops a biochemically motivated mathematical model of antiandrogen therapy that can be tested prospectively as a predictive tool. The model includes "personalized" parameters, which address the heterogeneity in the predicted course of the disease under various androgen-deprivation schedules. Model simulations are able to capture a variety of clinically observed outcomes for "average" patient data under different intermittent schedules. The model predicts that in the absence of a competitive advantage of androgen-dependent cancer cells over castration-resistant cancer cells, intermittent scheduling can lead to more rapid treatment failure as compared to continuous treatment. However, increasing a competitive advantage for hormone-sensitive cells swings the balance in favor of intermittent scheduling, delaying the acquisition of genetic or epigenetic alterations empowering androgen resistance. Given the near universal prevalence of antiandrogen treatment failure in the absence of competing mortality, such modeling has the potential of developing into a useful tool for incorporation into clinical research trials and ultimately as a prognostic tool for individual patients.

Investigation of metabolic objectives in cultured hepatocytes.

PubMed

Uygun, Korkut; Matthew, Howard W T; Huang, Yinlun

2007-06-15

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

Multiphoton versus confocal high resolution z-sectioning of enhanced green fluorescent microtubules: increased multiphoton photobleaching within the focal plane can be compensated using a Pockels cell and dual widefield detectors.

PubMed

Drummond, D R; Carter, N; Cross, R A

2002-05-01

Multiphoton excitation was originally projected to improve live cell fluorescence imaging by minimizing photobleaching effects outside the focal plane, yet reports suggest that photobleaching within the focal plane is actually worse than with one photon excitation. We confirm that when imaging enhanced green fluorescent protein, photobleaching is indeed more acute within the multiphoton excitation volume, so that whilst fluorescence increases as predicted with the square of the excitation power, photobleaching rates increase with a higher order relationship. Crucially however, multiphoton excitation also affords unique opportunities for substantial improvements to fluorescence detection. By using a Pockels cell to minimize exposure of the specimen together with multiple nondescanned detectors we show quantitatively that for any particular bleach rate multiphoton excitation produces significantly more signal than one photon excitation confocal microscopy in high resolution Z-axis sectioning of thin samples. Both modifications are readily implemented on a commercial multiphoton microscope system.

[The role of microRNAs in head and neck squamous cell carcinoma : Biomarkers for prognosis, therapy selection, and novel therapeutics].

PubMed

Heß, A K; Weichert, W; Budach, V; Tinhofer, I

2016-05-01

Despite recent advances in radiochemotherapy, treatment of locally advanced head and neck squamous cell carcinoma is still challenging, and survival rates have improved only slightly. This is due to the high frequency of metastases and local and/or regional tumor recurrences that have acquired radio- or chemoresistance. MiRNAs regulate diverse processes in tumorigenesis, metastasis, and therapy resistance in head and neck squamous cell carcinoma. Hence, miRNAs are highly valued in biomarker studies. Establishment of the miRNA profiles of oropharyngeal tumors enables personalized treatment selection, since expression of distinct miRNAs can predict the response to two different radiochemotherapy regimens. Development of novel miRNA therapeutics has a high clinical potential for further improving treatment of cancerous disease. The use of nanoparticles with distinct surface modifications as miRNA vectors permits prolonged bioavailability, high efficacy in tumor targeting, and low toxicity. Nevertheless, the efficacy of miRNA therapy has only been shown in animal models to date.

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

NASA Astrophysics Data System (ADS)

Xu, Yadong; Serre, Marc L.; Reyes, Jeanette M.; Vizuete, William

2017-10-01

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error.

[Diagnosis, prognosis, and prediction of non-small cell lung cancer. Importance of morphology, immunohistochemistry and molecular pathology].

PubMed

Warth, A

2015-11-01

Tumor diagnostics are based on histomorphology, immunohistochemistry and molecular pathological analysis of mutations, translocations and amplifications which are of diagnostic, prognostic and/or predictive value. In recent decades only histomorphology was used to classify lung cancer as either small (SCLC) or non-small cell lung cancer (NSCLC), although NSCLC was further subdivided in different entities; however, as no specific therapy options were available classification of specific subtypes was not clinically meaningful. This fundamentally changed with the discovery of specific molecular alterations in adenocarcinoma (ADC), e.g. mutations in KRAS, EGFR and BRAF or translocations of the ALK and ROS1 gene loci, which now form the basis of targeted therapies and have led to a significantly improved patient outcome. The diagnostic, prognostic and predictive value of imaging, morphological, immunohistochemical and molecular characteristics as well as their interaction were systematically assessed in a large cohort with available clinical data including patient survival. Specific and sensitive diagnostic markers and marker panels were defined and diagnostic test algorithms for predictive biomarker assessment were optimized. It was demonstrated that the semi-quantitative assessment of ADC growth patterns is a stage-independent predictor of survival and is reproducibly applicable in the routine setting. Specific histomorphological characteristics correlated with computed tomography (CT) imaging features and thus allowed an improved interdisciplinary classification, especially in the preoperative or palliative setting. Moreover, specific molecular characteristics, for example BRAF mutations and the proliferation index (Ki-67) were identified as clinically relevant prognosticators. Comprehensive clinical, morphological, immunohistochemical and molecular assessment of NSCLCs allow an optimized patient stratification. Respective algorithms now form the backbone of the 2015 lung cancer World Health Organization (WHO) classification.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

PubMed

Karr, Jonathan R; Williams, Alex H; Zucker, Jeremy D; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A; Bot, Brian M; Hoff, Bruce R; Kellen, Michael R; Covert, Markus W; Stolovitzky, Gustavo A; Meyer, Pablo

2015-05-01

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Development of lithium doped radiation resistent solar cells

NASA Technical Reports Server (NTRS)

Berman, P. A.

1972-01-01

Lithium-doped solar cells have been fabricated with initial lot efficiencies averaging 11.9 percent in an air mass zero (AMO) solar simulator and a maximum observed efficiency of 12.8 percent. The best lithium-doped solar cells are approximately 15 percent higher in maximum power than state-of-the-art n-p cells after moderate to high fluences of 1-MeV electrons and after 6-7 months exposure to low flux irradiation by a Sr-90 beta source, which approximates the electron spectrum and flux associated with near Earth space. Furthermore, lithium-doped cells were found to degrade at a rate only one tenth that of state-of-the-art n-p cells under 28-MeV electron irradiation. Excellent progress has been made in quantitative predictions of post-irradiation current-voltage characteristics as a function of cell design by means of capacitance-voltage measurements, and this information has been used to achieve further improvements in lithium-doped cell design.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

PubMed Central

Karr, Jonathan R.; Williams, Alex H.; Zucker, Jeremy D.; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A.; Bot, Brian M.; Hoff, Bruce R.; Kellen, Michael R.; Covert, Markus W.; Stolovitzky, Gustavo A.; Meyer, Pablo

2015-01-01

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. PMID:26020786

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

C-Reactive Protein and Prediction of 1-Year Mortality in Prevalent Hemodialysis Patients

PubMed Central

Bazeley, Jonathan; Bieber, Brian; Li, Yun; Morgenstern, Hal; de Sequera, Patricia; Combe, Christian; Yamamoto, Hiroyasu; Gallagher, Martin; Port, Friedrich K.

2011-01-01

Summary Background and objectives Measurement of C-reactive protein (CRP) levels remains uncommon in North America, although it is now routine in many countries. Using Dialysis Outcomes and Practice Patterns Study data, our primary aim was to evaluate the value of CRP for predicting mortality when measured along with other common inflammatory biomarkers. Design, setting, participants, & measurements We studied 5061 prevalent hemodialysis patients from 2005 to 2008 in 140 facilities routinely measuring CRP in 10 countries. The association of CRP with mortality was evaluated using Cox regression. Prediction of 1-year mortality was assessed in logistic regression models with differing adjustment variables. Results Median baseline CRP was lower in Japan (1.0 mg/L) than other countries (6.0 mg/L). CRP was positively, monotonically associated with mortality. No threshold below which mortality rate leveled off was identified. In prediction models, CRP performance was comparable with albumin and exceeded ferritin and white blood cell (WBC) count based on measures of model discrimination (c-statistics, net reclassification improvement [NRI]) and global model fit (generalized R2). The primary analysis included age, gender, diabetes, catheter use, and the four inflammatory markers (omitting one at a time). Specifying NRI ≥5% as appropriate reclassification of predicted mortality risk, NRI for CRP was 12.8% compared with 10.3% for albumin, 0.8% for ferritin, and <0.1% for WBC. Conclusions These findings demonstrate the value of measuring CRP in addition to standard inflammatory biomarkers to improve mortality prediction in hemodialysis patients. Future studies are indicated to identify interventions that lower CRP and to identify whether they improve clinical outcomes. PMID:21868617

A Bayesian network approach for modeling local failure in lung cancer

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Craft, Jeffrey; Lozi, Rawan Al; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O.; Bradley, Jeffrey D.; El Naqa, Issam

2011-03-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

The capacity of Aspergillus niger to sense and respond to cell wall stress requires at least three transcription factors: RlmA, MsnA and CrzA.

PubMed

Fiedler, Markus Rm; Lorenz, Annett; Nitsche, Benjamin M; van den Hondel, Cees Amjj; Ram, Arthur Fj; Meyer, Vera

2014-01-01

Cell wall integrity, vesicle transport and protein secretion are key factors contributing to the vitality and productivity of filamentous fungal cell factories such as Aspergillus niger . In order to pioneer rational strain improvement programs, fundamental knowledge on the genetic basis of these processes is required. The aim of the present study was thus to unravel survival strategies of A. niger when challenged with compounds interfering directly or indirectly with its cell wall integrity: calcofluor white, caspofungin, aureobasidin A, FK506 and fenpropimorph. Transcriptomics signatures of A. niger and phenotypic analyses of selected null mutant strains were used to predict regulator proteins mediating the survival responses against these stressors. This integrated approach allowed us to reconstruct a model for the cell wall salvage gene network of A. niger that ensures survival of the fungus upon cell surface stress. The model predicts that (i) caspofungin and aureobasidin A induce the cell wall integrity pathway as a main compensatory response via induction of RhoB and RhoD, respectively, eventually activating the mitogen-activated protein kinase kinase MkkA and the transcription factor RlmA. (ii) RlmA is the main transcription factor required for the protection against calcofluor white but it cooperates with MsnA and CrzA to ensure survival of A. niger when challenged with caspofungin and aureobasidin A. (iii) Membrane stress provoked by aureobasidin A via disturbance of sphingolipid synthesis induces cell wall stress, whereas fenpropimorph-induced disturbance of ergosterol synthesis does not. The present work uncovered a sophisticated defence system of A. niger which employs at least three transcription factors - RlmA, MsnA and CrzA - to protect itself against cell wall stress. The transcriptomic data furthermore predicts a fourth transfactor, SrbA, which seems to be specifically important to survive fenpropimorph-induced cell membrane stress. Future studies will disclose how these regulators are interlocked in different signaling pathways to secure survival of A. niger under different cell wall stress conditions.

STAT3/IRF1 Pathway Activation Sensitizes Cervical Cancer Cells to Chemotherapeutic Drugs.

PubMed

Walch-Rückheim, Barbara; Pahne-Zeppenfeld, Jennifer; Fischbach, Jil; Wickenhauser, Claudia; Horn, Lars Christian; Tharun, Lars; Büttner, Reinhard; Mallmann, Peter; Stern, Peter; Kim, Yoo-Jin; Bohle, Rainer Maria; Rübe, Christian; Ströder, Russalina; Juhasz-Böss, Ingolf; Solomayer, Erich-Franz; Smola, Sigrun

2016-07-01

Neoadjuvant radio/chemotherapy regimens can markedly improve cervical cancer outcome in a subset of patients, while other patients show poor responses, but may encounter severe adverse effects. Thus, there is a strong need for predictive biomarkers to improve clinical management of cervical cancer patients. STAT3 is considered as a critical antiapoptotic factor in various malignancies. We therefore investigated STAT3 activation during cervical carcinogenesis and its impact on the response of cervical cancer cells to chemotherapeutic drugs. Tyr705-phosphorylated STAT3 increased from low-grade cervical intraepithelial neoplasia (CIN1) to precancerous CIN3 lesions. Notably, pTyr705-STAT3 activation significantly declined from CIN3 to invasive cancer, also when compared in the same clinical biopsy. pTyr705-STAT3 was also low or absent in cultured human cervical cancer cell lines, consistent with the in vivo expression data. Unexpectedly, IL6-type cytokine signaling inducing STAT3 activation rendered cervical cancer cells significantly more susceptible to chemotherapeutic drugs, that is, cisplatin or etoposide. This chemosensitization was STAT3-dependent and we identified IFN regulatory factor-1 (IRF1) as the STAT3-inducible mediator required for cell death enhancement. In line with these data, pTyr705-STAT3 significantly correlated with nuclear IRF1 expression in cervical cancer in vivo Importantly, high IRF1 expression in pretreatment cervical cancer biopsy cells was associated with a significantly better response to neoadjuvant radio/chemotherapy of the patients. In summary, our study has identified a key role of the STAT3/IRF1 pathway for chemosensitization in cervical cancer. Our results suggest that pretherapeutic IRF1 expression should be evaluated as a novel predictive biomarker for neoadjuvant radio/chemotherapy responses. Cancer Res; 76(13); 3872-83. ©2016 AACR. ©2016 American Association for Cancer Research.

Refining the treatment of advanced nonsmall cell lung cancer

PubMed Central

Ogita, Shin; Wozniak, Antoinette J

2010-01-01

Metastatic nonsmall cell lung cancer (NSCLC) is a debilitating and deadly disease with virtually no chance for long-term survival. Chemotherapy has improved both survival and quality of life for patients with advanced disease. Overall survival of patients with metastatic NSCLC has gradually increased from 8 to 12 months over the past three decades with the introduction of new chemotherapeutic drugs and agents directed at novel targets in the cancer cell. Epidermal growth factor receptor and vascular endothelial growth factor are two such targets. Recent developments also include treatment based on histology and the use of maintenance therapy. It has been recognized that lung cancer is a very complex disease. It is common practice to include a number of scientific correlative studies in the design of clinical trials in order to determine predictive markers of benefit from treatment. This article will review the current approach to the treatment of advanced NSCLC including the use of chemotherapy and molecularly targeted agents. Future directions including the use of potentially predictive biomarkers and innovative clinical trials aimed at a more individualized approach to treatment will also be discussed. PMID:28210103

Comparison of the Sanofi Diagnostics Pasteur Chlamydia Microplate EIA shortened assay with the original standard assay and cell culture.

PubMed

Chan, E L; Brandt, K; Stoneham, H; Horsman, G

1997-08-01

The new Sanofi Diagnostics Pasteur Chlamydia Microplate EIA shortened assay was evaluated by comparison with the original standard assay and cell culture. A total of 853 paired male and female genital tract specimens was tested with both Sanofi Chlamydia Microplate EIA shortened and standard assays and the results were compared with those of cell culture. For confirmation, a blocking assay run in the shortened format was used. Discrepancies between the three methods were resolved by a direct fluorescent antibody (DFA) test on the EIA samples or the culture retentate, or both. After resolution of discrepant results, the standard assay had a sensitivity, specificity, positive predictive value and negative predictive value of 98.5%, 100%, 100% and 99.9%, respectively. The shortened assay results were 100%, 100%, 100% and 100%, respectively. The shortened assay takes approximately 1.5 h less time than the standard assay and this study demonstrated that they have equivalent sensitivity and specificity. The improvement in turnaround time enables results to be reported on the same day.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

PubMed Central

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

2017-01-01

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Farmann, Alexander; Sauer, Dirk Uwe

2016-10-01

This study provides an overview of available techniques for on-board State-of-Available-Power (SoAP) prediction of lithium-ion batteries (LIBs) in electric vehicles. Different approaches dealing with the on-board estimation of battery State-of-Charge (SoC) or State-of-Health (SoH) have been extensively discussed in various researches in the past. However, the topic of SoAP prediction has not been explored comprehensively yet. The prediction of the maximum power that can be applied to the battery by discharging or charging it during acceleration, regenerative braking and gradient climbing is definitely one of the most challenging tasks of battery management systems. In large lithium-ion battery packs because of many factors, such as temperature distribution, cell-to-cell deviations regarding the actual battery impedance or capacity either in initial or aged state, the use of efficient and reliable methods for battery state estimation is required. The available battery power is limited by the safe operating area (SOA), where SOA is defined by battery temperature, current, voltage and SoC. Accurate SoAP prediction allows the energy management system to regulate the power flow of the vehicle more precisely and optimize battery performance and improve its lifetime accordingly. To this end, scientific and technical literature sources are studied and available approaches are reviewed.

Treatment-Related Predictive and Prognostic Factors in Trimodality Approach in Stage IIIA/N2 Non-Small Cell Lung Cancer.

PubMed

Jeremić, Branislav; Casas, Francesc; Dubinsky, Pavol; Gomez-Caamano, Antonio; Čihorić, Nikola; Videtic, Gregory; Igrutinovic, Ivan

2018-01-01

While there are no established pretreatment predictive and prognostic factors in patients with stage IIIA/pN2 non-small cell lung cancer (NSCLC) indicating a benefit to surgery as a part of trimodality approach, little is known about treatment-related predictive and prognostic factors in this setting. A literature search was conducted to identify possible treatment-related predictive and prognostic factors for patients for whom trimodality approach was reported on. Overall survival was the primary endpoint of this study. Of 30 identified studies, there were two phase II studies, 5 "prospective" studies, and 23 retrospective studies. No study was found which specifically looked at treatment-related predictive factors of improved outcomes in trimodality treatment. Of potential treatment-related prognostic factors, the least frequently analyzed factors among 30 available studies were overall pathologic stage after preoperative treatment and UICC downstaging. Evaluation of treatment response before surgery and by pathologic tumor stage after induction therapy were analyzed in slightly more than 40% of studies and found not to influence survival. More frequently studied factors-resection status, degree of tumor regression, and pathologic nodal stage after induction therapy as well as the most frequently studied factor, the treatment (in almost 75% studies)-showed no discernible impact on survival, due to conflicting results. Currently, it is impossible to identify any treatment-related predictive or prognostic factors for selecting surgery in the treatment of patients with stage IIIA/pN2 NSCLC.

Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images

PubMed Central

Bagci, Ulas; Yao, Jianhua; Miller-Jaster, Kirsten; Chen, Xinjian; Mollura, Daniel J.

2013-01-01

We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on 18F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 18F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUVmax (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUVmax. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16). PMID:23431398

Frequency Reuse, Cell Separation, and Capacity Analysis of VHF Digital Link Mode 3 TDMA

NASA Technical Reports Server (NTRS)

Shamma, Mohammed A.; Nguyen, Thanh C.; Apaza, Rafael D.

2003-01-01

The most recent studies by the Federal Aviation Administration (FAA) and the aviation industry have indicated that it has become increasingly difficult to make new VHF frequency or channel assignments to meet the aviation needs for air-ground communications. FAA has planned for several aggressive improvement measures to the existing systems, but these measures would not meet the projected voice communications needs beyond 2009. FAA found that since 1974 there has been, on the average, a 4 percent annual increase in the number of channel assignments needed to satisfy the air-ground communication traffic (approximately 300 new channel assignments per year). With the planned improvement measures, the channel assignments are expected to reach a maximum number of 16615 channels by about 2010. Hence, the FAA proposed the use of VDL Mode 3 as a new integrated digital voice and data communications systems to meet the future air traffic demand. This paper presents analytical results of frequency reuse; cell separation and capacity estimation of VDL Mode 3 TDMA systems that FAA has planned to implement the future VHF air-ground communications system by the year 2010. For TDMA, it is well understood that the frequency reuse factor is a crucial parameter for capacity estimation. Formulation of this frequency reuse factor is shown, taking into account the limitation imposed by the requirement to have a sufficient Signal to Co-Channel Interference Ratio. Several different values for the Signal to Co-Channel Interference Ratio were utilized corresponding to the current analog VHF DSB-AM systems, and the future digital VDL Mode 3. The required separation of Co-Channel cells is computed for most of the Frequency Protected Service Volumes (FPSV's) currently in use by the FAA. Additionally, the ideal cell capacity for each FPSV is presented. Also, using actual traffic for the Detroit air space, a FPSV traffic distribution model is used to generate a typical cell for channel capacity prediction. Such prediction is useful for evaluating the improvement of future VDL Mode 3 deployment and capacity planning.

An implicit scheme with memory reduction technique for steady state solutions of DVBE in all flow regimes

NASA Astrophysics Data System (ADS)

Yang, L. M.; Shu, C.; Yang, W. M.; Wu, J.

2018-04-01

High consumption of memory and computational effort is the major barrier to prevent the widespread use of the discrete velocity method (DVM) in the simulation of flows in all flow regimes. To overcome this drawback, an implicit DVM with a memory reduction technique for solving a steady discrete velocity Boltzmann equation (DVBE) is presented in this work. In the method, the distribution functions in the whole discrete velocity space do not need to be stored, and they are calculated from the macroscopic flow variables. As a result, its memory requirement is in the same order as the conventional Euler/Navier-Stokes solver. In the meantime, it is more efficient than the explicit DVM for the simulation of various flows. To make the method efficient for solving flow problems in all flow regimes, a prediction step is introduced to estimate the local equilibrium state of the DVBE. In the prediction step, the distribution function at the cell interface is calculated by the local solution of DVBE. For the flow simulation, when the cell size is less than the mean free path, the prediction step has almost no effect on the solution. However, when the cell size is much larger than the mean free path, the prediction step dominates the solution so as to provide reasonable results in such a flow regime. In addition, to further improve the computational efficiency of the developed scheme in the continuum flow regime, the implicit technique is also introduced into the prediction step. Numerical results showed that the proposed implicit scheme can provide reasonable results in all flow regimes and increase significantly the computational efficiency in the continuum flow regime as compared with the existing DVM solvers.

Higher Donor Apheresis Blood Volumes Are Associated with Reduced Relapse Risk and Improved Survival in Reduced-Intensity Allogeneic Transplantations with Unrelated Donors.

PubMed

Crisalli, Lisa M; Hinkle, Joanne T; Walling, Christopher C; Sell, Mary; Frey, Noelle V; Hexner, Elizabeth O; Loren, Alison W; Luger, Selina M; Stadtmauer, Edward A; Porter, David L; Reshef, Ran

2018-06-01

Allogeneic hematopoietic stem cell transplantation (HSCT) with reduced-intensity conditioning (RIC) offers a curative option for patients with hematologic malignancies who are unable to undergo myeloablative conditioning, but its success is limited by high rates of relapse. Several studies have suggested a role for T cell doses in peripheral blood stem cell grafts in RIC HSCT. Because T cell dose is typically not known until after the collection, and apheresis blood volume is easily modifiable, we hypothesized that higher donor apheresis blood volumes would improve transplantation outcomes through an effect on graft composition. Thus, we analyzed the relationships between apheresis volume, graft composition, and transplantation outcomes in 142 consecutive patients undergoing unrelated donor allogeneic RIC HSCT. We found that apheresis volume ≥15 L was associated with a significantly decreased risk of relapse (adjusted hazard ratio [aHR], .48; 95% confidence interval [CI], .28 to .84]; P = .01) and improved relapse-free survival (aHR, .56; 95% CI, .35 to .89; P = .02) and overall survival (aHR, .55; 95% CI, .34 to .91; P = .02). A high apheresis volume was not associated with increased rates of acute or chronic graft-versus-host disease. These results demonstrate that an apheresis volume of at least 15 L is independently predictive of improved transplantation outcomes after RIC allogeneic HSCT. Copyright © 2018 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Versatile three-dimensional virus-based template for dye-sensitized solar cells with improved electron transport and light harvesting.

PubMed

Chen, Po-Yen; Dang, Xiangnan; Klug, Matthew T; Qi, Jifa; Dorval Courchesne, Noémie-Manuelle; Burpo, Fred J; Fang, Nicholas; Hammond, Paula T; Belcher, Angela M

2013-08-27

By genetically encoding affinity for inorganic materials into the capsid proteins of the M13 bacteriophage, the virus can act as a template for the synthesis of nanomaterial composites for use in various device applications. Herein, the M13 bacteriophage is employed to build a multifunctional and three-dimensional scaffold capable of improving both electron collection and light harvesting in dye-sensitized solar cells (DSSCs). This has been accomplished by binding gold nanoparticles (AuNPs) to the virus proteins and encapsulating the AuNP-virus complexes in TiO2 to produce a plasmon-enhanced and nanowire (NW)-based photoanode. The NW morphology exhibits an improved electron diffusion length compared to traditional nanoparticle-based DSSCs, and the AuNPs increase the light absorption of the dye-molecules through the phenomenon of localized surface plasmon resonance. Consequently, we report a virus-templated and plasmon-enhanced DSSC with an efficiency of 8.46%, which is achieved through optimizing both the NW morphology and the concentration of AuNPs loaded into the solar cells. In addition, we propose a theoretical model that predicts the experimentally observed trends of plasmon enhancement.

An update on the recent literature on sickle cell bone disease.

PubMed

Osunkwo, Ifeyinwa

2013-12-01

To summarize the findings of the recent publications on sickle cell bone disease (SBD). Individuals with sickle cell disease (SCD) are living longer and develop progressive organ damage including SBD with age. Recent studies suggest alternative radiological diagnostics such as ultrasound and scintigraphy can detect and differentiate between different forms of SBD. MRI with or without diffusion-weighted sequences remains the gold standard. Case reports of cranio-orofacial SBD highlight the rarity of this presentation. Vitamin D deficiency is highly prevalent at all ages, but may not be an independent risk factor for avascular necrosis (AVN). Gene polymorphisms of the Annexin A gene may predict AVN in SCD. A recent study demonstrated reduced days with pain and improved physical activity quality of life following high-dose vitamin D therapy. The high rates of osteopenia and osteoporosis in SCD support the need for research addressing this rising public health problem. Lastly, results of total hip arthroplasty for AVN in SCD has improved significantly over time with the use of cementless prosthetic material and improved supportive care. SBD remains poorly studied. Prospective randomized studies targeting predictors, diagnostics, prevention, and treatment options for SBD are sorely needed.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

Liquid biopsy in liver cancer.

PubMed

Labgaa, Ismail; Villanueva, Augusto

2015-04-01

Liver cancer has become the second cause of cancer-related death worldwide. Most patients are still diagnosed at intermediate or advanced stage, where potentially curative treatment options are not recommended. Unlike other solid tumors, there are no validated oncogenic addiction loops and the only systemic agent to improve survival in advanced disease is sorafenib. All phase 3 clinical trials testing molecular therapies after sorafenib have been negative, none of which selected patients based on predictive biomarkers of response. Theoretically, analysis of circulating cancer byproducts (e.g., circulating tumor cells, cell-free nucleic acids), namely "liquid biopsy," could provide easy access to molecular tumor information, improve patients' stratification and allow to assess tumor dynamics over time. Recent technical developments and preliminary data from other malignancies indicate that liquid biopsy might have a role in the future management of cancer patients.

Establishment of a structure-activity relationship of 1H-imidazo[4,5-c]quinoline-based kinase inhibitor NVP-BEZ235 as a lead for African sleeping sickness.

PubMed

Seixas, João D; Luengo-Arratta, Sandra A; Diaz, Rosario; Saldivia, Manuel; Rojas-Barros, Domingo I; Manzano, Pilar; Gonzalez, Silvia; Berlanga, Manuela; Smith, Terry K; Navarro, Miguel; Pollastri, Michael P

2014-06-12

Compound NVP-BEZ235 (1) is a potent inhibitor of human phospoinositide-3-kinases and mammalian target of rapamycin (mTOR) that also showed high inhibitory potency against Trypanosoma brucei cultures. With an eye toward using 1 as a starting point for anti-trypanosomal drug discovery, we report efforts to reduce host cell toxicity, to improve the physicochemical properties, and to improve the selectivity profile over human kinases. In this work, we have developed structure-activity relationships for analogues of 1 and have prepared analogues of 1 with improved solubility properties and good predicted central nervous system exposure. In this way, we have identified 4e, 9, 16e, and 16g as the most promising leads to date. We also report cell phenotype and phospholipidomic studies that suggest that these compounds exert their anti-trypanosomal effects, at least in part, by inhibition of lipid kinases.

Cell-free placental mRNA in maternal plasma to predict placental invasion in patients with placenta accreta.

PubMed

El Behery, Manal M; Rasha L, Etewa; El Alfy, Yehya

2010-04-01

To evaluate whether measuring cell-free placental mRNA in maternal plasma improves the diagnostic accuracy of ultrasound and color Doppler in detecting placental invasion in patients at risk for placenta accreta. Thirty-five singleton pregnant women of more than 28 weeks of gestation and at risk for placenta accreta underwent ultrasound and color Doppler assessment. Cell-free placental mRNA in maternal plasma was measured using real-time reverse-transcription polymerase chain reaction. Patients were classified into 2 groups based on the findings at cesarean delivery and histological examination: women with placenta accreta (n=7) and women without placenta accreta (n=28). The median MoM (multiples of the median) value of cell-free placental mRNA was significantly higher in patients with placenta accreta than in those without placenta accreta (6.50 vs 2.60; P<0.001. Moreover, cell-free placental mRNA was significantly elevated in patients with placenta increta and percreta than in those with simple accreta. Six false-positive results were found on ultrasound, all from patients without placenta accreta and an insignificant rise in cell-free placental mRNA levels. Measuring cell-free placental mRNA in maternal plasma may increase the accuracy of ultrasound and color Doppler in prenatal prediction of placental invasion in patients with suspected placenta accreta. Copyright 2009 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Combined panel of serum human tissue kallikreins and CA-125 for the detection of epithelial ovarian cancer.

PubMed

Koh, Stephen Chee Liang; Huak, Chan Yiong; Lutan, Delfi; Marpuang, Johny; Ketut, Suwiyoga; Budiana, Nyoma Gede; Saleh, Agustria Zainu; Aziz, Mohamad Farid; Winarto, Hariyono; Pradjatmo, Heru; Hoan, Nguyen Khac Han; Thanh, Pham Viet; Choolani, Mahesh

2012-07-01

To determine the predictive accuracy of the combined panels of serum human tissue kallikreins (hKs) and CA-125 for the detection of epithelial ovarian cancer. Serum specimens collected from 5 Indonesian centers and 1 Vietnamese center were analyzed for CA-125, hK6, and hK10 levels. A total of 375 specimens from patients presenting with ovarian tumors, which include 156 benign cysts, 172 epithelial ovarian cancers (stage I/II, n=72; stage III/IV, n=100), 36 germ cell tumors and 11 borderline tumors, were included in the study analysis. Receiver operating characteristic analysis were performed to determine the cutoffs for age, CA-125, hK6, and hK10. Sensitivity, specificity, negative, and positive predictive values were determined for various combinations of the biomarkers. The levels of hK6 and hK10 were significantly elevated in ovarian cancer cases compared to benign cysts. Combination of 3 markers, age/CA-125/hk6 or CA-125/hk6/hk10, showed improved specificity (100%) and positive predictive value (100%) for prediction of ovarian cancer, when compared to the performance of single markers having 80-92% specificity and 74-87% positive predictive value. Four-marker combination, age/CA-125/hK6/hK10 also showed 100% specificity and 100% positive predictive value, although it demonstrated low sensitivity (11.9%) and negative predictive value (52.8%). The combination of human tissue kallikreins and CA-125 showed potential for improving prediction of epithelial ovarian cancer in patients presenting with ovarian tumors.

A mathematical model and computational framework for three-dimensional chondrocyte cell growth in a porous tissue scaffold placed inside a bi-directional flow perfusion bioreactor.

PubMed

Shakhawath Hossain, Md; Bergstrom, D J; Chen, X B

2015-12-01

The in vitro chondrocyte cell culture for cartilage tissue regeneration in a perfusion bioreactor is a complex process. Mathematical modeling and computational simulation can provide important insights into the culture process, which would be helpful for selecting culture conditions to improve the quality of the developed tissue constructs. However, simulation of the cell culture process is a challenging task due to the complicated interaction between the cells and local fluid flow and nutrient transport inside the complex porous scaffolds. In this study, a mathematical model and computational framework has been developed to simulate the three-dimensional (3D) cell growth in a porous scaffold placed inside a bi-directional flow perfusion bioreactor. The model was developed by taking into account the two-way coupling between the cell growth and local flow field and associated glucose concentration, and then used to perform a resolved-scale simulation based on the lattice Boltzmann method (LBM). The simulation predicts the local shear stress, glucose concentration, and 3D cell growth inside the porous scaffold for a period of 30 days of cell culture. The predicted cell growth rate was in good overall agreement with the experimental results available in the literature. This study demonstrates that the bi-directional flow perfusion culture system can enhance the homogeneity of the cell growth inside the scaffold. The model and computational framework developed is capable of providing significant insight into the culture process, thus providing a powerful tool for the design and optimization of the cell culture process. © 2015 Wiley Periodicals, Inc.

Prenatal tolerance induction: relationship between cell dose, marrow T-cells, chimerism, and tolerance.

PubMed

Chen, Jeng-Chang; Chang, Ming-Ling; Huang, Shiu-Feng; Chang, Pei-Yeh; Muench, Marcus O; Fu, Ren-Huei; Ou, Liang-Shiou; Kuo, Ming-Ling

2008-01-01

It was reported that the dose of self-antigens can determine the consequence of deletional tolerance and donor T cells are critical for tolerance induction in mixed chimeras. This study aimed at assessing the effect of cell doses and marrow T cells on engraftment and tolerance induction after prenatal bone marrow transplantation. Intraperitoneal cell transplantation was performed in FVB/N (H-2K(q)) mice at gestational day 14 with escalating doses of adult C57BL/6 (H-2K(b)) marrows. Peripheral chimerism was examined postnatally by flow cytometry and tolerance was tested by skin transplantation. Transplantation of light-density marrow cells showed a dose response. High-level chimerism emerged with a threshold dose of 5.0 x 10(6) and host leukocytes could be nearly replaced at a dose of 7.5-10.0 x 10(6). High-dose transplants conferred a steady long-lasting donor-specific tolerance but were accompanied by >50% incidence of graft-versus-host disease. Depletion of marrow T cells lessened graft-versus-host disease to the detriment of engraftment. With low-level chimerism, tolerance was a graded phenomenon dependent upon the level of chimerism. Durable chimerism within 6 months required a threshold of > or = 2% chimerism at 1 month of age and predicted a 50% chance of long-term tolerance, whereas transient chimerism (<2%) only caused hyporesponsiveness to the donor. Tolerance induction did not succeed without peripheral chimerism even if a large amount of injected donor cells persisted in the peritoneum. Neither did an increase in cell doses or donor T-cell contents benefit skin graft survivals unless it had substantially improved peripheral chimerism. Thus, peripheral chimerism level can be a simple and straightforward test to predict the degree of prenatal immune tolerance.

Critical biological parameters modulate affinity as a determinant of function in T-cell receptor gene-modified T-cells.

PubMed

Spear, Timothy T; Wang, Yuan; Foley, Kendra C; Murray, David C; Scurti, Gina M; Simms, Patricia E; Garrett-Mayer, Elizabeth; Hellman, Lance M; Baker, Brian M; Nishimura, Michael I

2017-11-01

T-cell receptor (TCR)-pMHC affinity has been generally accepted to be the most important factor dictating antigen recognition in gene-modified T-cells. As such, there is great interest in optimizing TCR-based immunotherapies by enhancing TCR affinity to augment the therapeutic benefit of TCR gene-modified T-cells in cancer patients. However, recent clinical trials using affinity-enhanced TCRs in adoptive cell transfer (ACT) have observed unintended and serious adverse events, including death, attributed to unpredicted off-tumor or off-target cross-reactivity. It is critical to re-evaluate the importance of other biophysical, structural, or cellular factors that drive the reactivity of TCR gene-modified T-cells. Using a model for altered antigen recognition, we determined how TCR-pMHC affinity influenced the reactivity of hepatitis C virus (HCV) TCR gene-modified T-cells against a panel of naturally occurring HCV peptides and HCV-expressing tumor targets. The impact of other factors, such as TCR-pMHC stabilization and signaling contributions by the CD8 co-receptor, as well as antigen and TCR density were also evaluated. We found that changes in TCR-pMHC affinity did not always predict or dictate IFNγ release or degranulation by TCR gene-modified T-cells, suggesting that less emphasis might need to be placed on TCR-pMHC affinity as a means of predicting or augmenting the therapeutic potential of TCR gene-modified T-cells used in ACT. A more complete understanding of antigen recognition by gene-modified T-cells and a more rational approach to improve the design and implementation of novel TCR-based immunotherapies is necessary to enhance efficacy and maximize safety in patients.

Improving in vitro to in vivo extrapolation by incorporating toxicokinetic measurements: A case study of lindane-induced neurotoxicity

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

Croom, Edward L.; Shafer, Timothy J.; Evans, Marina V.

Approaches for extrapolating in vitro toxicity testing results for prediction of human in vivo outcomes are needed. The purpose of this case study was to employ in vitro toxicokinetics and PBPK modeling to perform in vitro to in vivo extrapolation (IVIVE) of lindane neurotoxicity. Lindane cell and media concentrations in vitro, together with in vitro concentration-response data for lindane effects on neuronal network firing rates, were compared to in vivo data and model simulations as an exercise in extrapolation for chemical-induced neurotoxicity in rodents and humans. Time- and concentration-dependent lindane dosimetry was determined in primary cultures of rat cortical neuronsmore » in vitro using “faux” (without electrodes) microelectrode arrays (MEAs). In vivo data were derived from literature values, and physiologically based pharmacokinetic (PBPK) modeling was used to extrapolate from rat to human. The previously determined EC{sub 50} for increased firing rates in primary cultures of cortical neurons was 0.6 μg/ml. Media and cell lindane concentrations at the EC{sub 50} were 0.4 μg/ml and 7.1 μg/ml, respectively, and cellular lindane accumulation was time- and concentration-dependent. Rat blood and brain lindane levels during seizures were 1.7–1.9 μg/ml and 5–11 μg/ml, respectively. Brain lindane levels associated with seizures in rats and those predicted for humans (average = 7 μg/ml) by PBPK modeling were very similar to in vitro concentrations detected in cortical cells at the EC{sub 50} dose. PBPK model predictions matched literature data and timing. These findings indicate that in vitro MEA results are predictive of in vivo responses to lindane and demonstrate a successful modeling approach for IVIVE of rat and human neurotoxicity. - Highlights: • In vitro to in vivo extrapolation for lindane neurotoxicity was performed. • Dosimetry of lindane in a micro-electrode array (MEA) test system was assessed. • Cell concentrations at the MEA EC{sub 50} equaled rat brain levels associated with seizure. • PBPK-predicted human brain levels at seizure also equaled EC{sub 50} cell concentrations. • In vitro MEA results are predictive of lindane in vivo dose–response in rats/humans.« less

Empirical Methods for Detecting Regional Trends and Other Spatial Expressions in Antrim Shale Gas Productivity, with Implications for Improving Resource Projections Using Local Nonparametric Estimation Techniques

USGS Publications Warehouse

Coburn, T.C.; Freeman, P.A.; Attanasi, E.D.

2012-01-01

The primary objectives of this research were to (1) investigate empirical methods for establishing regional trends in unconventional gas resources as exhibited by historical production data and (2) determine whether or not incorporating additional knowledge of a regional trend in a suite of previously established local nonparametric resource prediction algorithms influences assessment results. Three different trend detection methods were applied to publicly available production data (well EUR aggregated to 80-acre cells) from the Devonian Antrim Shale gas play in the Michigan Basin. This effort led to the identification of a southeast-northwest trend in cell EUR values across the play that, in a very general sense, conforms to the primary fracture and structural orientations of the province. However, including this trend in the resource prediction algorithms did not lead to improved results. Further analysis indicated the existence of clustering among cell EUR values that likely dampens the contribution of the regional trend. The reason for the clustering, a somewhat unexpected result, is not completely understood, although the geological literature provides some possible explanations. With appropriate data, a better understanding of this clustering phenomenon may lead to important information about the factors and their interactions that control Antrim Shale gas production, which may, in turn, help establish a more general protocol for better estimating resources in this and other shale gas plays. ?? 2011 International Association for Mathematical Geology (outside the USA).

Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. Copyright © 2012 Elsevier Ltd. All rights reserved.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

PubMed

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

PubMed

Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

2013-01-01

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

Toward Improving Predictability of Extreme Hydrometeorological Events: the Use of Multi-scale Climate Modeling in the Northern High Plains

NASA Astrophysics Data System (ADS)

Munoz-Arriola, F.; Torres-Alavez, J.; Mohamad Abadi, A.; Walko, R. L.

2014-12-01

Our goal is to investigate possible sources of predictability of hydrometeorological extreme events in the Northern High Plains. Hydrometeorological extreme events are considered the most costly natural phenomena. Water deficits and surpluses highlight how the water-climate interdependence becomes crucial in areas where single activities drive economies such as Agriculture in the NHP. Nonetheless we recognize the Water-Climate interdependence and the regulatory role that human activities play, we still grapple to identify what sources of predictability could be added to flood and drought forecasts. To identify the benefit of multi-scale climate modeling and the role of initial conditions on flood and drought predictability on the NHP, we use the Ocean Land Atmospheric Model (OLAM). OLAM is characterized by a dynamic core with a global geodesic grid with hexagonal (and variably refined) mesh cells and a finite volume discretization of the full compressible Navier Stokes equations, a cut-grid cell method for topography (that reduces error in computational gradient computation and anomalous vertical dispersion). Our hypothesis is that wet conditions will drive OLAM's simulations of precipitation to wetter conditions affecting both flood forecast and drought forecast. To test this hypothesis we simulate precipitation during identified historical flood events followed by drought events in the NHP (i.e. 2011-2012 years). We initialized OLAM with CFS-data 1-10 days previous to a flooding event (as initial conditions) to explore (1) short-term and high-resolution and (2) long-term and coarse-resolution simulations of flood and drought events, respectively. While floods are assessed during a maximum of 15-days refined-mesh simulations, drought is evaluated during the following 15 months. Simulated precipitation will be compared with the Sub-continental Observation Dataset, a gridded 1/16th degree resolution data obtained from climatological stations in Canada, US, and Mexico. This in-progress research will ultimately contribute to integrate OLAM and VIC models and improve predictability of extreme hydrometeorological events.

Optical Physics of Cu(In,Ga)Se2 Solar Cells and Their Layer Components

NASA Astrophysics Data System (ADS)

Ibdah, Abedl-Rahman

Polycrystalline Cu(In1-xGax)Se 2 (CIGS) thin film technology has emerged as a promising candidate for low cost and high performance solar modules. The efficiency of CIGS solar cells is strongly influenced by several key factors. Among these factors include Ga composition and its profile in the absorber layer, copper content in this layer, and the solar cell multilayer structure. As a result, tools for the characterization of thin film CIGS solar cells and their layer components are becoming increasingly essential in research and manufacturing. Spectroscopic ellipsometry is a non-invasive technique that can serve as an accurate probe of component layer optical properties and multilayer structures, and can be applied as a diagnostic tool for real-time, in-line, and off-line monitoring and analysis in small area solar cell fabrication as well as in large area photovoltaics manufacturing. Implementation of spectroscopic ellipsometry provides unique insights into the properties of complete solar cell multilayer structures and their layer components. These insights can improve our understanding of solar cell structures, overcome challenges associated with solar cell fabrication, and assist in process monitoring and control on a production line. In this dissertation research, Cu(In,Ga)Se2 films with different Cu contents have been prepared by the one stage co-evaporation process. These films have been studied by real time spectroscopic ellipsometry (RTSE) during deposition, and by in-situ SE at the deposition temperature as well as at room temperature to extract the dielectric functions (epsilon1, epsilon 2) of the thin film materials. Analytical expressions for the room temperature dielectric functions were developed, and the free parameters that describe these analytical functions were in turn expressed as functions of the Cu content. As a result of this parameterization, the dielectric function spectra (epsilon 1, epsilon2) can be predicted for any desired composition within the range of the samples investigated. This capability was applied for mapping the structural and compositional variations of CIGS thin films deposited over a 10 cm x 10 cm substrate area. In another application presented in this dissertation, a non-invasive method utilizing ex-situ spectroscopic ellipsometry analysis has been developed and applied to determine non-destructively the Ga compositional profile in CIGS absorbers. The method employs parameterized dielectric function spectra (epsilon1, epsilon2) of CIGS versus Ga content to probe the compositional variation with depth into the absorber. In addition, a methodology for prediction of the external quantum efficiency (QE) including optical gains and losses for a CIGS solar cell has been developed. The methodology utilizes ex-situ spectroscopic ellipsometry analysis of a complete solar cell, with no free parameters, to deduce the multilayer solar cell structure non-invasively and simulate optical light absorption in each of the layer components. In the case of high efficiency CIGS solar cells, with minimal electronic losses, QE spectra are predicted from the sum of optical absorption in the active layer components. For such solar cells with ideal photo-generated charge carrier collection, the SE-predicted QE spectra are excellent representation of the measured ones. Since the QE spectra as well as the short circuit current density (Jsc) can be calculated directly from SE analysis results, then the predicted QE from SE can be compared with the experimental QE to evaluate electronic losses based on the difference between the spectra. Moreover, the calculated Jsc can be used as a key parameter for the design and optimization of anti-reflection coating structures. Because the long term production potential of CIGS solar modules may be limited by the availability of indium, it becomes important to reduce the thickness of the CIGS absorber layer. Thickness reduction would reduce the quantity of indium required for production which would in turn reduce costs. A decrease in short-circuit current density (Jsc) is expected, however, upon thinning the CIGS absorber due to incomplete absorption. To clarify the limits of obtainable Jsc in ultra-thin CIGS solar cells with Mo back contacts, optical properties and multilayer structural data are deduced via spectroscopic ellipsometry analysis and used to predict the QE spectra and maximum obtainable Jsc values upon thinning the absorber. Moreover, SE-guided optical design of ultra-thin CIGS solar cells has been demonstrated. In the case of solar cells fabricated on Mo, thinning the absorber in a CIGS solar cell is associated with significant optical losses in the Mo containing back contact layers. This is due in part to the poor optical reflectance of Mo. Such optical losses may be reduced by employing a back contact design with improved reflectance. Thus, alternative novel solar cell structures with ultra-thin absorbers and improved back contact reflectance have been designed and investigated using SE and the optical modeling methods. In addition to optical losses, electronic losses in the ultra-thin solar cells have been evaluated. By separating the absorber layer into sub-layer regions (for example, near-junction, bulk, and near-back-contact) and varying carrier collection probability in these regions, the contribution of each region to the current can be estimated. Based on this separation, the origin of the electronic losses has been identified as near the back contact.

Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

PubMed

Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

2017-07-11

Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

Numerical prediction of algae cell mixing feature in raceway ponds using particle tracing methods.

PubMed

Ali, Haider; Cheema, Taqi A; Yoon, Ho-Sung; Do, Younghae; Park, Cheol W

2015-02-01

In the present study, a novel technique, which involves numerical computation of the mixing length of algae particles in raceway ponds, was used to evaluate the mixing process. A value of mixing length that is higher than the maximum streamwise distance (MSD) of algae cells indicates that the cells experienced an adequate turbulent mixing in the pond. A coupling methodology was adapted to map the pulsating effects of a 2D paddle wheel on a 3D raceway pond in this study. The turbulent mixing was examined based on the computations of mixing length, residence time, and algae cell distribution in the pond. The results revealed that the use of particle tracing methodology is an improved approach to define the mixing phenomenon more effectively. Moreover, the algae cell distribution aided in identifying the degree of mixing in terms of mixing length and residence time. © 2014 Wiley Periodicals, Inc.

Dense and Sparse Matrix Operations on the Cell Processor

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

Williams, Samuel W.; Shalf, John; Oliker, Leonid

2005-05-01

The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. Therefore, the high performance computing community is examining alternative architectures that address the limitations of modern superscalar designs. In this work, we examine STI's forthcoming Cell processor: a novel, low-power architecture that combines a PowerPC core with eight independent SIMD processing units coupled with a software-controlled memory to offer high FLOP/s/Watt. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop an analytic framework to predict Cell performance on dense and sparse matrix operations, usingmore » a variety of algorithmic approaches. Results demonstrate Cell's potential to deliver more than an order of magnitude better GFLOP/s per watt performance, when compared with the Intel Itanium2 and Cray X1 processors.« less

Mechanical regulation of chondrogenesis

PubMed Central

2013-01-01

Mechanical factors play a crucial role in the development of articular cartilage in vivo. In this regard, tissue engineers have sought to leverage native mechanotransduction pathways to enhance in vitro stem cell-based cartilage repair strategies. However, a thorough understanding of how individual mechanical factors influence stem cell fate is needed to predictably and effectively utilize this strategy of mechanically-induced chondrogenesis. This article summarizes some of the latest findings on mechanically stimulated chondrogenesis, highlighting several new areas of interest, such as the effects of mechanical stimulation on matrix maintenance and terminal differentiation, as well as the use of multifactorial bioreactors. Additionally, the roles of individual biophysical factors, such as hydrostatic or osmotic pressure, are examined in light of their potential to induce mesenchymal stem cell chondrogenesis. An improved understanding of biomechanically-driven tissue development and maturation of stem cell-based cartilage replacements will hopefully lead to the development of cell-based therapies for cartilage degeneration and disease. PMID:23809493

Modeling the effect of boost timing in murine irradiated sporozoite prime-boost vaccines

PubMed Central

Zhang, Min; Herrero, Miguel A.; Acosta, Francisco J.; Tsuji, Moriya

2018-01-01

Vaccination with radiation-attenuated sporozoites has been shown to induce CD8+ T cell-mediated protection against pre-erythrocytic stages of malaria. Empirical evidence suggests that successive inoculations often improve the efficacy of this type of vaccines. An initial dose (prime) triggers a specific cellular response, and subsequent inoculations (boost) amplify this response to create a robust CD8+ T cell memory. In this work we propose a model to analyze the effect of T cell dynamics on the performance of prime-boost vaccines. This model suggests that boost doses and timings should be selected according to the T cell response elicited by priming. Specifically, boosting during late stages of clonal contraction would maximize T cell memory production for vaccines using lower doses of irradiated sporozoites. In contrast, single-dose inoculations would be indicated for higher vaccine doses. Experimental data have been obtained that support theoretical predictions of the model. PMID:29329308

Optoelectronic engineering of colloidal quantum-dot solar cells beyond the efficiency black hole: a modeling approach

NASA Astrophysics Data System (ADS)

Mahpeykar, Seyed Milad; Wang, Xihua

2017-02-01

Colloidal quantum dot (CQD) solar cells have been under the spotlight in recent years mainly due to their potential for low-cost solution-processed fabrication and efficient light harvesting through multiple exciton generation (MEG) and tunable absorption spectrum via the quantum size effect. Despite the impressive advances achieved in charge carrier mobility of quantum dot solids and the cells' light trapping capabilities, the recent progress in CQD solar cell efficiencies has been slow, leaving them behind other competing solar cell technologies. In this work, using comprehensive optoelectronic modeling and simulation, we demonstrate the presence of a strong efficiency loss mechanism, here called the "efficiency black hole", that can significantly hold back the improvements achieved by any efficiency enhancement strategy. We prove that this efficiency black hole is the result of sole focus on enhancement of either light absorption or charge extraction capabilities of CQD solar cells. This means that for a given thickness of CQD layer, improvements accomplished exclusively in optic or electronic aspect of CQD solar cells do not necessarily translate into tangible enhancement in their efficiency. The results suggest that in order for CQD solar cells to come out of the mentioned black hole, incorporation of an effective light trapping strategy and a high quality CQD film at the same time is an essential necessity. Using the developed optoelectronic model, the requirements for this incorporation approach and the expected efficiencies after its implementation are predicted as a roadmap for CQD solar cell research community.

Particle Trajectories in Rotating Wall Cell Culture Devices

NASA Technical Reports Server (NTRS)

Ramachandran N.; Downey, J. P.

1999-01-01

Cell cultures are extremely important to the medical community since such cultures provide an opportunity to perform research on human tissue without the concerns inherent in experiments on individual humans. Development of cells in cultures has been found to be greatly influenced by the conditions of the culture. Much work has focused on the effect of the motions of cells in the culture relative to the solution. Recently rotating wall vessels have been used with success in achieving improved cellular cultures. Speculation and limited research have focused on the low shear environment and the ability of rotating vessels to keep cells suspended in solution rather than floating or sedimenting as the primary reasons for the improved cellular cultures using these devices. It is widely believed that the cultures obtained using a rotating wall vessel simulates to some degree the effect of microgravity on cultures. It has also been speculated that the microgravity environment may provide the ideal acceleration environment for culturing of cellular tissues due to the nearly negligible levels of sedimentation and shear possible. This work predicts particle trajectories of cells in rotating wall vessels of cylindrical and annular design consistent with the estimated properties of typical cellular cultures. Estimates of the shear encountered by cells in solution and the interactions with walls are studied. Comparisons of potential experiments in ground and microgravity environments are performed.

Effect of kumquat (Fortunella crassifolia) pericarp on natural killer cell activity in vitro and in vivo.

PubMed

Nagahama, Kiyoko; Eto, Nozomu; Shimojo, Tomofumi; Kondoh, Tomomi; Nakahara, Keiko; Sakakibara, Yoichi; Fukui, Keiichi; Suiko, Masahito

2015-01-01

Natural killer (NK) cells play a key role in innate immune defense against infectious disease and cancer. A reduction of NK activity is likely to be associated with increased risk of these types of disease. In this study, we investigate the activation potential of kumquat pericarp acetone fraction (KP-AF) on NK cells. It is shown to significantly increase IFN-γ production and NK cytotoxic activity in human KHYG-1 NK cells. Moreover, oral administration of KP-AF significantly improves both suppressed plasma IFN-γ levels and NK cytotoxic activity per splenocyte in restraint-stressed mice. These results indicate that raw kumquat pericarp activates NK cells in vitro and in vivo. To identify the active constituents, we also examined IFN-γ production on KHYG-1 cells by the predicted active components. Only β-cryptoxanthin increased IFN-γ production, suggesting that NK cell activation effects of KP-AF may be caused by carotenoids such as β-cryptoxanthin.

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo.

PubMed

Thurber, Greg M; Yang, Katy S; Reiner, Thomas; Kohler, Rainer H; Sorger, Peter; Mitchison, Tim; Weissleder, Ralph

2013-01-01

Pharmacokinetic analysis at the organ level provides insight into how drugs distribute throughout the body, but cannot explain how drugs work at the cellular level. Here we demonstrate in vivo single-cell pharmacokinetic imaging of PARP-1 inhibitors and model drug behaviour under varying conditions. We visualize intracellular kinetics of the PARP-1 inhibitor distribution in real time, showing that PARP-1 inhibitors reach their cellular target compartment, the nucleus, within minutes in vivo both in cancer and normal cells in various cancer models. We also use these data to validate predictive finite element modelling. Our theoretical and experimental data indicate that tumour cells are exposed to sufficiently high PARP-1 inhibitor concentrations in vivo and suggest that drug inefficiency is likely related to proteomic heterogeneity or insensitivity of cancer cells to DNA-repair inhibition. This suggests that single-cell pharmacokinetic imaging and derived modelling improve our understanding of drug action at single-cell resolution in vivo.

Prediction-based association control scheme in dense femtocell networks.

PubMed

Sung, Nak Woon; Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond

2017-01-01

The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system's effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations.

PubMed

Tang, Jing

2017-01-01

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Metabolite formation kinetics and intrinsic clearance of phenacetin, tolbutamide, alprazolam, and midazolam in adenoviral cytochrome P450-transfected HepG2 cells and comparison with hepatocytes and in vivo.

PubMed

Donato, M Teresa; Hallifax, David; Picazo, Laura; Castell, José V; Houston, J Brian; Gomez-Lechón, M José; Lahoz, Agustin

2010-09-01

Cryopreserved human hepatocytes and other in vitro systems often underpredict in vivo intrinsic clearance (CL(int)). The aim of this study was to explore the potential utility of HepG2 cells transduced with adenovirus vectors expressing a single cytochrome P450 enzyme (Ad-CYP1A2, Ad-CYP2C9, or Ad-CYP3A4) for metabolic clearance predictions. The kinetics of metabolite formation from phenacetin, tolbutamide, and alprazolam and midazolam, selected as substrates probes for CYP1A2, CYP2C9, and CYP3A4, respectively, were characterized in this in vitro system. The magnitude of the K(m) or S(50) values observed in Ad-P450 cells was similar to those found in the literature for other human liver-derived systems. For each substrate, CL(int) (or CL(max)), values from Ad-P450 systems were scaled to human hepatocytes in primary culture using the relative activity factor (RAF) approach. Scaled Ad-P450 CL(int) values were approximately 3- to 6-fold higher (for phenacetin O-deethylation, tolbutamide 4-hydroxylation, and alprazolam 4-hydroxyaltion) or lower (midazolam 1'-hydroxylation) than those reported for human cryopreserved hepatocytes in suspension. Comparison with the in vivo data reveals that Ad-P450 cells provide a favorable prediction of CL(int) for the substrates studied (in a range of 20-200% in vivo observed CL(int)). This is an improvement compared with the consistent underpredictions (<10-50% in in vivo observed CL(int)) found in cryopreserved hepatocyte studies with the same substrates. These results suggest that the Ad-P450 cell is a promising in vitro system for clearance predictions of P450-metabolized drugs.

Metabolic enzyme microarray coupled with miniaturized cell-culture array technology for high-throughput toxicity screening.

PubMed

Lee, Moo-Yeal; Dordick, Jonathan S; Clark, Douglas S

2010-01-01

Due to poor drug candidate safety profiles that are often identified late in the drug development process, the clinical progression of new chemical entities to pharmaceuticals remains hindered, thus resulting in the high cost of drug discovery. To accelerate the identification of safer drug candidates and improve the clinical progression of drug candidates to pharmaceuticals, it is important to develop high-throughput tools that can provide early-stage predictive toxicology data. In particular, in vitro cell-based systems that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process. The in vitro techniques that provide a high degree of human toxicity prediction will be perhaps more important in cosmetic and chemical industries in Europe, as animal toxicity testing is being phased out entirely in the immediate future.We have developed a metabolic enzyme microarray (the Metabolizing Enzyme Toxicology Assay Chip, or MetaChip) and a miniaturized three-dimensional (3D) cell-culture array (the Data Analysis Toxicology Assay Chip, or DataChip) for high-throughput toxicity screening of target compounds and their metabolic enzyme-generated products. The human or rat MetaChip contains an array of encapsulated metabolic enzymes that is designed to emulate the metabolic reactions in the human or rat liver. The human or rat DataChip contains an array of 3D human or rat cells encapsulated in alginate gels for cell-based toxicity screening. By combining the DataChip with the complementary MetaChip, in vitro toxicity results are obtained that correlate well with in vivo rat data.

Self-reported clothing size as a proxy measure for body size.

PubMed

Hughes, Laura A E; Schouten, Leo J; Goldbohm, R Alexandra; van den Brandt, Piet A; Weijenberg, Matty P

2009-09-01

Few studies have considered the potential utility of clothing size as a predictor of diseases associated with body weight. We used data on weight-stable men and women from a subcohort of the Netherlands Cohort Study to assess the correlation of clothing size with other anthropometric variables. Cox regression using the case-cohort approach was performed to establish whether clothing size can predict cancer risk after 13.3 years of follow-up, and if additionally considering body mass index (BMI) in the model improves the prediction. Trouser and skirt size correlated well with circumference measurements. Skirt size predicted endometrial cancer risk, and this effect was slightly attenuated when BMI was added to the model. Trouser size predicted risk of renal cell carcinoma, regardless of whether BMI was in the model. Clothing size appears to predict cancer risk independently of BMI, suggesting that clothing size is a useful measure to consider in epidemiologic studies when waist circumference is not available.

Voidage correction algorithm for unresolved Euler-Lagrange simulations

NASA Astrophysics Data System (ADS)

Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan

2018-04-01

The effect of grid coarsening on the predicted total drag force and heat exchange rate in dense gas-particle flows is investigated using Euler-Lagrange (EL) approach. We demonstrate that grid coarsening may reduce the predicted total drag force and exchange rate. Surprisingly, exchange coefficients predicted by the EL approach deviate more significantly from the exact value compared to results of Euler-Euler (EE)-based calculations. The voidage gradient is identified as the root cause of this peculiar behavior. Consequently, we propose a correction algorithm based on a sigmoidal function to predict the voidage experienced by individual particles. Our correction algorithm can significantly improve the prediction of exchange coefficients in EL models, which is tested for simulations involving Euler grid cell sizes between 2d_p and 12d_p . It is most relevant in simulations of dense polydisperse particle suspensions featuring steep voidage profiles. For these suspensions, classical approaches may result in an error of the total exchange rate of up to 30%.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

PubMed

Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

2016-10-15

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Efficient Identification of Novel Hla-A*0201–Presented Cytotoxic T Lymphocyte Epitopes in the Widely Expressed Tumor Antigen Prame by Proteasome-Mediated Digestion Analysis

PubMed Central

Kessler, Jan H.; Beekman, Nico J.; Bres-Vloemans, Sandra A.; Verdijk, Pauline; van Veelen, Peter A.; Kloosterman-Joosten, Antoinette M.; Vissers, Debby C.J.; ten Bosch, George J.A.; Kester, Michel G.D.; Sijts, Alice; Drijfhout, Jan Wouter; Ossendorp, Ferry; Offringa, Rienk; Melief, Cornelis J.M.

2001-01-01

We report the efficient identification of four human histocompatibility leukocyte antigen (HLA)-A*0201–presented cytotoxic T lymphocyte (CTL) epitopes in the tumor-associated antigen PRAME using an improved “reverse immunology” strategy. Next to motif-based HLA-A*0201 binding prediction and actual binding and stability assays, analysis of in vitro proteasome-mediated digestions of polypeptides encompassing candidate epitopes was incorporated in the epitope prediction procedure. Proteasome cleavage pattern analysis, in particular determination of correct COOH-terminal cleavage of the putative epitope, allows a far more accurate and selective prediction of CTL epitopes. Only 4 of 19 high affinity HLA-A*0201 binding peptides (21%) were found to be efficiently generated by the proteasome in vitro. This approach avoids laborious CTL response inductions against high affinity binding peptides that are not processed and limits the number of peptides to be assayed for binding. CTL clones induced against the four identified epitopes (VLDGLDVLL, PRA100–108; SLYSFPEPEA, PRA142–151; ALYVDSLFFL, PRA300–309; and SLLQHLIGL, PRA425–433) lysed melanoma, renal cell carcinoma, lung carcinoma, and mammary carcinoma cell lines expressing PRAME and HLA-A*0201. This indicates that these epitopes are expressed on cancer cells of diverse histologic origin, making them attractive targets for immunotherapy of cancer. PMID:11136822

Mining manufacturing data for discovery of high productivity process characteristics.

PubMed

Charaniya, Salim; Le, Huong; Rangwala, Huzefa; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

2010-06-01

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses. 2010 Elsevier B.V. All rights reserved.

Non-tumour bone marrow lymphocytes correlate with improved overall survival in childhood acute lymphoblastic leukaemia.

PubMed

Edwin, Claire; Dean, Joanne; Bonnett, Laura; Phillips, Kate; Keenan, Russell

2016-10-01

Composition of tumour immune cell infiltrates correlates with response to treatment and overall survival (OS) in several cancer settings. We retrospectively examined immune cells present in diagnostic bone marrow aspirates from paediatric patients with B-cell acute lymphoblastic leukaemia. Our analysis identified a sub-group (∼30% of patients) with >2.37% CD20 and >6.05% CD7 expression, which had 100% OS, and a sub-group (∼30% of patients) with ≤2.37% CD20 and ≤6.05% CD7 expression at increased risk of treatment failure (66.7% OS, P < 0.05). Immune cell infiltrate at diagnosis may predict treatment response and could provide a means to enhance immediate treatment risk stratification. © 2016 The Authors. Pediatric Blood & Cancer, published by Wiley Periodicals, Inc.

Deep NPM1 Sequencing Following Allogeneic Hematopoietic Cell Transplantation Improves Risk Assessment in Adults with NPM1-Mutated AML.

PubMed

Zhou, Yi; Othus, Megan; Walter, Roland B; Estey, Elihu H; Wu, David; Wood, Brent L

2018-04-21

Relapse is the major cause of death in patients with acute myeloid leukemia (AML) after allogeneic hematopoietic cell transplantation (HCT). Measurable residual disease (MRD) detected by multiparameter flow cytometry (MFC) before and after HCT is a strong, independent risk factor for relapse. As next-generation sequencing (NGS) is increasingly applied in AML MRD detection, it remains to be determined if NGS can improve prediction of post-HCT relapse. Herein, we investigated pre-HCT MRD detected by MFC and NGS in 59 adult patients with NPM1-mutated AML in morphologic remission; 45 of the 59 had post-HCT MRD determined by MFC and NGS around day 28. Before HCT, MRD detected by MFC was the most significant risk factor for relapse (hazard ratio [HR], 4.63; P < .001), whereas MRD detected only by NGS was not. After HCT, MRD detected by either MFC or NGS was significant risk factor for relapse (HR, 4.96, P = .004 and HR, 4.36, P = .002, respectively). Combining pre- and post-HCT MRD provided the best prediction for relapse (HR, 5.25; P < .001), with a sensitivity at 83%. We conclude that NGS testing of mutated NPM1 post-HCT improves the risk assessment for relapse, whereas pre-HCT MFC testing identifies a subset of high-risk patients in whom additional therapy should be tested. Copyright © 2018 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

PubMed

Megchelenbrink, Wout; Katzir, Rotem; Lu, Xiaowen; Ruppin, Eytan; Notebaart, Richard A

2015-09-29

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

Cytolytic Activity Score to Assess Anticancer Immunity in Colorectal Cancer.

PubMed

Narayanan, Sumana; Kawaguchi, Tsutomu; Yan, Li; Peng, Xuan; Qi, Qianya; Takabe, Kazuaki

2018-05-16

Elevated tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment is a known positive prognostic factor in colorectal cancer (CRC). We hypothesized that since cytotoxic T cells release cytolytic proteins such as perforin (PRF1) and pro-apoptotic granzymes (GZMA) to attack cancer cells, a cytolytic activity score (CYT) would be a useful tool to assess anticancer immunity. Genomic expression data were obtained from 456 patients from The Cancer Genome Atlas (TCGA). CYT was defined by GZMA and PRF1 expression, and CIBERSORT was used to evaluate intratumoral immune cell composition. High CYT was associated with high microsatellite instability (MSI-H), as well as high levels of activated memory CD4+T cells, gamma-delta T cells, and M1 macrophages. CYT-high CRC patients had improved overall survival (p = 0.019) and disease-free survival (p = 0.016) compared with CYT-low CRC patients, especially in TIL-positive tumors. Multivariate analysis demonstrated that CYT- high associates with improved survival independently after controlling for age, lymphovascular invasion, colonic location, microsatellite instability, and TIL positivity. The levels of immune checkpoint molecules (ICMs)-programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), lymphocyte-activation gene 3 (LAG3), T cell immunoglobulin and mucin domain 3 (TIM3), and indoleamine 2,3-dioxygenase 1 (IDO1)-correlated significantly with CYT (p < 0.0001); with improved survival in CYT-high and ICM-low patients, and poorer survival in ICM-high patients. High CYT within CRC is associated with improved survival, likely due to increased immunity and cytolytic activity of T cells and M1 macrophages. High CYT is also associated with high expression of ICMs; thus, further studies to elucidate the role of CYT as a predictive biomarker of the efficacy of immune checkpoint blockade are warranted.

A polynomial based model for cell fate prediction in human diseases.

PubMed

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

Visual Analytics approach for Lightning data analysis and cell nowcasting

NASA Astrophysics Data System (ADS)

Peters, Stefan; Meng, Liqiu; Betz, Hans-Dieter

2013-04-01

Thunderstorms and their ground effects, such as flash floods, hail, lightning, strong wind and tornadoes, are responsible for most weather damages (Bonelli & Marcacci 2008). Thus to understand, identify, track and predict lightning cells is essential. An important aspect for decision makers is an appropriate visualization of weather analysis results including the representation of dynamic lightning cells. This work focuses on the visual analysis of lightning data and lightning cell nowcasting which aim to detect and understanding spatial-temporal patterns of moving thunderstorms. Lightnings are described by 3D coordinates and the exact occurrence time of lightnings. The three-dimensionally resolved total lightning data used in our experiment are provided by the European lightning detection network LINET (Betz et al. 2009). In all previous works, lightning point data, detected lightning cells and derived cell tracks are visualized in 2D. Lightning cells are either displayed as 2D convex hulls with or without the underlying lightning point data. Due to recent improvements of lightning data detection and accuracy, there is a growing demand on multidimensional and interactive visualization in particular for decision makers. In a first step lightning cells are identified and tracked. Then an interactive graphic user interface (GUI) is developed to investigate the dynamics of the lightning cells: e.g. changes of cell density, location, extension as well as merging and splitting behavior in 3D over time. In particular a space time cube approach is highlighted along with statistical analysis. Furthermore a lightning cell nowcasting is conducted and visualized. The idea thereby is to predict the following cell features for the next 10-60 minutes including location, centre, extension, density, area, volume, lifetime and cell feature probabilities. The main focus will be set to a suitable interactive visualization of the predicted featured within the GUI. The developed visual exploring tool for the purpose of supporting decision making is investigated for two determined user groups: lightning experts and interested lay public. Betz HD, Schmidt K, Oettinger WP (2009) LINET - An International VLF/LF Lightning Detection Network in Europe. In: Betz HD, Schumann U, Laroche P (eds) Lightning: Principles, Instruments and Applications. Springer Netherlands, Dordrecht, pp 115-140 Bonelli P, Marcacci P (2008) Thunderstorm nowcasting by means of lightning and radar data: algorithms and applications in northern Italy. Nat. Hazards Earth Syst. Sci 8(5):1187-1198

Predictive performance of the human Cell Line Activation Test (h-CLAT) for lipophilic chemicals with high octanol-water partition coefficients.

PubMed

Takenouchi, Osamu; Miyazawa, Masaaki; Saito, Kazutoshi; Ashikaga, Takao; Sakaguchi, Hitoshi

2013-01-01

To meet the urgent need for a reliable alternative test for predicting skin sensitizing potential of many chemicals, we have developed a cell-based in vitro test, human Cell Line Activation Test (h-CLAT). However, the predictive performance for lipophilic chemicals in the h-CLAT still remains relatively unknown. Moreover, it's suggested that low water solubility of chemicals might induce false negative outcomes. Thus, in this study, we tested relatively low water soluble 37 chemicals with log Kow values above and below 3.5 in the h-CLAT. The small-scale assessment resulted in nine false negative outcomes for chemicals with log Kow values greater than 3.5. We then created a dataset of 143 chemicals by combining the existing dataset of 106 chemicals and examined the predictive performance of the h-CLAT for chemicals with a log Kow of less than 3.5; a total of 112 chemicals from the 143 chemicals in the dataset. The sensitivity and overall accuracy for the 143 chemicals were 83% and 80%, respectively. In contrast, sensitivity and overall accuracy for the 112 chemicals with log Kow values below 3.5 improved to 94% and 88%, respectively. These data suggested that the h-CLAT could successfully detect sensitizers with log Kow values up to 3.5. When chemicals with log Kow values greater than 3.5 that were deemed positive by h-CLAT were included with the 112 chemicals, the sensitivity and accuracy in terms of the resulting applicable 128 chemicals out of the 143 chemicals became 95% and 88%, respectively. The use of log Kow values gave the h-CLAT a higher predictive performance. Our results demonstrated that the h-CLAT could predict sensitizing potential of various chemicals, which contain lipophilic chemicals using a large-scale chemical dataset.

Dynamics of electrical double layer formation in room-temperature ionic liquids under constant-current charging conditions

NASA Astrophysics Data System (ADS)

Jiang, Xikai; Huang, Jingsong; Zhao, Hui; Sumpter, Bobby G.; Qiao, Rui

2014-07-01

We report detailed simulation results on the formation dynamics of an electrical double layer (EDL) inside an electrochemical cell featuring room-temperature ionic liquids (RTILs) enclosed between two planar electrodes. Under relatively small charging currents, the evolution of cell potential from molecular dynamics (MD) simulations during charging can be suitably predicted by the Landau-Ginzburg-type continuum model proposed recently (Bazant et al 2011 Phys. Rev. Lett. 106 046102). Under very large charging currents, the cell potential from MD simulations shows pronounced oscillation during the initial stage of charging, a feature not captured by the continuum model. Such oscillation originates from the sequential growth of the ionic space charge layers near the electrode surface. This allows the evolution of EDLs in RTILs with time, an atomistic process difficult to visualize experimentally, to be studied by analyzing the cell potential under constant-current charging conditions. While the continuum model cannot predict the potential oscillation under such far-from-equilibrium charging conditions, it can nevertheless qualitatively capture the growth of cell potential during the later stage of charging. Improving the continuum model by introducing frequency-dependent dielectric constant and density-dependent ion diffusion coefficients may help to further extend the applicability of the model. The evolution of ion density profiles is also compared between the MD and the continuum model, showing good agreement.

Dynamics of electrical double layer formation in room-temperature ionic liquids under constant-current charging conditions.

PubMed

Jiang, Xikai; Huang, Jingsong; Zhao, Hui; Sumpter, Bobby G; Qiao, Rui

2014-07-16

We report detailed simulation results on the formation dynamics of an electrical double layer (EDL) inside an electrochemical cell featuring room-temperature ionic liquids (RTILs) enclosed between two planar electrodes. Under relatively small charging currents, the evolution of cell potential from molecular dynamics (MD) simulations during charging can be suitably predicted by the Landau-Ginzburg-type continuum model proposed recently (Bazant et al 2011 Phys. Rev. Lett. 106 046102). Under very large charging currents, the cell potential from MD simulations shows pronounced oscillation during the initial stage of charging, a feature not captured by the continuum model. Such oscillation originates from the sequential growth of the ionic space charge layers near the electrode surface. This allows the evolution of EDLs in RTILs with time, an atomistic process difficult to visualize experimentally, to be studied by analyzing the cell potential under constant-current charging conditions. While the continuum model cannot predict the potential oscillation under such far-from-equilibrium charging conditions, it can nevertheless qualitatively capture the growth of cell potential during the later stage of charging. Improving the continuum model by introducing frequency-dependent dielectric constant and density-dependent ion diffusion coefficients may help to further extend the applicability of the model. The evolution of ion density profiles is also compared between the MD and the continuum model, showing good agreement.

Targeting B7x and B7-H3 as New Immunotherapies for Prostate Cancer

DTIC Science & Technology

2017-11-01

treat rheumatoid arthritis and prevent acute kidney transplant rejection (Fiocco et al., 2008; Vincenti et al., 2011). The past decade has witnessed a...cell lines. The first Phase I trial with pidilizumab recruited patients with hematologic malignancies, including acute myeloid leukemia (AML), chronic... radiation , chemotherapy, other coinhibi- tory antibodies, or vaccines can improve the response rate in cancers. Predictive biomarkers need to be developed

The Cosmetics Europe strategy for animal-free genotoxicity testing: project status up-date.

PubMed

Pfuhler, S; Fautz, R; Ouedraogo, G; Latil, A; Kenny, J; Moore, C; Diembeck, W; Hewitt, N J; Reisinger, K; Barroso, J

2014-02-01

The Cosmetics Europe (formerly COLIPA) Genotoxicity Task Force has driven and funded three projects to help address the high rate of misleading positives in in vitro genotoxicity tests: The completed "False Positives" project optimized current mammalian cell assays and showed that the predictive capacity of the in vitro micronucleus assay was improved dramatically by selecting more relevant cells and more sensitive toxicity measures. The on-going "3D skin model" project has been developed and is now validating the use of human reconstructed skin (RS) models in combination with the micronucleus (MN) and Comet assays. These models better reflect the in use conditions of dermally applied products, such as cosmetics. Both assays have demonstrated good inter- and intra-laboratory reproducibility and are entering validation stages. The completed "Metabolism" project investigated enzyme capacities of human skin and RS models. The RS models were shown to have comparable metabolic capacity to native human skin, confirming their usefulness for testing of compounds with dermal exposure. The program has already helped to improve the initial test battery predictivity and the RS projects have provided sound support for their use as a follow-up test in the assessment of the genotoxic hazard of cosmetic ingredients in the absence of in vivo data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Total lightning characteristics of recent hazardous weather events in Japan

NASA Astrophysics Data System (ADS)

Hobara, Y.; Kono, S.; Ogawa, T.; Heckman, S.; Stock, M.; Liu, C.

2017-12-01

In recent years, the total lightning (IC + CG) activity have attracted a lot of attention to improve the quality of prediction of hazardous weather phenomena (hail, wind gusts, tornadoes, heavy precipitation). Sudden increases of the total lightning flash rate so-called lightning jump (LJ) preceding the hazardous weather, reported in several studies, are one of the promising precursors. Although, increases in the frequency and intensity of these extreme weather events were reported in Japan, relationship with these events with total lightning have not studied intensively yet. In this paper, we will demonstrate the recent results from Japanese total lightning detection network (JTLN) in relation with hazardous weather events occurred in Japan in the period of 2014-2016. Automatic thunderstorm cell tracking was carried out based on the very high spatial and temporal resolution X-band MP radar echo data (1 min and 250 m) to correlate with total lightning activity. Results obtained reveal promising because the flash rate of total lightning tends to increase about 10 40 minutes before the onset of the extreme weather events. We also present the differences in lightning characteristics of thunderstorm cells between hazardous weather events and non-hazardous weather events, which is a vital information to improve the prediction efficiency.

PIK3CA and KRAS mutations in cell free circulating DNA are useful markers for monitoring ovarian clear cell carcinoma

PubMed Central

Morikawa, Asuka; Hayashi, Tomoatsu; Shimizu, Naomi; Kobayashi, Mana; Taniue, Kenzui; Takahashi, Akiko; Tachibana, Kota; Saito, Misato; Kawabata, Ayako; Iida, Yasushi; Ueda, Kazu; Saito, Motoaki; Yanaihara, Nozomu; Tanabe, Hiroshi; Yamada, Kyosuke; Takano, Hirokuni; Nureki, Osamu; Okamoto, Aikou; Akiyama, Tetsu

2018-01-01

Ovarian clear cell carcinoma (OCCC) exhibits distinct phenotypes, such as resistance to chemotherapy, poor prognosis and an association with endometriosis. Biomarkers and imaging techniques currently in use are not sufficient for reliable diagnosis of this tumor or prediction of therapeutic response. It has recently been reported that analysis of somatic mutations in cell-free circulating DNA (cfDNA) released from tumor tissues can be useful for tumor diagnosis. In the present study, we attempted to detect mutations in PIK3CA and KRAS in cfDNA from OCCC patients using droplet digital PCR (ddPCR). Here we show that we were able to specifically detect PIK3CA-H1047R and KRAS-G12D in cfDNA from OCCC patients and monitor their response to therapy. Furthermore, we found that by cleaving wild-type PIK3CA using the CRISPR/Cas9 system, we were able to improve the sensitivity of the ddPCR method and detect cfDNA harboring PIK3CA-H1047R. Our results suggest that detection of mutations in cfDNA by ddPCR would be useful for the diagnosis of OCCC, and for predicting its recurrence. PMID:29632642

A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma

PubMed Central

Wei, Jin-Huan; Haddad, Ahmed; Wu, Kai-Jie; Zhao, Hong-Wei; Kapur, Payal; Zhang, Zhi-Ling; Zhao, Liang-Yun; Chen, Zhen-Hua; Zhou, Yun-Yun; Zhou, Jian-Cheng; Wang, Bin; Yu, Yan-Hong; Cai, Mu-Yan; Xie, Dan; Liao, Bing; Li, Cai-Xia; Li, Pei-Xing; Wang, Zong-Ren; Zhou, Fang-Jian; Shi, Lei; Liu, Qing-Zuo; Gao, Zhen-Li; He, Da-Lin; Chen, Wei; Hsieh, Jer-Tsong; Li, Quan-Zhen; Margulis, Vitaly; Luo, Jun-Hang

2015-01-01

Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the overall survival of ccRCC patients (hazard ratio=2.96−4.82; P=3.9 × 10−6−2.2 × 10−9), independent of standard clinical prognostic factors. The five-CpG-based classifier successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome in respective clinical stages and individual ‘stage, size, grade and necrosis' scores. Moreover, methylation at the five CpGs correlates with expression of five genes: PITX1, FOXE3, TWF2, EHBP1L1 and RIN1. Our five-CpG-based classifier is a practical and reliable prognostic tool for ccRCC that can add prognostic value to the staging system. PMID:26515236

Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?

PubMed

Fanesi, Andrea; Wagner, Heiko; Wilhelm, Christian

2017-02-08

Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power (R 2 > 0.8) and low error in predicting new observations. Interestingly, not all of the predictors present the same weight in the modelling of growth rate. A set of specific parameters, such as non-photochemical fluorescence quenching (NPQ) and the quantum yield of carbon production in the first model, and lipid, protein and carbohydrate contents for the second one, strongly covary with cell growth rate regardless of the taxonomic position of the phytoplankton species investigated. This reflects a set of specific physiological adjustments covarying with growth rate, conserved among taxonomically distant algal species that might be used as guidelines for the improvement of modern primary production models. The high predictive power of both sets of cellular traits for growth rate is of great importance for applied phycological studies. Our approach may find application as a quality control tool for the monitoring of phytoplankton populations in natural communities or in photobioreactors. © 2017 The Author(s).

Ultrasound-assisted extraction of gymnemic acids from Gymnema sylvestre leaves and its effect on insulin-producing RINm-5 F β cell lines.

PubMed

Sheoran, Sunita; Panda, Bibhu Prasad; Admane, Prasad S; Panda, Amulya Kumar; Wajid, Saima

2015-01-01

Gymnema sylvestre is an important anti-diabetic medicinal plant, hence it is necessary to study the effective extraction of its active medicinal components. To develop an efficient ultrasound-assisted extraction method for anti-diabetic gymnemic acids from Gymnema sylvestre leaves and measure their effect on insulin-producing RINm-5 F β cells. Box-Behnken's design and response surface methodology was applied to the ultrasound-assisted extraction of gymnemic acids from Gymnema sylvestre leaves. Analysis of gymnemic acids was carried out by high-performance thin-layer chromatography by converting total gymnemic acids into gymnemagenin by alkali hydrolysis. Effects of extracts on insulin production were tested on cultured, insulin-producing RINm-5 F β cell lines. The point prediction tool of the design expert software predicted 397.9 mg gymnemic acids per gram of the defatted G. sylvestre leaves using ultrasound-assisted extraction, with ethanol at 60 °C for 30 min. The predicted condition shows 93.34% validity under experimental conditions. The ultrasound-assisted extract caused up to about four times more insulin production from RINm-5 F β cells than extracts obtained from Soxhlet extraction. Response surface methodology was successfully used to improve the extraction of gymnemic acids from G. sylvestre leaves. The ultrasound-assisted extraction process may be a better alternative to prepare such herbal extracts because it saves time and may prevent excess degradation of the target analytes. Copyright © 2014 John Wiley & Sons, Ltd.

Dynamic regulation of EZH2 from HPSc to hepatocyte-like cell fate

PubMed Central

Helsen, Nicky; Vanhove, Jolien; Boon, Ruben; Xu, Zhuofei; Ordovas, Laura; Verfaillie, Catherine M.

2017-01-01

Currently, drug metabolization and toxicity studies rely on the use of primary human hepatocytes and hepatoma cell lines, which both have conceivable limitations. Human pluripotent stem cell (hPSC)—derived hepatocyte-like cells (HLCs) are an alternative and valuable source of hepatocytes that can overcome these limitations. EZH2 (enhancer of zeste homolog 2), a transcriptional repressor of the polycomb repressive complex 2 (PRC2), may play an important role in hepatocyte development, but its role during in vitro hPSC-HLC differentiation has not yet been assessed. We here demonstrate dynamic regulation of EZH2 during hepatic differentiation of hPSC. To enhance EZH2 expression, we inducibly overexpressed EZH2 between d0 and d8, demonstrating a significant improvement in definitive endoderm formation, and improved generation of HLCs. Despite induction of EZH2 overexpression until d8, EZH2 transcript and protein levels decreased from d4 onwards, which might be caused by expression of microRNAs predicted to inhibit EZH2 expression. In conclusion, our studies demonstrate that EZH2 plays a role in endoderm formation and hepatocyte differentiation, but its expression is tightly post-transcriptionally regulated during this process. PMID:29091973

Thermal modeling of a Ni-H2 battery cell

NASA Technical Reports Server (NTRS)

Ryu, Si-Ok; Dewitt, K. J.; Keith, T. G.

1991-01-01

The nickel-hydrogen secondary battery has many desirable features which make it attractive for satellite power systems. It can provide a significant improvement over the energy density of present spacecraft nickel-cadnium batteries, combined with longer life, tolerance to overcharge and possibility of state-of-charge indication. However, to realize these advantages, accurate thermal modeling of nickel-hydrogen cells is required in order to properly design the battery pack so that it operates within a specified temperature range during the operation. Maintenance of a low operating temperature and a uniform temperature profile within the cell will yield better reliability, improved cycle life and better charge/discharge efficiencies. This research has the objective of developing and testing a thermal model which can be used to characterize battery operation. Primarily, temperature distribution with the heat generation rates as a function of position and time will be evaluated for a Ni-H2 cell in the three operating modes: (1) charge cycle, (2) discharge cycle, and (3) overcharge condition, if applicable. Variables to be examined include charging current, discharge rates, state of charge, pressure and temperature. Once the thermal model has been developed, this resulting model will predict the actual operating temperature and temperature gradient for the specific cell geometry to be used.

Engineering A11 Minibody-Conjugated, Polypeptide-Based Gold Nanoshells for Prostate Stem Cell Antigen (PSCA)-Targeted Photothermal Therapy.

PubMed

Mayle, Kristine M; Dern, Kathryn R; Wong, Vincent K; Chen, Kevin Y; Sung, Shijun; Ding, Ke; Rodriguez, April R; Knowles, Scott; Taylor, Zachary; Zhou, Z Hong; Grundfest, Warren S; Wu, Anna M; Deming, Timothy J; Kamei, Daniel T

2017-02-01

Currently, there is no curative treatment for advanced metastatic prostate cancer, and options, such as chemotherapy, are often nonspecific, harming healthy cells and resulting in severe side effects. Attaching targeting ligands to agents used in anticancer therapies has been shown to improve efficacy and reduce nonspecific toxicity. Furthermore, the use of triggered therapies can enable spatial and temporal control over the treatment. Here, we combined an engineered prostate cancer-specific targeting ligand, the A11 minibody, with a novel photothermal therapy agent, polypeptide-based gold nanoshells, which generate heat in response to near-infrared light. We show that the A11 minibody strongly binds to the prostate stem cell antigen that is overexpressed on the surface of metastatic prostate cancer cells. Compared to nonconjugated gold nanoshells, our A11 minibody-conjugated gold nanoshell exhibited significant laser-induced, localized killing of prostate cancer cells in vitro. In addition, we improved upon a comprehensive heat transfer mathematical model that was previously developed by our laboratory. By relaxing some of the assumptions of our earlier model, we were able to generate more accurate predictions for this particular study. Our experimental and theoretical results demonstrate the potential of our novel minibody-conjugated gold nanoshells for metastatic prostate cancer therapy.

NanoTopoChip: High-throughput nanotopographical cell instruction.

PubMed

Hulshof, Frits F B; Zhao, Yiping; Vasilevich, Aliaksei; Beijer, Nick R M; de Boer, Meint; Papenburg, Bernke J; van Blitterswijk, Clemens; Stamatialis, Dimitrios; de Boer, Jan

2017-10-15

Surface topography is able to influence cell phenotype in numerous ways and offers opportunities to manipulate cells and tissues. In this work, we develop the Nano-TopoChip and study the cell instructive effects of nanoscale topographies. A combination of deep UV projection lithography and conventional lithography was used to fabricate a library of more than 1200 different defined nanotopographies. To illustrate the cell instructive effects of nanotopography, actin-RFP labeled U2OS osteosarcoma cells were cultured and imaged on the Nano-TopoChip. Automated image analysis shows that of many cell morphological parameters, cell spreading, cell orientation and actin morphology are mostly affected by the nanotopographies. Additionally, by using modeling, the changes of cell morphological parameters could by predicted by several feature shape parameters such as lateral size and spacing. This work overcomes the technological challenges of fabricating high quality defined nanoscale features on unprecedented large surface areas of a material relevant for tissue culture such as PS and the screening system is able to infer nanotopography - cell morphological parameter relationships. Our screening platform provides opportunities to identify and study the effect of nanotopography with beneficial properties for the culture of various cell types. The nanotopography of biomaterial surfaces can be modified to influence adhering cells with the aim to improve the performance of medical implants and tissue culture substrates. However, the necessary knowledge of the underlying mechanisms remains incomplete. One reason for this is the limited availability of high-resolution nanotopographies on relevant biomaterials, suitable to conduct systematic biological studies. The present study shows the fabrication of a library of nano-sized surface topographies with high fidelity. The potential of this library, called the 'NanoTopoChip' is shown in a proof of principle HTS study which demonstrates how cells are affected by nanotopographies. The large dataset, acquired by quantitative high-content imaging, allowed us to use predictive modeling to describe how feature dimensions affect cell morphology. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Prognostic Cell Biological Markers in Cervical Cancer Patients Primarily Treated With (Chemo)radiation: A Systematic Review

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

Noordhuis, Maartje G.; Eijsink, Jasper J.H.; Roossink, Frank

2011-02-01

The aim of this study was to systematically review the prognostic and predictive significance of cell biological markers in cervical cancer patients primarily treated with (chemo)radiation. A PubMed, Embase, and Cochrane literature search was performed. Studies describing a relation between a cell biological marker and survival in {>=}50 cervical cancer patients primarily treated with (chemo)radiation were selected. Study quality was assessed, and studies with a quality score of 4 or lower were excluded. Cell biological markers were clustered on biological function, and the prognostic and predictive significance of these markers was described. In total, 42 studies concerning 82 cell biologicalmore » markers were included in this systematic review. In addition to cyclooxygenase-2 (COX-2) and serum squamous cell carcinoma antigen (SCC-ag) levels, markers associated with poor prognosis were involved in epidermal growth factor receptor (EGFR) signaling (EGFR and C-erbB-2) and in angiogenesis and hypoxia (carbonic anhydrase 9 and hypoxia-inducible factor-1{alpha}). Epidermal growth factor receptor and C-erbB-2 were also associated with poor response to (chemo)radiation. In conclusion, EGFR signaling is associated with poor prognosis and response to therapy in cervical cancer patients primarily treated with (chemo)radiation, whereas markers involved in angiogenesis and hypoxia, COX-2, and serum SCC-ag levels are associated with a poor prognosis. Therefore, targeting these pathways in combination with chemoradiation may improve survival in advanced-stage cervical cancer patients.« less

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

PubMed

Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

2018-04-24

Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

PubMed

Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A; Guinney, Justin

2015-01-01

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

A life prediction methodology for encapsulated solar cells

NASA Technical Reports Server (NTRS)

Coulbert, C. D.

1978-01-01

This paper presents an approach to the development of a life prediction methodology for encapsulated solar cells which are intended to operate for twenty years or more in a terrestrial environment. Such a methodology, or solar cell life prediction model, requires the development of quantitative intermediate relationships between local environmental stress parameters and the basic chemical mechanisms of encapsulant aging leading to solar cell failures. The use of accelerated/abbreviated testing to develop these intermediate relationships and in revealing failure modes is discussed. Current field and demonstration tests of solar cell arrays and the present laboratory tests to qualify solar module designs provide very little data applicable to predicting the long-term performance of encapsulated solar cells. An approach to enhancing the value of such field tests to provide data for life prediction is described.

A systematic review on the impact of diabetes mellitus on the ocular surface

PubMed Central

Shih, K Co; Lam, K S-L; Tong, L

2017-01-01

Diabetes mellitus is associated with extensive morbidity and mortality in any human community. It is well understood that the burden of diabetes is attributed to chronic progressive damage in major end-organs, but it is underappreciated that the most superficial and transparent organ affected by diabetes is the cornea. Different corneal components (epithelium, nerves, immune cells and endothelium) underpin specific systemic complications of diabetes. Just as diabetic retinopathy is a marker of more generalized microvascular disease, corneal nerve changes can predict peripheral and autonomic neuropathy, providing a window of opportunity for early treatment. In addition, alterations of immune cells in corneas suggest an inflammatory component in diabetic complications. Furthermore, impaired corneal epithelial wound healing may also imply more widespread disease. The non-invasiveness and improvement in imaging technology facilitates the emergence of new screening tools. Systemic control of diabetes can improve ocular surface health, possibly aided by anti-inflammatory and vasoprotective agents. PMID:28319106

Low-Cost CdTe/Silicon Tandem Solar Cells

DOE PAGES

Tamboli, Adele C.; Bobela, David C.; Kanevce, Ana; ...

2017-09-06

Achieving higher photovoltaic efficiency in single-junction devices is becoming increasingly difficult, but tandem modules offer the possibility of significant efficiency improvements. By device modeling we show that four-terminal CdTe/Si tandem solar modules offer the prospect of 25%-30% module efficiency, and technoeconomic analysis predicts that these efficiency gains can be realized at costs per Watt that are competitive with CdTe and Si single junction alternatives. The cost per Watt of the modeled tandems is lower than crystalline silicon, but slightly higher than CdTe alone. But, these higher power modules reduce area-related balance of system costs, providing increased value especially in area-constrainedmore » applications. This avenue for high-efficiency photovoltaics enables improved performance on a near-term timeframe, as well as a path to further reduced levelized cost of electricity as module and cell processes continue to advance.« less

Low-Cost CdTe/Silicon Tandem Solar Cells

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

Tamboli, Adele C.; Bobela, David C.; Kanevce, Ana

Achieving higher photovoltaic efficiency in single-junction devices is becoming increasingly difficult, but tandem modules offer the possibility of significant efficiency improvements. By device modeling we show that four-terminal CdTe/Si tandem solar modules offer the prospect of 25%-30% module efficiency, and technoeconomic analysis predicts that these efficiency gains can be realized at costs per Watt that are competitive with CdTe and Si single junction alternatives. The cost per Watt of the modeled tandems is lower than crystalline silicon, but slightly higher than CdTe alone. But, these higher power modules reduce area-related balance of system costs, providing increased value especially in area-constrainedmore » applications. This avenue for high-efficiency photovoltaics enables improved performance on a near-term timeframe, as well as a path to further reduced levelized cost of electricity as module and cell processes continue to advance.« less

Rapid learning in visual cortical networks.

PubMed

Wang, Ye; Dragoi, Valentin

2015-08-26

Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

Cytotoxic 3,4,5-trimethoxychalcones as mitotic arresters and cell migration inhibitors

PubMed Central

Salum, Lívia B.; Altei, Wanessa F.; Chiaradia, Louise D.; Cordeiro, Marlon N.S.; Canevarolo, Rafael R.; Melo, Carolina P.S.; Winter, Evelyn; Mattei, Bruno; Daghestani, Hikmat N.; Santos-Silva, Maria Cláudia; Creczynski-Pasa, Tânia B.; Yunes, Rosendo A.; Yunes, José A.; Andricopulo, Adriano D.; Day, Billy W.; Nunes, Ricardo J.; Vogt, Andreas

2013-01-01

Based on classical colchicine site ligands and a computational model of the colchicine binding site on beta tubulin, two classes of chalcone derivatives were designed, synthesized and evaluated for inhibition of tubulin assembly and toxicity in human cancer cell lines. Docking studies suggested that the chalcone scaffold could fit the colchicine site on tubulin in an orientation similar to that of the natural product. In particular, a 3,4,5-trimethoxyphenyl ring adjacent to the carbonyl group appeared to benefit the ligand-tubulin interaction, occupying the same subcavity as the corresponding moiety in colchicine. Consistent with modeling predictions, several 3,4,5-trimethoxychalcones showed improved cytotoxicity to murine acute lymphoblastic leukemia cells compared with a previously described parent compound, and inhibited tubulin assembly in vitro as potently as colchicine. The most potent chalcones inhibited the growth of human leukemia cell lines at nanomolar concentrations, caused microtubule destabilization and mitotic arrest in human cervical cancer cells, and inhibited human breast cancer cell migration in scratch wound and Boyden chamber assays. PMID:23524161

Prostate Cancer Cell Telomere Length Variability and Stromal Cell Telomere Length as Prognostic Markers for Metastasis and Death

PubMed Central

Heaphy, Christopher M.; Yoon, Ghil Suk; Peskoe, Sarah B.; Joshu, Corinne E.; Lee, Thomas K.; Giovannucci, Edward; Mucci, Lorelei A.; Kenfield, Stacey A.; Stampfer, Meir J.; Hicks, Jessica L.; De Marzo, Angelo M.; Platz, Elizabeth A.; Meeker, Alan K.

2013-01-01

Current prognostic indicators are imperfect predictors of outcome in men with clinicallylocalized prostate cancer. Thus, tissue-based markers are urgently needed to improve treatment and surveillance decision-making. Given that shortened telomeres enhance chromosomal instability and such instability is a hallmark of metastatic lesions, we hypothesized that alterations in telomere length in the primary cancer would predict risk of progression to metastasis and prostate cancer death. To test this hypothesis, we conducted a prospective cohort study of 596 surgically treated men who participated in the ongoing Health Professionals Follow-up Study. Men who had the combination of more variable telomere length among prostate cancer cells (cell-to-cell) and shorter telomere length in prostate cancer-associated stromal cells were substantially more likely to progress to metastasis or die of their prostate cancer. These findings point to the translational potential of this telomere biomarker for prognostication and risk stratification for individualized therapeutic and surveillance strategies. PMID:23779129

Morphogenesis and Complexity of the Tumor Patterns

NASA Astrophysics Data System (ADS)

Izquierdo-Kulich, E.; Nieto-Villar, J. M.

A mechanism to describe the apoptosis process at mesoscopic level through p53 is proposed in this paper. A deterministic model given by three differential equations is deduced from the mesoscopic approach, which exhibits sustained oscillations caused by a supercritical Andronov-Hopf bifurcation. Taking as hypothesis that the p53 sustained oscillation is the fundamental mechanism for apoptosis regulation; the model predicts that it is necessary a strict control of p53 to stimulated it, which is an important consideration to established new therapy strategy to fight cancer. The mathematical modeling of tumor growth allows us to describe the most important regularities of these systems. A stochastic model, based on the most important processes that take place at the level of individual cells, is proposed to predict the dynamical behavior of the expected radius of the tumor and its fractal dimension. It was found that the tumor has a characteristic fractal dimension, which contains the necessary information to predict the tumor growth until it reaches a stationary state. The mathematical modeling of tumor growth is an approach to explain the complex nature of these systems. A model that describes tumor growth was obtained by using a mesoscopic formalism and fractal dimension. This model theoretically predicts the relation between the morphology of the cell pattern and the mitosis/apoptosis quotient that helps to predict tumor growth from tumoral cells fractal dimension. The relation between the tumor macroscopic morphology and the cell pattern morphology is also determined. This could explain why the interface fractal dimension decreases with the increase of the cell pattern fractal dimension and consequently with the increase of the mitosis/apoptosis relation. Indexes to characterize tumoral cell proliferation and invasion capacities are proposed and used to predict the growth of different types of tumors. These indexes also show that the proliferation capacity is directly proportional to the invasion capacity. The proposed model assumes: i) only interface cells proliferate and invade the host, and ii) the fractal dimension of tumoral cell patterns, can reproduce the Gompertzian growth law. A mathematical model was obtained to describe the relation between the tissue morphology of cervix carcinoma and both dynamic processes of mitosis and apoptosis, and an expression to quantify the tumor aggressiveness, which in this context is associated with the tumor growth rate. The proposed model was applied to Stage III cervix carcinoma in vivo studies. In this study we found that the apoptosis rate was significantly smaller in the tumor tissues and both the mitosis rate and aggressiveness index decrease with Stage III patient's age. These quantitative results correspond to observed behavior in clinical and genetics studies. Finally, the entropy production rate was determined for avascular tumor growth. The proposed formula relates the fractal dimension of the tumor contour with the quotient between mitosis and apoptosis rate, which can be used to characterize the degree of proliferation of tumor cells. The entropy production rate was determined for fourteen tumor cell lines as a physical function of cancer robustness. The entropy production rate is a hallmark that allows us the possibility of prognosis of tumor proliferation and invasion capacities, key factors to improve cancer therapy.

Merging bioreactor technology with 3D hepatocyte-fibroblast culturing approaches: Improved in vitro models for toxicological applications.

PubMed

Leite, Sofia B; Teixeira, Ana P; Miranda, Joana P; Tostões, Rui M; Clemente, João J; Sousa, Marcos F; Carrondo, Manuel J T; Alves, Paula M

2011-06-01

During the last years an increasing number of in vitro models have been developed for drug screening and toxicity testing. Primary cultures of hepatocytes are, by far, the model of choice for those high-throughput studies but their spontaneous dedifferentiation after some time in culture hinders long-term studies. Thus, novel cell culture systems allowing extended hepatocyte maintenance and more predictive long term in vitro studies are required. It has been shown that hepatocytes functionality can be improved and extended in time when cultured as 3D-cell aggregates in environmental controlled stirred bioreactors. In this work, aiming at further improving hepatocytes functionality in such 3D cellular structures, co-cultures with fibroblasts were performed. An inoculum concentration of 1.2×10(5) cell/mL and a 1:2 hepatocyte:mouse embryonic fibroblast ratio allowed to improve significantly the albumin secretion rate and both ECOD (phase I) and UGT (phase II) enzymatic activities in 3D co-cultures, as compared to the routinely used 2D hepatocyte monocultures. Significant improvements were also observed in relation to 3D monocultures of hepatocytes. Furthermore, hepatocytes were able to respond to the addition of beta-Naphtoflavone by increasing ECOD activity showing CYP1A inducibility. The dependence of CYP activity on oxygen concentration was also observed. In summary, the improved hepatocyte specific functions during long term incubation of 3D co-cultures of hepatocytes with fibroblasts indicate that this system is a promising in vitro model for long term toxicological studies. Copyright © 2011 Elsevier Ltd. All rights reserved.

Recent insight into potential acute respiratory distress syndrome.

PubMed

Amin, Zulkifli; Rahmawati, Fitriana N

2017-04-01

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury, characterized by increased pulmonary capillary endothelial cells and alveolar epithelial cells permeability leading to respiratory failure in the absence of cardiac failure. Despite recent advances in treatments, the overall mortality because of ARDS remains high. Biomarkers may help to diagnose, predict the severity, development, and outcome of ARDS in order to improve patient care and decrease morbidity and mortality. This review will focus on soluble receptor for advanced glycation end-products, soluble tumor necrosis factor-receptor 1, Interluken-6 (IL-6), IL-8, and plasminogen activator inhibitor-1, which have a greater potential based on recent studies.

Recent insight into potential acute respiratory distress syndrome

PubMed Central

Amin, Zulkifli; Rahmawati, Fitriana N.

2017-01-01

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury, characterized by increased pulmonary capillary endothelial cells and alveolar epithelial cells permeability leading to respiratory failure in the absence of cardiac failure. Despite recent advances in treatments, the overall mortality because of ARDS remains high. Biomarkers may help to diagnose, predict the severity, development, and outcome of ARDS in order to improve patient care and decrease morbidity and mortality. This review will focus on soluble receptor for advanced glycation end-products, soluble tumor necrosis factor-receptor 1, Interluken-6 (IL-6), IL-8, and plasminogen activator inhibitor-1, which have a greater potential based on recent studies. PMID:28397939

Preoperative predictive model of cervical lymph node metastasis combining fluorine-18 fluorodeoxyglucose positron-emission tomography/computerized tomography findings and clinical factors in patients with oral or oropharyngeal squamous cell carcinoma.

PubMed

Mochizuki, Yumi; Omura, Ken; Nakamura, Shin; Harada, Hiroyuki; Shibuya, Hitoshi; Kurabayashi, Toru

2012-02-01

This study aimed to construct a preoperative predictive model of cervical lymph node metastasis using fluorine-18 fluorodeoxyglucose positron-emission tomography/computerized tomography ((18)F-FDG PET/CT) findings in patients with oral or oropharyngeal squamous cell carcinoma (SCC). Forty-nine such patients undergoing preoperative (18)F-FDG PET/CT and neck dissection or lymph node biopsy were enrolled. Retrospective comparisons with spatial correlation between PET/CT and the anatomical sites based on histopathological examinations of surgical specimens were performed. We calculated a logistic regression model, including the SUVmax-related variable. When using the optimal cutoff point criterion of probabilities calculated from the model that included either clinical factors and delayed-phase SUVmax ≥0.087 or clinical factors and maximum standardized uptake (SUV) increasing rate (SUV-IR) ≥ 0.100, it significantly increased the sensitivity, specificity, and accuracy (87.5%, 65.7%, and 75.2%, respectively). The use of predictive models that include clinical factors and delayed-phase SUVmax and SUV-IR improve preoperative nodal diagnosis. Copyright © 2012 Elsevier Inc. All rights reserved.

Comprehensive Micromechanics-Analysis Code - Version 4.0

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Bednarcyk, B. A.

2005-01-01

Version 4.0 of the Micromechanics Analysis Code With Generalized Method of Cells (MAC/GMC) has been developed as an improved means of computational simulation of advanced composite materials. The previous version of MAC/GMC was described in "Comprehensive Micromechanics-Analysis Code" (LEW-16870), NASA Tech Briefs, Vol. 24, No. 6 (June 2000), page 38. To recapitulate: MAC/GMC is a computer program that predicts the elastic and inelastic thermomechanical responses of continuous and discontinuous composite materials with arbitrary internal microstructures and reinforcement shapes. The predictive capability of MAC/GMC rests on a model known as the generalized method of cells (GMC) - a continuum-based model of micromechanics that provides closed-form expressions for the macroscopic response of a composite material in terms of the properties, sizes, shapes, and responses of the individual constituents or phases that make up the material. Enhancements in version 4.0 include a capability for modeling thermomechanically and electromagnetically coupled ("smart") materials; a more-accurate (high-fidelity) version of the GMC; a capability to simulate discontinuous plies within a laminate; additional constitutive models of materials; expanded yield-surface-analysis capabilities; and expanded failure-analysis and life-prediction capabilities on both the microscopic and macroscopic scales.

A risk score including microdeletions improves relapse prediction for standard and medium risk precursor B-cell acute lymphoblastic leukaemia in children.

PubMed

Sutton, Rosemary; Venn, Nicola C; Law, Tamara; Boer, Judith M; Trahair, Toby N; Ng, Anthea; Den Boer, Monique L; Dissanayake, Anuruddhika; Giles, Jodie E; Dalzell, Pauline; Mayoh, Chelsea; Barbaric, Draga; Revesz, Tamas; Alvaro, Frank; Pieters, Rob; Haber, Michelle; Norris, Murray D; Schrappe, Martin; Dalla Pozza, Luciano; Marshall, Glenn M

2018-02-01

To prevent relapse, high risk paediatric acute lymphoblastic leukaemia (ALL) is treated very intensively. However, most patients who eventually relapse have standard or medium risk ALL with low minimal residual disease (MRD) levels. We analysed recurrent microdeletions and other clinical prognostic factors in a cohort of 475 uniformly treated non-high risk precursor B-cell ALL patients with the aim of better predicting relapse and refining risk stratification. Lower relapse-free survival at 7 years (RFS) was associated with IKZF1 intragenic deletions (P < 0·0001); P2RY8-CRLF2 gene fusion (P < 0·0004); Day 33 MRD>5 × 10 -5 (P < 0·0001) and High National Cancer Institute (NCI) risk (P < 0·0001). We created a predictive model based on a risk score (RS) for deletions, MRD and NCI risk, extending from an RS of 0 (RS0) for patients with no unfavourable factors to RS2 +  for patients with 2 or 3 high risk factors. RS0, RS1, and RS2 +  groups had RFS of 93%, 78% and 49%, respectively, and overall survival (OS) of 99%, 91% and 71%. The RS provided greater discrimination than MRD-based risk stratification into standard (89% RFS, 96% OS) and medium risk groups (79% RFS, 91% OS). We conclude that this RS may enable better early therapeutic stratification and thus improve cure rates for childhood ALL. © 2017 John Wiley & Sons Ltd.

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

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

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

Experimental hookworm infection and gluten microchallenge promote tolerance in celiac disease.

PubMed

Croese, John; Giacomin, Paul; Navarro, Severine; Clouston, Andrew; McCann, Leisa; Dougall, Annette; Ferreira, Ivana; Susianto, Atik; O'Rourke, Peter; Howlett, Mariko; McCarthy, James; Engwerda, Christian; Jones, Dianne; Loukas, Alex

2015-02-01

Celiac disease (CeD) is a common gluten-sensitive autoimmune enteropathy. A gluten-free diet is an effective treatment, but compliance is demanding; hence, new treatment strategies for CeD are required. Parasitic helminths hold promise for treating inflammatory disorders, so we examined the influence of experimental hookworm infection on the predicted outcomes of escalating gluten challenges in CeD subjects. A 52-week study was conducted involving 12 adults with diet-managed CeD. Subjects were inoculated with 20 Necator americanus larvae, and escalating gluten challenges consumed as pasta were subsequently administered: (1) 10 to 50 mg for 12 weeks (microchallenge); (2) 25 mg daily + 1 g twice weekly for 12 weeks (GC-1g); and (3) 3 g daily (60-75 straws of spaghetti) for 2 weeks (GC-3g). Symptomatic, serologic, and histological outcomes evaluated gluten toxicity. Regulatory and inflammatory T cell populations in blood and mucosa were examined. Two gluten-intolerant subjects were withdrawn after microchallenge. Ten completed GC-1g, 8 of whom enrolled in and completed GC-3g. median villous height-to-crypt depth ratios (2.60-2.63; P = .98) did not decrease as predicted after GC-1g, and the mean IgA-tissue transglutaminase titers declined, contrary to the predicted rise after GC-3g. quality of life scores improved (46.3-40.6; P = .05); celiac symptom indices (24.3-24.3; P = .53), intra-epithelial lymphocyte percentages (32.5-35.0; P = .47), and Marsh scores were unchanged by gluten challenge. Intestinal T cells expressing IFNγ were reduced following hookworm infection (23.9%-11.5%; P = .04), with corresponding increases in CD4(+) Foxp3(+) regulatory T cells (0.19%-1.12%; P = .001). Necator americanus and gluten microchallenge promoted tolerance and stabilized or improved all tested indices of gluten toxicity in CeD subjects. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

In situ immune response after neoadjuvant chemotherapy for breast cancer predicts survival.

PubMed

Ladoire, Sylvain; Mignot, Grégoire; Dabakuyo, Sandrine; Arnould, Laurent; Apetoh, Lionel; Rébé, Cedric; Coudert, Bruno; Martin, Francois; Bizollon, Marie Hélène; Vanoli, André; Coutant, Charles; Fumoleau, Pierre; Bonnetain, Franck; Ghiringhelli, François

2011-07-01

Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. However, the predictive value of tumour-infiltrating lymphocytes after neoadjuvant chemotherapy for breast cancer remains unknown. We hypothesized that the nature of the immune infiltrate following neoadjuvant chemotherapy would predict patient survival. In a series of 111 consecutive HER2- and a series of 51 non-HER2-overexpressing breast cancer patients treated by neoadjuvant chemotherapy, we studied by immunohistochemistry tumour infiltration by FOXP3 and CD8 T lymphocytes before and after chemotherapy. Kaplan-Meier analysis and Cox modelling were used to assess relapse-free survival (RFS) and overall survival (OS). A predictive scoring system using American Joint Committee on Cancer (AJCC) pathological staging and immunological markers was created. Association of high CD8 and low FOXP3 cell infiltrates after chemotherapy was significantly associated with improved RFS (p = 0.02) and OS (p = 0.002), and outperformed classical predictive factors in multivariate analysis. A combined score associating CD8/FOXP3 ratio and pathological AJCC staging isolated a subgroup of patients with a long-term overall survival of 100%. Importantly, this score also identified patients with a favourable prognosis in an independent cohort of HER2-negative breast cancer patients. These results suggest that immunological CD8 and FOXP3 cell infiltrate after treatment is an independent predictive factor of survival in breast cancer patients treated with neoadjuvant chemotherapy and provides new insights into the role of the immune milieu and cancer. Copyright © 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Coastal geomorphology through the looking glass

NASA Astrophysics Data System (ADS)

Sherman, Douglas J.; Bauer, Bernard O.

1993-07-01

Coastal geomorphology will gain future prominence as environmentally sound coastal zone management strategies, requiring scientific information, begin to supplant engineered shoreline stabilization schemes for amelioration of coastal hazards. We anticipate substantial change and progress over the next two decades, but we do not predict revolutionary advances in theoretical understanding of coastal geomorphic systems. Paradigm shifts will not occur; knowledge will advance incrementally. We offer predictions for specific coastal systems delineated according to scale. For the surf zone, we predict advances in wave shoaling theory, but not for wave breaking. We also predict greater understanding of turbulent processes, and substantive improvements in surf-zone circulation and radiation stress models. Very few of these improvements are expected to be incorporated in geomorphic models of coastal processes. We do not envision improvements in the theory of sediment transport, although some new and exciting empirical observations are probable. At the beach and nearshore scale, we predict the development of theoretically-based, two- and three-dimensional morphodynamical models that account for non-linear, time-dependent feedback processes using empirically calibrated modules. Most of the geomorphic research effort, however, will be concentrated at the scale of littoral cells. This scale is appropriate for coastal zone management because processes at this scale are manageable using traditional geomorphic techniques. At the largest scale, little advance will occur in our understanding of how coastlines evolve. Any empirical knowledge that is gained will accrue indirectly. Finally, we contend that anthropogenic influences, directly and indirectly, will be powerful forces in steering the future of Coastal Geomorphology. "If you should suddenly feel the need for a lesson in humility, try forecasting the future…" (Kleppner, 1991, p. 10).

FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences

PubMed Central

Minneci, Federico; Piovesan, Damiano; Cozzetto, Domenico; Jones, David T.

2013-01-01

To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information – either experimentally validated or computationally inferred by similarity – remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy. PMID:23717476

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

PubMed Central

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

PubMed

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Volume holographic lens spectrum-splitting photovoltaic system for high energy yield with direct and diffuse solar illumination

NASA Astrophysics Data System (ADS)

Chrysler, Benjamin D.; Wu, Yuechen; Yu, Zhengshan; Kostuk, Raymond K.

2017-08-01

In this paper a prototype spectrum-splitting photovoltaic system based on volume holographic lenses (VHL) is designed, fabricated and tested. In spectrum-splitting systems, incident sunlight is divided in spectral bands for optimal conversion by a set of single-junction PV cells that are laterally separated. The VHL spectrumsplitting system in this paper has a form factor similar to conventional silicon PV modules but with higher efficiencies (>30%). Unlike many other spectrum-splitting systems that have been proposed in the past, the system in this work converts both direct and diffuse sunlight while using inexpensive 1-axis tracking systems. The VHL system uses holographic lenses that focus light at a transition wavelength to the boundary between two PV cells. Longer wavelength light is dispersed to the narrow bandgap cell and shorter wavelength light to the wide bandgap cell. A prototype system is designed with silicon and GaAs PV cells. The holographic lenses are fabricated in Covestro Bayfol HX photopolymer by `stitching' together lens segments through sequential masked exposures. The PV cells and holographic lenses were characterized and the data was used in a raytrace simulation and predicts an improvement in total power output of 15.2% compared to a non-spectrum-splitting reference. A laboratory measurement yielded an improvement in power output of 8.5%.

Pheromone-Induced Morphogenesis Improves Osmoadaptation Capacity by Activating the HOG MAPK Pathway**

PubMed Central

Baltanás, Rodrigo; Bush, Alan; Couto, Alicia; Durrieu, Lucía; Hohmann, Stefan; Colman-Lerner, Alejandro

2013-01-01

Environmental and internal conditions expose cells to a multiplicity of stimuli whose consequences are difficult to predict. Here, we investigate the response to mating pheromone of yeast cells adapted to high osmolarity. Events downstream of pheromone binding involve two mitogen-activated protein kinase (MAPK) cascades: the pheromone response (PR) and the cell-wall integrity response (CWI). Although these MAPK pathways share components with each and a third MAPK pathway, the high osmolarity response (HOG), they are normally only activated by distinct stimuli, a phenomenon called insulation. We found that in cells adapted to high osmolarity, PR activated the HOG pathway in a pheromone- and osmolarity- dependent manner. Activation of HOG by the PR was not due to loss of insulation, but rather a response to a reduction in internal osmolarity, which resulted from an increase in glycerol release caused by the PR. By analyzing single-cell time courses, we found that stimulation of HOG occurred in discrete bursts that coincided with the “shmooing” morphogenetic process. Activation required the polarisome, the cell wall integrity MAPK Slt2, and the aquaglyceroporin Fps1. HOG activation resulted in high glycerol turnover that improved adaptability to rapid changes in osmolarity. Our work shows how a differentiation signal can recruit a second, unrelated sensory pathway to enable responses to yeast to multiple stimuli. PMID:23612707

Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates

USGS Publications Warehouse

Patino, Reynaldo; VanLandeghem, Matthew M.; Denny, Shawn

2016-01-01

Golden alga (Prymnesium parvum) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from ∼ 80% to ∼ 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

PubMed

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

2017-01-09

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people.

PubMed

Molinaro, Annette M; Ferrucci, Leah M; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J; Bale, Allen E; Mayne, Susan T

2015-06-01

Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

Study to determine and improve design for lithium-doped solar cells

NASA Technical Reports Server (NTRS)

Brucker, G.; Faith, T. J.; Holmes-Siedle, A.

1971-01-01

Solar cell experiments show that a single lithium density parameter, the lithium density gradient, calculated from nondestructive capacitance measurements, provides the basis for accurate predictions of lithium cell behavior in a 1-MeV electron environment for fluences ranging between 3 X 10 to the 13th power e/sq cm and 3 X 10 to the 15th power/e sq cm. The oxygen-rich (quartz crucible) lithium cell with phosphorous starting dopant and lithium gradient between approximately 5 X 10 to the 18th power and 1.5 x 10 to the 19th power/cm to the 4th power was found superior in performance to the commercial 10 ohm-cm n/p control cells. Post-recovery stability of oxygen-rich cells was satisfactory. An average post-recovery current drop of approximately 1 mA was observed for 70 crucible cells after 1 year-equivalent storage time at 80 C. In contrast the oxygen-poor (float zone and Lopex) lithium cells displayed spotty initial performance and stability problems at room temperature.

Laminins and cancer stem cells: Partners in crime?

PubMed

Qin, Yan; Rodin, Sergey; Simonson, Oscar E; Hollande, Frédéric

2017-08-01

As one of the predominant protein families within the extracellular matrix both structurally and functionally, laminins have been shown to be heavily involved in tumor progression and drug resistance. Laminins participate in key cellular events for tumor angiogenesis, cell invasion and metastasis development, including the regulation of epithelial-mesenchymal transition and basement membrane remodeling, which are tightly associated with the phenotypic characteristics of stem-like cells, particularly in the context of cancer. In addition, a great deal of studies and reports has highlighted the critical roles of laminins in modulating stem cell phenotype and differentiation, as part of the stem cell niche. Stemming from these discoveries a growing body of literature suggests that laminins may act as regulators of cancer stem cells, a tumor cell subpopulation that plays an instrumental role in long-term cancer maintenance, metastasis development and therapeutic resistance. The accumulating evidence in this emerging research area suggests that laminins represent potential therapeutic targets for anti-cancer treatments against cancer stem cells, and that they may be used as predictive and prognostic markers to inform clinical management and improve patient survival. Copyright © 2016 Elsevier Ltd. All rights reserved.

Early recovery of T-cell function predicts improved survival after T-cell depleted allogeneic transplant.

PubMed

Goldberg, Jenna D; Zheng, Junting; Ratan, Ravin; Small, Trudy N; Lai, Kuan-Chi; Boulad, Farid; Castro-Malaspina, Hugo; Giralt, Sergio A; Jakubowski, Ann A; Kernan, Nancy A; O'Reilly, Richard J; Papadopoulos, Esperanza B; Young, James W; van den Brink, Marcel R M; Heller, Glenn; Perales, Miguel-Angel

2017-08-01

Infection, relapse, and GVHD can complicate allogeneic hematopoietic stem cell transplantation (allo-HSCT). Although the effect of poor immune recovery on infection risk is well-established, there are limited data on the effect of immune reconstitution on relapse and survival, especially following T-cell depletion (TCD). To characterize the pattern of immune reconstitution in the first year after transplant and its effects on survival and relapse, we performed a retrospective study in 375 recipients of a myeloablative TCD allo-HSCT for hematologic malignancies. We noted that different subsets recover sequentially, CD8 + T cells first, followed by total CD4 + and naïve CD4 + T cells, indicating thymic recovery during the first year after HSCT. In the multivariate model, a fully HLA-matched donor and recovery of T-cell function, assessed by PHA response at 6 months, were the only factors independently associated with OS and EFS. In conclusion, T-cell recovery is an important predictor of outcome after TCD allo-HSCT.

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

PubMed Central

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

2016-01-01

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

Prospective study of automated versus manual annotation of early time-lapse markers in the human preimplantation embryo.

PubMed

Kaser, Daniel J; Farland, Leslie V; Missmer, Stacey A; Racowsky, Catherine

2017-08-01

How does automated time-lapse annotation (Eeva™) compare to manual annotation of the same video images performed by embryologists certified in measuring durations of the 2-cell (P2; time to the 3-cell minus time to the 2-cell, or t3-t2) and 3-cell (P3; time to 4-cell minus time to the 3-cell, or t4-t3) stages? Manual annotation was superior to the automated annotation provided by Eeva™ version 2.2, because manual annotation assigned a rating to a higher proportion of embryos and yielded a greater sensitivity for blastocyst prediction than automated annotation. While use of the Eeva™ test has been shown to improve an embryologist's ability to predict blastocyst formation compared to Day 3 morphology alone, the accuracy of the automated image analysis employed by the Eeva™ system has never been compared to manual annotation of the same time-lapse markers by a trained embryologist. We conducted a prospective cohort study of embryos (n = 1477) cultured in the Eeva™ system (n = 8 microscopes) at our institution from August 2014 to February 2016. Embryos were assigned a blastocyst prediction rating of High (H), Medium (M), Low (L), or Not Rated (NR) by Eeva™ version 2.2 according to P2 and P3. An embryologist from a team of 10, then manually annotated each embryo and if the automated and manual ratings differed, a second embryologist independently annotated the embryo. If both embryologists disagreed with the automated Eeva™ rating, then the rating was classified as discordant. If the second embryologist agreed with the automated Eeva™ score, the rating was not considered discordant. Spearman's correlation (ρ), weighted kappa statistics and the intra-class correlation (ICC) coefficients with 95% confidence intervals (CI) between Eeva™ and manual annotation were calculated, as were the proportions of discordant embryos, and the sensitivity, specificity, positive predictive value (PPV) and NPV of each method for blastocyst prediction. The distribution of H, M and L ratings differed by annotation method (P < 0.0001). The correlation between Eeva™ and manual annotation was higher for P2 (ρ = 0.75; ICC = 0.82; 95% CI 0.82-0.83) than for P3 (ρ = 0.39; ICC = 0.20; 95% CI 0.16-0.26). Eeva™ was more likely than an embryologist to rate an embryo as NR (11.1% vs. 3.0%, P < 0.0001). Discordance occurred in 30.0% (443/1477) of all embryos and was not associated with factors such as Day 3 cell number, fragmentation, symmetry or presence of abnormal cleavage. Rather, discordance was associated with direct cleavage (P2 ≤ 5 h) and short P3 (≤0.25 h), and also factors intrinsic to the Eeva™ system, such as the automated rating (proportion of discordant embryos by rating: H: 9.3%; M: 18.1%; L: 41.3%; NR: 31.4%; P < 0.0001), microwell location (peripheral: 31.2%; central: 23.8%; P = 0.02) and Eeva™ microscope (n = 8; range 22.9-42.6%; P < 0.0001). Manual annotation upgraded 82.6% of all discordant embryos from a lower to a higher rating, and improved the sensitivity for predicting blastocyst formation. One team of embryologists performed the manual annotations; however, the study staff was trained and certified by the company sponsor. Only two time-lapse markers were evaluated, so the results are not generalizable to other parameters; likewise, the results are not generalizable to future versions of Eeva™ or other automated image analysis systems. Based on the proportion of discordance and the improved performance of manual annotation, clinics using the Eeva™ system should consider manual annotation of P2 and P3 to confirm the automated ratings generated by Eeva™. These data were acquired in a study funded by Progyny, Inc. There are no competing interests. N/A. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

Enzyme clustering accelerates processing of intermediates through metabolic channeling

PubMed Central

Castellana, Michele; Wilson, Maxwell Z.; Xu, Yifan; Joshi, Preeti; Cristea, Ileana M.; Rabinowitz, Joshua D.; Gitai, Zemer; Wingreen, Ned S.

2015-01-01

We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation. PMID:25262299

Upper tract urothelial recurrence following radical cystectomy for transitional cell carcinoma of the bladder: an analysis of 1,069 patients with 10-year followup.

PubMed

Sanderson, Kristin M; Cai, Jie; Miranda, Gustavo; Skinner, Donald G; Stein, John P

2007-06-01

Risk factors for upper tract recurrence following radical cystectomy for transitional cell carcinoma of the bladder are not yet well-defined. We reviewed our population of patients who underwent radical cystectomy to identify prognostic factors and clinical outcomes associated with upper tract recurrence. From our prospective database of 1,359 patients who underwent radical cystectomy we identified 1,069 patients treated for transitional cell carcinoma of the bladder between January 1985 and December 2001. Univariate analysis was completed to determine factors predictive of upper tract recurrence. A total of 853 men and 216 women were followed for a median of 10.3 years (maximum 18.5). There were 27 (2.5%) upper tract recurrences diagnosed at a median of 3.3 years (range 0.4 to 9.3). Only urethral tumor involvement was predictive of upper tract recurrence. In men superficial transitional cell carcinoma of the prostatic urethra was associated with an increased risk of upper tract recurrence compared with prostatic stromal invasion or absence of prostatic transitional cell carcinoma (p <0.01). In women urethral transitional cell carcinoma was associated with an increased risk of upper tract recurrence (p = 0.01). Despite routine surveillance 78% of upper tract recurrence was detected after development of symptoms. Median survival following upper tract recurrence was 1.7 years (range 0.2 to 8.8). Detection of asymptomatic upper tract recurrence via surveillance did not predict lower nephroureterectomy tumor stage, absence of lymph node metastases or improved survival. Patients with bladder cancer are at lifelong risk for late oncological recurrence in the upper tract urothelium. Patients with evidence of tumor involvement within the urethra are at highest risk. Surveillance regimens frequently fail to detect tumors before symptoms develop. However, radical nephroureterectomy can provide prolonged survival.

Modelling bloom formation of the toxic dinoflagellates Dinophysis acuminata and Dinophysis caudata in a highly modified estuary, south eastern Australia

NASA Astrophysics Data System (ADS)

Ajani, Penelope; Larsson, Michaela E.; Rubio, Ana; Bush, Stephen; Brett, Steve; Farrell, Hazel

2016-12-01

Dinoflagellates belonging to the toxigenic genus Dinophysis are increasing in abundance in the Hawkesbury River, south-eastern Australia. This study investigates a twelve year time series of abundance and physico-chemical data to model these blooms. Four species were reported over the sampling campaign - Dinophysis acuminata, Dinophysis caudata, Dinophysis fortii and Dinophysis tripos-with D. acuminata and D. caudata being most abundant. Highest abundance of D. acuminata occurred in the austral spring (max. abundance 4500 cells l-1), whilst highest D. caudata occurred in the summer to autumn (max. 12,000 cells l-1). Generalised additive models revealed abundance of D. acuminata was significantly linked to season, thermal stratification and nutrients, whilst D. caudata was associated with nutrients, salinity and dissolved oxygen. The models' predictive capability was up to 60% for D. acuminata and 53% for D. caudata. Altering sampling strategies during blooms accompanied with in situ high resolution monitoring will further improve Dinophysis bloom prediction capability.

Biomarkers of head and neck cancer, tools or a gordian knot?

PubMed Central

Lampri, Evangeli S; Chondrogiannis, Georgios; Ioachim, Elli; Varouktsi, Anna; Mitselou, Antigoni; Galani, Aggeliki; Briassoulis, Evangelos; Kanavaros, Panagiotis; Galani, Vasiliki

2015-01-01

Head and neck tumors comprise a wide spectrum of heterogeneous neoplasms for which biomarkers are needed to aid in earlier diagnosis, risk assessment and therapy response. Personalized medicine based on predictive markers linked to drug response, it is hoped, will lead to improvements in outcomes and avoidance of unnecessary treatment in carcinoma of the head and neck. Because of the heterogeneity of head and neck tumors, the integration of multiple selected markers in association with the histopathologic features is advocated for risk assessment. Validation of each biomarker in the context of clinical trials will be required before a specific marker can be incorporated into daily practice. Furthermore, we will give evidence that some proteins implicated in cell-cell interaction, such as CD44 may be involved in the multiple mechanism of the development and progression of laryngeal lesions and may help to predict the risk of transformation of the benign or precancerous lesions to cancer. PMID:26379825

Engineered Emitters for Improved Silicon Photovoltaics

NASA Astrophysics Data System (ADS)

Kamat, Ronak A.

In 2014, installation of 5.3GW of new Photovoltaic (PV) systems occurred in the United States, raising the total installed capacity to 16.36GW. Strong growth is predicted for the domestic PV market with analysts reporting goals of 696GW by 2020. Conventional single crystalline silicon cells are the technology of choice, accounting for 90% of the installations in the global commercial market. Cells made of GaAs offer higher efficiencies, but at a substantially higher cost. Thin film technologies such as CIGS and CdTe compete favorably with multi-crystalline Si (u-Si), but at 20% efficiency, still lag the c-Si cell in performance. The c-Si cell can be fabricated to operate at approximately 25% efficiency, but commercially the efficiencies are in the 18-21% range, which is a direct result of cost trade-offs between process complexity and rapid throughput. With the current cost of c-Si cell modules at nearly 0.60/W. The technology is well below the historic metric of 1/W for economic viability. The result is that more complex processes, once cost-prohibitive, may now be viable. An example is Panasonic's HIT cell which operates in the 22-24% efficiency range. To facilitate research and development of novel PV materials and techniques, RIT has developed a basic solar cell fabrication process. Student projects prior to this work had produced cells with 12.8% efficiency using p type substrates. This thesis reports on recent work to improve cell efficiencies while simultaneously expanding the capability of the rapid prototyping process. In addition to the p-Si substrates, cells have been produced using n-Si substrates. The cell emitter, which is often done with a single diffusion or implant has been re-engineered using a dual implant of the same dose. This dual-implanted emitter has been shown to lower contact resistance, increase Voc, and increase the efficiency. A p-Si substrate cell has been fabricated with an efficiency of 14.6% and n-Si substrate cell with a 13.5% efficiency. Further improvements could be made through the incorporation of a front-surface field, surface texturing and nitride ARC.

Perioperative detection of circulating tumour cells in patients with lung cancer.

PubMed

Chudasama, Dimple; Burnside, Nathan; Beeson, Julie; Karteris, Emmanouil; Rice, Alexandra; Anikin, Vladimir

2017-08-01

Lung cancer is a leading cause of mortality and despite surgical resection a proportion of patients may develop metastatic spread. The detection of circulating tumour cells (CTCs) may allow for improved prediction of metastatic spread and survival. The current study evaluates the efficacy of the ScreenCell® filtration device, to capture, isolate and propagate CTCs in patients with primary lung cancer. Prior to assessment of CTCs, the present study detected cancer cells in a proof-of-principle- experiment using A549 human lung carcinoma cells as a model. Ten patients (five males and five females) with pathologically diagnosed primary non-small cell lung cancer undergoing surgical resection, had their blood tested for CTCs. Samples were taken from a peripheral vessel at the baseline, from the pulmonary vein draining the lobe containing the tumour immediately prior to division, a further central sample was taken following completion of the resection, and a final peripheral sample was taken three days post-resection. A significant increase in CTCs was observed from baseline levels following lung manipulation. No association was able to be made between increased levels of circulating tumour cells and survival or the development of metastatic deposits. Manipulation of the lung during surgical resection for non-small cell lung carcinoma results in a temporarily increased level of CTCs; however, no clinical impact for this increase was observed. Overall, the study suggests the ScreenCell® device has the potential to be used as a CTC isolation tool, following further work, adaptations and improvements to the technology and validation of results.

Modeling the Hydraulics of Root Growth in Three Dimensions with Phloem Water Sources1[C][OA

PubMed Central

Wiegers, Brandy S.; Cheer, Angela Y.; Silk, Wendy K.

2009-01-01

Primary growth is characterized by cell expansion facilitated by water uptake generating hydrostatic (turgor) pressure to inflate the cell, stretching the rigid cell walls. The multiple source theory of root growth hypothesizes that root growth involves transport of water both from the soil surrounding the growth zone and from the mature tissue higher in the root via phloem and protophloem. Here, protophloem water sources are used as boundary conditions in a classical, three-dimensional model of growth-sustaining water potentials in primary roots. The model predicts small radial gradients in water potential, with a significant longitudinal gradient. The results improve the agreement of theory with empirical studies for water potential in the primary growth zone of roots of maize (Zea mays). A sensitivity analysis quantifies the functional importance of apical phloem differentiation in permitting growth and reveals that the presence of phloem water sources makes the growth-sustaining water relations of the root relatively insensitive to changes in root radius and hydraulic conductivity. Adaptation to drought and other environmental stresses is predicted to involve more apical differentiation of phloem and/or higher phloem delivery rates to the growth zone. PMID:19542299

Cancer Systems Biology: a peak into the future of patient care?

PubMed Central

Werner, Henrica M. J.; Mills, Gordon B.; Ram, Prahlad T.

2015-01-01

Traditionally, scientific research has focused on studying individual events, such as single mutations, gene function or the effect of the manipulation of one protein on a biological phenotype. A range of technologies, combined with the ability to develop robust and predictive mathematical models, is beginning to provide information that will enable a holistic view of how the genomic and epigenetic aberrations in cancer cells can alter the homeostasis of signalling networks within these cells, between cancer cells and the local microenvironment, at the organ and organism level. This systems biology process needs to be integrated with an iterative approach wherein hypotheses and predictions that arise from modelling are refined and constrained by experimental evaluation. Systems biology approaches will be vital for developing and implementing effective strategies to deliver personalized cancer therapy. Specifically, these approaches will be important to select those patients most likely to benefit from targeted therapies as well as for the development and implementation of rational combinatorial therapies. Systems biology can help to increase therapy efficacy or bypass the emergence of resistance, thus converting the current (often short term) effects of targeted therapies into durable responses, ultimately to improve quality of life and provide a cure. PMID:24492837

Nonimmediate hypersensitivity reactions to iodinated contrast media.

PubMed

Gómez, Enrique; Ariza, Adriana; Blanca-López, Natalia; Torres, Maria J

2013-08-01

To provide a detailed analysis of the latest findings on the mechanisms underlying the nonimmediate reactions to iodinated contrast media and comment on the recent advances in diagnosis, focusing on the roles of the skin test, drug provocation test (DPT), and lymphocyte transformation test (LTT). Several studies have reported new findings supporting an important role for T-lymphocytes in the nonimmediate reactions to iodinated contrast media. The LTT has been used as an in-vitro tool for diagnosis, but with variable results. However, the inclusion of autologous monocyte-derived dendritic cells as professional antigen-presenting cells has improved the sensitivity of this test. Regarding in-vivo diagnosis, although skin testing has been routine, it has now been shown that its sensitivity and negative predictive value are low. Recent studies have demonstrated that the DPT is a well tolerated and useful procedure that is necessary to confirm the diagnosis of nonimmediate hypersensitivity reactions to iodinated contrast media. Nonimmediate reactions to contrast media are usually T-cell mediated. Diagnosis is based on skin testing, although its sensitivity and negative predictive value are not optimal. Consequently, drug provocation testing is often needed to confirm the diagnosis and also to seek alternative contrast media that can be tolerated.

Cancer stem cells: a systems biology view of their role in prognosis and therapy.

PubMed

Mertins, Susan D

2014-04-01

Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSCs). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of CSCs can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes.

Modeling the hydraulics of root growth in three dimensions with phloem water sources.

PubMed

Wiegers, Brandy S; Cheer, Angela Y; Silk, Wendy K

2009-08-01

Primary growth is characterized by cell expansion facilitated by water uptake generating hydrostatic (turgor) pressure to inflate the cell, stretching the rigid cell walls. The multiple source theory of root growth hypothesizes that root growth involves transport of water both from the soil surrounding the growth zone and from the mature tissue higher in the root via phloem and protophloem. Here, protophloem water sources are used as boundary conditions in a classical, three-dimensional model of growth-sustaining water potentials in primary roots. The model predicts small radial gradients in water potential, with a significant longitudinal gradient. The results improve the agreement of theory with empirical studies for water potential in the primary growth zone of roots of maize (Zea mays). A sensitivity analysis quantifies the functional importance of apical phloem differentiation in permitting growth and reveals that the presence of phloem water sources makes the growth-sustaining water relations of the root relatively insensitive to changes in root radius and hydraulic conductivity. Adaptation to drought and other environmental stresses is predicted to involve more apical differentiation of phloem and/or higher phloem delivery rates to the growth zone.

Sub-50-nm self-assembled nanotextures for enhanced broadband antireflection in silicon solar cells.

PubMed

Rahman, Atikur; Ashraf, Ahsan; Xin, Huolin; Tong, Xiao; Sutter, Peter; Eisaman, Matthew D; Black, Charles T

2015-01-21

Materials providing broadband light antireflection have applications as highly transparent window coatings, military camouflage, and coatings for efficiently coupling light into solar cells and out of light-emitting diodes. In this work, densely packed silicon nanotextures with feature sizes smaller than 50 nm enhance the broadband antireflection compared with that predicted by their geometry alone. A significant fraction of the nanotexture volume comprises a surface layer whose optical properties differ substantially from those of the bulk, providing the key to improved performance. The nanotexture reflectivity is quantitatively well-modelled after accounting for both its profile and changes in refractive index at the surface. We employ block copolymer self-assembly for precise and tunable nanotexture design in the range of ~10-70 nm across macroscopic solar cell areas. Implementing this efficient antireflection approach in crystalline silicon solar cells significantly betters the performance gain compared with an optimized, planar antireflection coating.

Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (Phospho)Proteomic Profiling.

PubMed

Singec, Ilyas; Crain, Andrew M; Hou, Junjie; Tobe, Brian T D; Talantova, Maria; Winquist, Alicia A; Doctor, Kutbuddin S; Choy, Jennifer; Huang, Xiayu; La Monaca, Esther; Horn, David M; Wolf, Dieter A; Lipton, Stuart A; Gutierrez, Gustavo J; Brill, Laurence M; Snyder, Evan Y

2016-09-13

Controlled differentiation of human embryonic stem cells (hESCs) can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs). This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites) provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families), phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt. Published by Elsevier Inc.

An efficient descriptor model for designing materials for solar cells

NASA Astrophysics Data System (ADS)

Alharbi, Fahhad H.; Rashkeev, Sergey N.; El-Mellouhi, Fedwa; Lüthi, Hans P.; Tabet, Nouar; Kais, Sabre

2015-11-01

An efficient descriptor model for fast screening of potential materials for solar cell applications is presented. It works for both excitonic and non-excitonic solar cells materials, and in addition to the energy gap it includes the absorption spectrum (α(E)) of the material. The charge transport properties of the explored materials are modelled using the characteristic diffusion length (Ld) determined for the respective family of compounds. The presented model surpasses the widely used Scharber model developed for bulk heterojunction solar cells. Using published experimental data, we show that the presented model is more accurate in predicting the achievable efficiencies. To model both excitonic and non-excitonic systems, two different sets of parameters are used to account for the different modes of operation. The analysis of the presented descriptor model clearly shows the benefit of including α(E) and Ld in view of improved screening results.

Liquid Water Saturation and Oxygen Transport Resistance in Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers

NASA Astrophysics Data System (ADS)

Muirhead, Daniel

In this thesis, the relative humidity (RH) of the cathode reactant gas was investigated as a factor which influences gas diffusion layer (GDL) liquid water accumulation and mass transport-related efficiency losses over a range of operating current densities in a polymer electrolyte membrane (PEM) fuel cell. Limiting current measurements were used to characterize fuel cell oxygen transport resistance while simultaneous measurements of liquid water accumulation were conducted using synchrotron X-ray radiography. GDL porosity distributions were characterized with micro-computed tomography (microCT). The work presented here can be used by researchers to develop improved numerical models to predict GDL liquid water accumulation and to inform the design of next-generation GDL materials to mitigate mass transport-related efficiency losses. This work also contributes an extensive set of concurrent performance and liquid water visualization data to the PEM fuel cell field that can be used for validating multiphase transport models.

Determining the number of fingers in the lifting Hele-Shaw problem

NASA Astrophysics Data System (ADS)

Miranda, Jose; Dias, Eduardo

2013-11-01

The lifting Hele-Shaw cell flow is a variation of the celebrated radial viscous fingering problem for which the upper cell plate is lifted uniformly at a specified rate. This procedure causes the formation of intricate interfacial patterns. Most theoretical studies determine the total number of emerging fingers by maximizing the linear growth rate, but this generates discrepancies between theory and experiments. In this work, we tackle the number of fingers selection problem in the lifting Hele-Shaw cell by employing the recently proposed maximum-amplitude criterion. Our linear stability analysis accounts for the action of capillary, viscous normal stresses, and wetting effects, as well as the cell confinement. The comparison of our results with very precise laboratory measurements for the total number of fingers shows a significantly improved agreement between theoretical predictions and experimental data. We thank CNPq (Brazilian Sponsor) for financial support.

Prediction-based association control scheme in dense femtocell networks

PubMed Central

Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond

2017-01-01

The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992

Investigation of Sb-Containing Precursors for Cu(In, Ga)Se2 Thin Films Through Design of Experiments

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

Mansfield, Lorelle M.; To, Bobby; Reedy, Robert C.

2016-11-21

The Design of Experiments (DoE) module in JMP statistical software was used to determine the best parameters for Sb-containing CIGS precursors with a fixed selenization step. Solar cells were fabricated and measured for all completed films. The most important factor influencing the current-voltage device parameters was identified as the temperature and antimony flux interaction. The DoE prediction profiler and predictive contour plots provided guidance to further improve the device parameters. In one follow-up run, we increased device efficiency from 14.9% to 15.5% Additional gains in efficiency to 16.9% were realized by introducing an intentional Ga gradient and an antireflective coating.

Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.

PubMed

Yu, Bin; Li, Shan; Qiu, Wen-Ying; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

2017-12-08

Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

PubMed Central

Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

2017-01-01

Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research. PMID:29296195

Development of Predictive Energy Management Strategies for Hybrid Electric Vehicles

NASA Astrophysics Data System (ADS)

Baker, David

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods.

Cell survival fraction estimation based on the probability densities of domain and cell nucleus specific energies using improved microdosimetric kinetic models.

PubMed

Sato, Tatsuhiko; Furusawa, Yoshiya

2012-10-01

Estimation of the survival fractions of cells irradiated with various particles over a wide linear energy transfer (LET) range is of great importance in the treatment planning of charged-particle therapy. Two computational models were developed for estimating survival fractions based on the concept of the microdosimetric kinetic model. They were designated as the double-stochastic microdosimetric kinetic and stochastic microdosimetric kinetic models. The former model takes into account the stochastic natures of both domain and cell nucleus specific energies, whereas the latter model represents the stochastic nature of domain specific energy by its approximated mean value and variance to reduce the computational time. The probability densities of the domain and cell nucleus specific energies are the fundamental quantities for expressing survival fractions in these models. These densities are calculated using the microdosimetric and LET-estimator functions implemented in the Particle and Heavy Ion Transport code System (PHITS) in combination with the convolution or database method. Both the double-stochastic microdosimetric kinetic and stochastic microdosimetric kinetic models can reproduce the measured survival fractions for high-LET and high-dose irradiations, whereas a previously proposed microdosimetric kinetic model predicts lower values for these fractions, mainly due to intrinsic ignorance of the stochastic nature of cell nucleus specific energies in the calculation. The models we developed should contribute to a better understanding of the mechanism of cell inactivation, as well as improve the accuracy of treatment planning of charged-particle therapy.

Colony stimulating factor 1 receptor blockade improves the efficacy of chemotherapy against human neuroblastoma in the absence of T lymphocytes.

PubMed

Webb, Matthew W; Sun, Jianping; Sheard, Michael A; Liu, Wei-Yao; Wu, Hong-Wei; Jackson, Jeremy R; Malvar, Jemily; Sposto, Richard; Daniel, Dylan; Seeger, Robert C

2018-04-17

Tumor-associated macrophages can promote growth of cancers. In neuroblastoma, tumor-associated macrophages have greater frequency in metastatic versus loco-regional tumors, and higher expression of genes associated with macrophages helps to predict poor prognosis in the 60% of high-risk patients who have MYCN-non-amplified disease. The contribution of cytotoxic T-lymphocytes to anti-neuroblastoma immune responses may be limited by low MHC class I expression and low exonic mutation frequency. Therefore, we modelled human neuroblastoma in T-cell deficient mice to examine whether depletion of monocytes/macrophages from the neuroblastoma microenvironment by blockade of CSF-1R can improve the response to chemotherapy. In vitro, CSF-1 was released by neuroblastoma cells, and topotecan increased this release. In vivo, neuroblastomas formed by subcutaneous co-injection of human neuroblastoma cells and human monocytes into immunodeficient NOD/SCID mice had fewer human CD14 + and CD163 + cells and mouse F4/80 + cells after CSF-1R blockade. In subcutaneous or intra-renal models in immunodeficient NSG or NOD/SCID mice, CSF-1R blockade alone did not affect tumor growth or mouse survival. However, when combined with cyclophosphamide plus topotecan, the CSF-1R inhibitor BLZ945, either without or with anti-human and anti-mouse CSF-1 mAbs, inhibited neuroblastoma growth and synergistically improved mouse survival. These findings indicate that depletion of tumor-associated macrophages from neuroblastomas can be associated with increased chemotherapeutic efficacy without requiring a contribution from T-lymphocytes, suggesting the possibility that combination of CSF-1R blockade with chemotherapy might be effective in patients who have limited anti-tumor T-cell responses. © 2018 UICC.

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

Acute graft-versus-host disease biomarkers measured during therapy can predict treatment outcomes: a Blood and Marrow Transplant Clinical Trials Network study

PubMed Central

Logan, Brent R.; Wu, Juan; Alousi, Amin M.; Bolaños-Meade, Javier; Ferrara, James L. M.; Ho, Vincent T.; Weisdorf, Daniel J.

2012-01-01

Acute graft-versus-host disease (GVHD) is the primary limitation of allogeneic hematopoietic cell transplantation, and once it develops, there are no reliable diagnostic tests to predict treatment outcomes. We hypothesized that 6 previously validated diagnostic biomarkers of GVHD (IL-2 receptor-α; tumor necrosis factor receptor-1; hepatocyte growth factor; IL-8; elafin, a skin-specific marker; and regenerating islet–derived 3-α, a gastrointestinal tract–specific marker) could discriminate between therapy responsive and nonresponsive patients and predict survival in patients receiving GVHD therapy. We measured GVHD biomarker concentrations from samples prospectively obtained at the initiation of treatment, day 14, and day 28, on a multicenter, randomized, 4-arm phase 2 clinical trial for newly diagnosed acute GVHD. We found that at each of 3 time points, GVHD onset, 2 weeks into treatment, and 4 weeks into treatment, a 6-protein biomarker panel predicted for the important clinical outcomes of day 28 posttherapy nonresponse and mortality at day 180 from onset. GVHD biomarker panels can be used for early identification of patients at high or low risk for treatment nonresponsiveness or death, and they may provide opportunities for early intervention and improved survival after hematopoietic cell transplantation. The study was registered in clinicaltrials.gov as NCT00224874. PMID:22383800

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

PubMed Central

Paramasivam, Nagarajan; Linke, Dirk

2011-01-01

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates

PubMed Central

Yabu, Julie M.; Siebert, Janet C.; Maecker, Holden T.

2016-01-01

Background Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Methods and Findings Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Conclusions Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates. PMID:27078882

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

PubMed

Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

2016-01-01

Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.

Hepatocyte growth factor sensitizes brain tumors to c-MET kinase inhibition

PubMed Central

Zhang, Ying; Farenholtz, Kaitlyn E.; Yang, Yanzhi; Guessous, Fadila; diPierro, Charles G.; Calvert, Valerie S.; Deng, Jianghong; Schiff, David; Xin, Wenjun; Lee, Jae K.; Purow, Benjamin; Christensen, James; Petricoin, Emanuel; Abounader, Roger

2013-01-01

Purpose The receptor tyrosine kinase (RTK) c-MET and its ligand hepatocyte growth factor (HGF) are deregulated and promote malignancy in cancer and brain tumors. Consequently, clinically applicable c-MET inhibitors have been developed. The purpose of this study was to investigate the not well known molecular determinants that predict responsiveness to c-MET inhibitors, and to explore new strategies for improving inhibitor efficacy in brain tumors. Experimental design We investigated the molecular factors and pathway activation signatures that determine sensitivity to c-MET inhibitors in a panel of glioblastoma and medulloblastoma cells, glioblastoma stem cells (GSCs), and established cell line-derived xenografts using functional assays, reverse protein microarrays, and in vivo tumor volume measurements, but validation with animal survival analyses remains to be done. We also explored new approaches for improving the efficacy of the inhibitors in vitro and in vivo. Results We found that HGF co-expression is a key predictor of response to c-MET inhibition among the examined factors, and identified an ERK/JAK/p53 pathway activation signature that differentiates c-MET inhibition in responsive and non-responsive cells. Surprisingly, we also found that short pre-treatment of cells and tumors with exogenous HGF moderately but statistically significantly enhanced the anti-tumor effects of c-MET inhibition. We observed a similar ligand-induced sensitization effect to an EGFR small molecule kinase inhibitor. Conclusions These findings allow the identification of a subset of patients that will be responsive to c-MET inhibition, and propose ligand pre-treatment as a potential new strategy for improving the anti-cancer efficacy of RTK inhibitors. PMID:23386689

Allogeneic Cell Therapy Bioprocess Economics and Optimization: Single-Use Cell Expansion Technologies

PubMed Central

Simaria, Ana S; Hassan, Sally; Varadaraju, Hemanthram; Rowley, Jon; Warren, Kim; Vanek, Philip; Farid, Suzanne S

2014-01-01

For allogeneic cell therapies to reach their therapeutic potential, challenges related to achieving scalable and robust manufacturing processes will need to be addressed. A particular challenge is producing lot-sizes capable of meeting commercial demands of up to 109 cells/dose for large patient numbers due to the current limitations of expansion technologies. This article describes the application of a decisional tool to identify the most cost-effective expansion technologies for different scales of production as well as current gaps in the technology capabilities for allogeneic cell therapy manufacture. The tool integrates bioprocess economics with optimization to assess the economic competitiveness of planar and microcarrier-based cell expansion technologies. Visualization methods were used to identify the production scales where planar technologies will cease to be cost-effective and where microcarrier-based bioreactors become the only option. The tool outputs also predict that for the industry to be sustainable for high demand scenarios, significant increases will likely be needed in the performance capabilities of microcarrier-based systems. These data are presented using a technology S-curve as well as windows of operation to identify the combination of cell productivities and scale of single-use bioreactors required to meet future lot sizes. The modeling insights can be used to identify where future R&D investment should be focused to improve the performance of the most promising technologies so that they become a robust and scalable option that enables the cell therapy industry reach commercially relevant lot sizes. The tool outputs can facilitate decision-making very early on in development and be used to predict, and better manage, the risk of process changes needed as products proceed through the development pathway. Biotechnol. Bioeng. 2014;111: 69–83. © 2013 Wiley Periodicals, Inc. PMID:23893544

Allogeneic cell therapy bioprocess economics and optimization: single-use cell expansion technologies.

PubMed

Simaria, Ana S; Hassan, Sally; Varadaraju, Hemanthram; Rowley, Jon; Warren, Kim; Vanek, Philip; Farid, Suzanne S

2014-01-01

For allogeneic cell therapies to reach their therapeutic potential, challenges related to achieving scalable and robust manufacturing processes will need to be addressed. A particular challenge is producing lot-sizes capable of meeting commercial demands of up to 10(9) cells/dose for large patient numbers due to the current limitations of expansion technologies. This article describes the application of a decisional tool to identify the most cost-effective expansion technologies for different scales of production as well as current gaps in the technology capabilities for allogeneic cell therapy manufacture. The tool integrates bioprocess economics with optimization to assess the economic competitiveness of planar and microcarrier-based cell expansion technologies. Visualization methods were used to identify the production scales where planar technologies will cease to be cost-effective and where microcarrier-based bioreactors become the only option. The tool outputs also predict that for the industry to be sustainable for high demand scenarios, significant increases will likely be needed in the performance capabilities of microcarrier-based systems. These data are presented using a technology S-curve as well as windows of operation to identify the combination of cell productivities and scale of single-use bioreactors required to meet future lot sizes. The modeling insights can be used to identify where future R&D investment should be focused to improve the performance of the most promising technologies so that they become a robust and scalable option that enables the cell therapy industry reach commercially relevant lot sizes. The tool outputs can facilitate decision-making very early on in development and be used to predict, and better manage, the risk of process changes needed as products proceed through the development pathway. © 2013 Wiley Periodicals, Inc.

Basal CD34+ Cell Count Predicts Peripheral Blood Stem Cell Mobilization in Healthy Donors after Administration of Granulocyte Colony-Stimulating Factor: A Longitudinal, Prospective, Observational, Single-Center, Cohort Study.

PubMed

Martino, Massimo; Gori, Mercedes; Pitino, Annalisa; Gentile, Massimo; Dattola, Antonia; Pontari, Antonella; Vigna, Ernesto; Moscato, Tiziana; Recchia, Anna Grazia; Barilla', Santina; Tripepi, Giovanni; Morabito, Fortunato

2017-07-01

A longitudinal, prospective, observational, single-center, cohort study on healthy donors (HDs) was designed to identify predictors of CD34 + cells on day 5 with emphasis on the predictive value of the basal CD34 + cell count. As potential predictors of mobilization, age, sex, body weight, height, blood volume as well as white blood cell count, peripheral blood (PB) mononuclear cells, platelet count, hematocrit, and hemoglobin levels were considered. Two different evaluations of CD34 + cell counts were determined for each donor: baseline (before granulocyte colony-stimulating factor [G-CSF] administration) and in PB after G-CSF administration on the morning of the fifth day (day 5). A total of 128 consecutive HDs (66 males) with a median age of 43 years were enrolled. CD34 + levels on day 5 displayed a non-normal distribution, with a median value of 75.5 cells/µL. To account for the non-normal distribution of the dependent variable, a quantile regression analysis to predict CD34 + on day 5 using the baseline value of CD34 + as the key predictor was performed. On crude analysis, a baseline value of CD34 + ranging from .5 cells/µL to 1 cells/µL predicts a median value of 50 cells/µL on day 5; a value of 2 cells/µL predicts a median value of 70.7 cells/µL; a value of 3 cells/µL to 4 cells/µL predicts a median value of 91.3 cells/µL, and a value ≥ 5 predicts a median value of 112 cells/µL. In conclusion, the baseline PB CD34 + cell count correlates with the effectiveness of allogeneic PB stem cell mobilization and could be useful to plan the collection. Copyright © 2017 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Parameter estimation for lithium ion batteries

NASA Astrophysics Data System (ADS)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of road conditions is important. An algorithm to predict the SOC in time intervals as small as 5 ms is of critical demand. In such cases, the conventional non-linear estimation procedure is not time-effective. There exist methodologies in the literature, such as those based on fuzzy logic; however, these techniques require a lot of computational storage space. Consequently, it is not possible to implement such techniques on a micro-chip for integration as a part of a real-time device. The Extended Kalman Filter (EKF) based approach presented in this work is a first step towards developing an efficient method to predict online, the State of Charge of a lithium ion cell based on an electrochemical model. The final part of the dissertation focuses on incorporating uncertainty in parameter values into electrochemical models using the polynomial chaos theory (PCT).

Modified unified kinetic scheme for all flow regimes.

PubMed

Liu, Sha; Zhong, Chengwen

2012-06-01

A modified unified kinetic scheme for the prediction of fluid flow behaviors in all flow regimes is described. The time evolution of macrovariables at the cell interface is calculated with the idea that both free transport and collision mechanisms should be considered. The time evolution of macrovariables is obtained through the conservation constraints. The time evolution of local Maxwellian distribution is obtained directly through the one-to-one mapping from the evolution of macrovariables. These improvements provide more physical realities in flow behaviors and more accurate numerical results in all flow regimes especially in the complex transition flow regime. In addition, the improvement steps introduce no extra computational complexity.

A classification tree approach for improving the utilization of flow cytometry testing of blood specimens for B-cell non-Hodgkin lymphoproliferative disorders.

PubMed

Healey, Ryan; Naugler, Christopher; de Koning, Lawrence; Patel, Jay L

2015-01-01

We sought to improve the diagnostic efficiency of flow cytometry investigation on blood by developing data-driven ordering guidelines. Our goal was to improve flow cytometry utilization by decreasing negative testing, therefore reducing healthcare costs. We investigated several laboratory tests performed alongside flow cytometry to identify biomarkers useful in excluding non-leukemic bloods. Test results and patient demographic features were subjected to receiver-operator characteristic (ROC) curve, logistic regression and classification tree analyses to find significant predictors and develop decision rules. Our data show that, in the absence of a compelling clinical indication, flow cytometry testing is largely non-informative on bloods from patients less than 50 years of age having an absolute lymphocyte count (ALC) below 5.0 × 10(9)/L. For patients over age 50 having an ALC below this value, a ferritin value above 450 μg/L is counter-indicative of B-cell clonality. Using these guidelines, 26% of cases were correctly predicted as negative with greater than 97% accuracy.

A simple theoretical framework for understanding heterogeneous differentiation of CD4+ T cells

PubMed Central

2012-01-01

Background CD4+ T cells have several subsets of functional phenotypes, which play critical yet diverse roles in the immune system. Pathogen-driven differentiation of these subsets of cells is often heterogeneous in terms of the induced phenotypic diversity. In vitro recapitulation of heterogeneous differentiation under homogeneous experimental conditions indicates some highly regulated mechanisms by which multiple phenotypes of CD4+ T cells can be generated from a single population of naïve CD4+ T cells. Therefore, conceptual understanding of induced heterogeneous differentiation will shed light on the mechanisms controlling the response of populations of CD4+ T cells under physiological conditions. Results We present a simple theoretical framework to show how heterogeneous differentiation in a two-master-regulator paradigm can be governed by a signaling network motif common to all subsets of CD4+ T cells. With this motif, a population of naïve CD4+ T cells can integrate the signals from their environment to generate a functionally diverse population with robust commitment of individual cells. Notably, two positive feedback loops in this network motif govern three bistable switches, which in turn, give rise to three types of heterogeneous differentiated states, depending upon particular combinations of input signals. We provide three prototype models illustrating how to use this framework to explain experimental observations and make specific testable predictions. Conclusions The process in which several types of T helper cells are generated simultaneously to mount complex immune responses upon pathogenic challenges can be highly regulated, and a simple signaling network motif can be responsible for generating all possible types of heterogeneous populations with respect to a pair of master regulators controlling CD4+ T cell differentiation. The framework provides a mathematical basis for understanding the decision-making mechanisms of CD4+ T cells, and it can be helpful for interpreting experimental results. Mathematical models based on the framework make specific testable predictions that may improve our understanding of this differentiation system. PMID:22697466

Circulating CD34+ progenitor cells and risk of mortality in a population with coronary artery disease.

PubMed

Patel, Riyaz S; Li, Qunna; Ghasemzadeh, Nima; Eapen, Danny J; Moss, Lauren D; Janjua, A Umair; Manocha, Pankaj; Kassem, Hatem Al; Veledar, Emir; Samady, Habib; Taylor, W Robert; Zafari, A Maziar; Sperling, Laurence; Vaccarino, Viola; Waller, Edmund K; Quyyumi, Arshed A

2015-01-16

Low circulating progenitor cell numbers and activity may reflect impaired intrinsic regenerative/reparative potential, but it remains uncertain whether this translates into a worse prognosis. To investigate whether low numbers of progenitor cells associate with a greater risk of mortality in a population at high cardiovascular risk. Patients undergoing coronary angiography were recruited into 2 cohorts (1, n=502 and 2, n=403) over separate time periods. Progenitor cells were enumerated by flow cytometry as CD45(med+) blood mononuclear cells expressing CD34, with additional quantification of subsets coexpressing CD133, vascular endothelial growth factor receptor 2, and chemokine (C-X-C motif) receptor 4. Coefficient of variation for CD34 cells was 2.9% and 4.8%, 21.6% and 6.5% for the respective subsets. Each cohort was followed for a mean of 2.7 and 1.2 years, respectively, for the primary end point of all-cause death. There was an inverse association between CD34(+) and CD34(+)/CD133(+) cell counts and risk of death in cohort 1 (β=-0.92, P=0.043 and β=-1.64, P=0.019, respectively) that was confirmed in cohort 2 (β=-1.25, P=0.020 and β=-1.81, P=0.015, respectively). Covariate-adjusted hazard ratios in the pooled cohort (n=905) were 3.54 (1.67-7.50) and 2.46 (1.18-5.13), respectively. CD34(+)/CD133(+) cell counts improved risk prediction metrics beyond standard risk factors. Reduced circulating progenitor cell counts, identified primarily as CD34(+) mononuclear cells or its subset expressing CD133, are associated with risk of death in individuals with coronary artery disease, suggesting that impaired endogenous regenerative capacity is associated with increased mortality. These findings have implications for biological understanding, risk prediction, and cell selection for cell-based therapies. © 2014 American Heart Association, Inc.

Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

PubMed

Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

2018-04-01

It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential. Copyright © 2017 John Wiley & Sons, Ltd.

Transcriptome Analysis of the Chrysanthemum Foliar Nematode, Aphelenchoides ritzemabosi (Aphelenchida: Aphelenchoididae)

PubMed Central

Li, Jun-Yi; Xie, Hui; Xu, Chun-Ling; Li, Yu

2016-01-01

The chrysanthemum foliar nematode (CFN), Aphelenchoides ritzemabosi, is a plant parasitic nematode that attacks many plants. In this study, a transcriptomes of mixed-stage population of CFN was sequenced on the Illumina HiSeq 2000 platform. 68.10 million Illumina high quality paired end reads were obtained which generated 26,817 transcripts with a mean length of 1,032 bp and an N50 of 1,672 bp, of which 16,467 transcripts were annotated against six databases. In total, 20,311 coding region sequences (CDS), 495 simple sequence repeats (SSRs) and 8,353 single-nucleotide polymorphisms (SNPs) were predicted, respectively. The CFN with the most shared sequences was B. xylophilus with 16,846 (62.82%) common transcripts and 10,543 (39.31%) CFN transcripts matched sequences of all of four plant parasitic nematodes compared. A total of 111 CFN transcripts were predicted as homologues of 7 types of carbohydrate-active enzymes (CAZymes) with plant/fungal cell wall-degrading activities, fewer transcripts were predicted as homologues of plant cell wall-degrading enzymes than fungal cell wall-degrading enzymes. The phylogenetic analysis of GH5, GH16, GH43 and GH45 proteins between CFN and other organisms showed CFN and other nematodes have a closer phylogenetic relationship. In the CFN transcriptome, sixteen types of genes orthologues with seven classes of protein families involved in the RNAi pathway in C. elegans were predicted. This research provides comprehensive gene expression information at the transcriptional level, which will facilitate the elucidation of the molecular mechanisms of CFN and the distribution of gene functions at the macro level, potentially revealing improved methods for controlling CFN. PMID:27875578