Validation, acceptance, and extension of a predictive model of reproductive toxicity using ToxCast data

EPA Science Inventory

The EPA ToxCast research program uses a high-throughput screening (HTS) approach for predicting the toxicity of large numbers of chemicals. Phase-I tested 309 well-characterized chemicals (mostly pesticides) in over 500 assays of different molecular targets, cellular responses an...

Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening

EPA Science Inventory

The EPA ToxCast research program uses high throughput screening for bioactivity profiling and predicting the toxicity of large numbers of chemicals. ToxCast Phase‐I tested 309 well‐characterized chemicals in over 500 assays for a wide range of molecular targets and cellular respo...

Predictive Signatures from ToxCast Data for Chronic, Developmental and Reproductive Toxicity Endpoints

EPA Science Inventory

The EPA ToxCast program is using in vitro assay data and chemical descriptors to build predictive models for in vivo toxicity endpoints. In vitro assays measure activity of chemicals against molecular targets such as enzymes and receptors (measured in cell-free and cell-based sys...

Predictive Signatures of Developmental Toxicity Modeled with HTS Data from ToxCast™ Bioactivity Profiles

EPA Science Inventory

The EPA ToxCast™ research program uses a high-throughput screening (HTS) approach for predicting the toxicity of large numbers of chemicals. Phase-I contains 309 well-characterized chemicals which are mostly pesticides tested in over 600 assays of different molecular targets, cel...

Developing Predictive Toxicity Signatures Using In Vitro Data from the EPA ToxCast Program

EPA Science Inventory

A major focus in toxicology research is the development of in vitro methods to predict in vivo chemical toxicity. Numerous studies have evaluated the use of targeted biochemical, cell-based and genomic assay approaches. Each of these techniques is potentially helpful, but provide...

iTAK: A program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators and protein kinases

USDA-ARS?s Scientific Manuscript database

Transcription factors (TFs) are proteins that regulate the expression of target genes by binding to specific elements in their regulatory regions. Transcriptional regulators (TRs) also regulate the expression of target genes; however, they operate indirectly via interaction with the basal transcript...

The dragnet of children's feeding programs in Atlantic Canada.

PubMed

Dayle, J B; McIntyre, L; Raine-Travers, K D

2000-12-01

Ivan Illich's 1976 prediction that medical dragnets will continue was correct. Now quasi-health dragnets are being established ostensibly to feed children perceived to be hungry. Our qualitative, multi-site case study found that programs justify their expansion to non-target group children as a means of reducing stigmatization, while reaching only an estimated one-third of targeted children. The dragnet continues as new services are added and franchising is proposed while the purpose of the program feeding healthy foods to children ultimately succumbs to drives for efficiency and the desire to maintain the program itself. In this field of social power relations, children become commodified through dialectical interplays among fundamental needs, manipulated needs, benevolence, and domination.

Computer program for the IBM personal computer which searches for approximate matches to short oligonucleotide sequences in long target DNA sequences.

PubMed Central

Myers, E W; Mount, D W

1986-01-01

We describe a program which may be used to find approximate matches to a short predefined DNA sequence in a larger target DNA sequence. The program predicts the usefulness of specific DNA probes and sequencing primers and finds nearly identical sequences that might represent the same regulatory signal. The program is written in the C programming language and will run on virtually any computer system with a C compiler, such as the IBM/PC and other computers running under the MS/DOS and UNIX operating systems. The program has been integrated into an existing software package for the IBM personal computer (see article by Mount and Conrad, this volume). Some examples of its use are given. PMID:3753785

Exploring the speed and performance of molecular replacement with AMPLE using QUARK ab initio protein models.

PubMed

Keegan, Ronan M; Bibby, Jaclyn; Thomas, Jens; Xu, Dong; Zhang, Yang; Mayans, Olga; Winn, Martyn D; Rigden, Daniel J

2015-02-01

AMPLE clusters and truncates ab initio protein structure predictions, producing search models for molecular replacement. Here, an interesting degree of complementarity is shown between targets solved using the different ab initio modelling programs QUARK and ROSETTA. Search models derived from either program collectively solve almost all of the all-helical targets in the test set. Initial solutions produced by Phaser after only 5 min perform surprisingly well, improving the prospects for in situ structure solution by AMPLE during synchrotron visits. Taken together, the results show the potential for AMPLE to run more quickly and successfully solve more targets than previously suspected.

A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.

PubMed

Abadi, Shiran; Yan, Winston X; Amar, David; Mayrose, Itay

2017-10-01

The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA). However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment), a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.

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

ISO Key Project: Exploring the Full Range of Quasar/AGN Properties

NASA Technical Reports Server (NTRS)

Wilkes, Belinda J.

1997-01-01

The ISOPHOT team have developed new recommendations for observing faint sources with ISHPOHT which involve small rasters rather than chopping. This was finalized around Feb 1997 and following this we re-designed the observations for the remainder of our observing time. We had put our program on hold in September when it became clear that chopped observations had major problems. The revised program, which included re-observation at long wavelengths using rasters for a number of high-priority targets and re-specification of new observations of others, was released in April 1997. The latest prediction for the satellite lifetime has extended its life until April 1998. Our project has been allocated a 15% increase in our observing time as a result of this life extension. We are currently working on setting priorities in order to determine which targets to include in this additional time. This will help to offset some of the targets lost due to the significant decrease in detector sensitivity over pre-flight predictions.

Predicting Wanton Assault in a General Youth Sample via the HEW Youth Development Model's Community Program Impact Scales, Age and Sex.

ERIC Educational Resources Information Center

Truckenmiller, James L.

The former HEW National Strategy for Youth Development Model was a community-based planning and procedural tool designed to enhance positive youth development and prevent delinquency through a process of youth needs assessment, development of targeted programs, and program impact evaluation. A series of 12 Impact Scales most directly reflect the…

GTARG - The TOPEX/Poseidon ground track maintenance maneuver targeting program

NASA Technical Reports Server (NTRS)

Shapiro, Bruce E.; Bhat, Ramachandra S.

1993-01-01

GTARG is a computer program used to design orbit maintenance maneuvers for the TOPEX/Poseidon satellite. These maneuvers ensure that the ground track is kept within +/-1 km with of an = 9.9 day exact repeat pattern. Maneuver parameters are determined using either of two targeting strategies: longitude targeting, which maximizes the time between maneuvers, and time targeting, in which maneuvers are targeted to occur at specific intervals. The GTARG algorithm propagates nonsingular mean elements, taking into account anticipated error sigma's in orbit determination, Delta v execution, drag prediction and Delta v quantization. A satellite unique drag model is used which incorporates an approximate mean orbital Jacchia-Roberts atmosphere and a variable mean area model. Maneuver Delta v magnitudes are targeted to precisely maintain either the unbiased ground track itself, or a comfortable (3 sigma) error envelope about the unbiased ground track.

Small Engine Technology (SET) Task 23 ANOPP Noise Prediction for Small Engines, Wing Reflection Code

NASA Technical Reports Server (NTRS)

Lieber, Lysbeth; Brown, Daniel; Golub, Robert A. (Technical Monitor)

2000-01-01

The work performed under Task 23 consisted of the development and demonstration of improvements for the NASA Aircraft Noise Prediction Program (ANOPP), specifically targeted to the modeling of engine noise enhancement due to wing reflection. This report focuses on development of the model and procedure to predict the effects of wing reflection, and the demonstration of the procedure, using a representative wing/engine configuration.

Measurement of functional capacity requirements to aid in development of an occupation-specific rehabilitation training program to help firefighters with cardiac disease safely return to work.

PubMed

Adams, Jenny; Roberts, Joanne; Simms, Kay; Cheng, Dunlei; Hartman, Julie; Bartlett, Charles

2009-03-15

We designed a study to measure the functional capacity requirements of firefighters to aid in the development of an occupation-specific training program in cardiac rehabilitation; 23 healthy male firefighters with no history of heart disease completed a fire and rescue obstacle course that simulated 7 common firefighting tasks. They wore complete personal protective equipment and portable metabolic instruments that included a data collection mask. We monitored each subject's oxygen consumption (VO(2)) and working heart rate, then calculated age-predicted maximum heart rates (220 - age) and training target heart rates (85% of age-predicted maximum heart rate). During performance of the obstacle course, the subjects' mean working heart rates and peak heart rates were higher than the calculated training target heart rates (t(22) = 5.69 [working vs target, p <0.001] and t(22) = 15.14 [peak vs target, p <0.001]). These findings, with mean results for peak VO(2) (3,447 ml/min) and metabolic equivalents (11.9 METs), show that our subjects' functional capacity greatly exceeded that typically attained by patients in traditional cardiac rehabilitation programs (5 to 8 METs). In conclusion, our results indicate the need for intense, occupation-specific cardiac rehabilitation training that will help firefighters safely return to work after a cardiac event.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

Exploring the speed and performance of molecular replacement with AMPLE using QUARK ab initio protein models

PubMed Central

Keegan, Ronan M.; Bibby, Jaclyn; Thomas, Jens; Xu, Dong; Zhang, Yang; Mayans, Olga; Winn, Martyn D.; Rigden, Daniel J.

2015-01-01

AMPLE clusters and truncates ab initio protein structure predictions, producing search models for molecular replacement. Here, an interesting degree of complementarity is shown between targets solved using the different ab initio modelling programs QUARK and ROSETTA. Search models derived from either program collectively solve almost all of the all-helical targets in the test set. Initial solutions produced by Phaser after only 5 min perform surprisingly well, improving the prospects for in situ structure solution by AMPLE during synchrotron visits. Taken together, the results show the potential for AMPLE to run more quickly and successfully solve more targets than previously suspected. PMID:25664744

Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs.

PubMed

Lazzari, Barbara; Caprera, Andrea; Cestaro, Alessandro; Merelli, Ivan; Del Corvo, Marcello; Fontana, Paolo; Milanesi, Luciano; Velasco, Riccardo; Stella, Alessandra

2009-06-29

Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated.

Estimating Acceptability of Financial Health Incentives.

PubMed

Bigsby, Elisabeth; Seitz, Holli H; Halpern, Scott D; Volpp, Kevin; Cappella, Joseph N

2017-08-01

A growing body of evidence suggests that financial incentives can influence health behavior change, but research on the public acceptability of these programs and factors that predict public support have been limited. A representative sample of U.S. adults ( N = 526) were randomly assigned to receive an incentive program description in which the funding source of the program (public or private funding) and targeted health behavior (smoking cessation, weight loss, or colonoscopy) were manipulated. Outcome variables were attitude toward health incentives and allocation of hypothetical funding for incentive programs. Support was highest for privately funded programs. Support for incentives was also higher among ideologically liberal participants than among conservative participants. Demographics and health history differentially predicted attitude and hypothetical funding toward incentives. Incentive programs in the United States are more likely to be acceptable to the public if they are funded by private companies.

Network pharmacology analysis of the anti-cancer pharmacological mechanisms of Ganoderma lucidum extract with experimental support using Hepa1-6-bearing C57 BL/6 mice.

PubMed

Zhao, Ruo-Lin; He, Yu-Min

2018-01-10

Ganoderma lucidum (GL) is an oriental medical fungus, which was used to prevent and treat many diseases. Previously, the effective compounds of Ganoderma lucidum extract (GLE) were extracted from two kinds of GL, [Ganoderma lucidum (Leyss. Ex Fr.) Karst.] and [Ganoderma sinense Zhao, Xu et Zhang], which have been used for adjuvant anti-cancer clinical therapy for more than 20 years. However, its concrete active compounds and its regulation mechanisms on tumor are unclear. In this study, we aimed to identify the main active compounds from GLE and to investigate its anti-cancer mechanisms via drug-target biological network construction and prediction. The main active compounds of GLE were identified by HPLC, EI-MS and NMR, and the compounds related targets were predicted using docking program. To investigate the functions of GL holistically, the active compounds of GL and related targets were predicted based on four public databases. Subsequently, the Identified-Compound-Target network and Predicted-Compound-Target network were constructed respectively, and they were overlapped to detect the hub potential targets in both networks. Furthermore, the qRT-PCR and western-blot assays were used to validate the expression levels of target genes in GLE treated Hepa1-6-bearing C57 BL/6 mice. In our work, 12 active compounds of GLE were identified, including Ganoderic acid A, Ganoderenic acid A, Ganoderic acid B, Ganoderic acid H, Ganoderic acid C2, Ganoderenic acid D, Ganoderic acid D, Ganoderenic acid G, Ganoderic acid Y, Kaemferol, Genistein and Ergosterol. Using the docking program, 20 targets were mapped to 12 compounds of GLE. Furthermore, 122 effective active compounds of GL and 116 targets were holistically predicted using public databases. Compare with the Identified-Compound-Target network and Predicted-Compound-Target network, 6 hub targets were screened, including AR, CHRM2, ESR1, NR3C1, NR3C2 and PGR, which was considered as potential markers and might play important roles in the process of GLE treatment. GLE effectively inhibited tumor growth in Hepa1-6-bearing C57 BL/6 mice. Finally, consistent with the results of qRT-PCR data, the results of western-blot assay demonstrated the expression levels of PGR and ESR1 were up-regulated, as well as the expression levels of NR3C2 and AR were down-regulated, while the change of NR3C1 and CHRM2 had no statistical significance. The results indicated that these 4 hub target genes, including NR3C2, AR, ESR1 and PGR, might act as potential markers to evaluate the curative effect of GLE treatment in tumor. And, the combined data provide preliminary study of the pharmacological mechanisms of GLE, which may be a promising potential therapeutic and chemopreventative candidate for anti-cancer. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

What Implementation Components Predict Positive Outcomes in a Parenting Program?

ERIC Educational Resources Information Center

Álvarez, Míriam; Rodrigo, María José; Byrne, Sonia

2018-01-01

Objectives: To examine the components affecting the quality of the implementation and their impact on the outcomes of the "Growing Up Happily in the Family" program targeted at parents with children aged 0-5. Method: At-risk and non-at-risk parents (N = 196) participated in 26 groups in local social services. Adherence, adaptations,…

Data Analysis Measurement: Having a Solar Blast! NASA Connect: Program 7 in the 2001-2002 Video Series. [Videotape].

ERIC Educational Resources Information Center

National Aeronautics and Space Administration, Hampton, VA. Langley Research Center.

NASA Connect is an interdisciplinary, instructional distance learning program targeting students in grades 6-8. This videotape explains how engineers and researchers at the National Aeronautics and Space Administration (NASA) use data analysis and measurement to predict solar storms, anticipate how they will affect the Earth, and improve…

Implementation of evidence-based home visiting programs aimed at reducing child maltreatment: A meta-analytic review.

PubMed

Casillas, Katherine L; Fauchier, Angèle; Derkash, Bridget T; Garrido, Edward F

2016-03-01

In recent years there has been an increase in the popularity of home visitation programs as a means of addressing risk factors for child maltreatment. The evidence supporting the effectiveness of these programs from several meta-analyses, however, is mixed. One potential explanation for this inconsistency explored in the current study involves the manner in which these programs were implemented. In the current study we reviewed 156 studies associated with 9 different home visitation program models targeted to caregivers of children between the ages of 0 and 5. Meta-analytic techniques were used to determine the impact of 18 implementation factors (e.g., staff selection, training, supervision, fidelity monitoring, etc.) and four study characteristics (publication type, target population, study design, comparison group) in predicting program outcomes. Results from analyses revealed that several implementation factors, including training, supervision, and fidelity monitoring, had a significant effect on program outcomes, particularly child maltreatment outcomes. Study characteristics, including the program's target population and the comparison group employed, also had a significant effect on program outcomes. Implications of the study's results for those interested in implementing home visitation programs are discussed. A careful consideration and monitoring of program implementation is advised as a means of achieving optimal study results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction methodologies for target scene generation in the aerothermal targets analysis program (ATAP)

NASA Astrophysics Data System (ADS)

Hudson, Douglas J.; Torres, Manuel; Dougherty, Catherine; Rajendran, Natesan; Thompson, Rhoe A.

2003-09-01

The Air Force Research Laboratory (AFRL) Aerothermal Targets Analysis Program (ATAP) is a user-friendly, engineering-level computational tool that features integrated aerodynamics, six-degree-of-freedom (6-DoF) trajectory/motion, convective and radiative heat transfer, and thermal/material response to provide an optimal blend of accuracy and speed for design and analysis applications. ATAP is sponsored by the Kinetic Kill Vehicle Hardware-in-the-Loop Simulator (KHILS) facility at Eglin AFB, where it is used with the CHAMP (Composite Hardbody and Missile Plume) technique for rapid infrared (IR) signature and imagery predictions. ATAP capabilities include an integrated 1-D conduction model for up to 5 in-depth material layers (with options for gaps/voids with radiative heat transfer), fin modeling, several surface ablation modeling options, a materials library with over 250 materials, options for user-defined materials, selectable/definable atmosphere and earth models, multiple trajectory options, and an array of aerodynamic prediction methods. All major code modeling features have been validated with ground-test data from wind tunnels, shock tubes, and ballistics ranges, and flight-test data for both U.S. and foreign strategic and theater systems. Numerous applications include the design and analysis of interceptors, booster and shroud configurations, window environments, tactical missiles, and reentry vehicles.

A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search.

PubMed

Adeli, Hossein; Vitu, Françoise; Zelinsky, Gregory J

2017-02-08

Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely, although most models of saccade programming are tightly coupled to underlying neurophysiology, none have been tested using real-world stimuli and tasks. We combined the strengths of these two approaches in MASC, a model of attention in the superior colliculus (SC) that captures known neurophysiological constraints on saccade programming. We show that MASC predicted the fixation locations of humans freely viewing naturalistic scenes and performing exemplar and categorical search tasks, a breadth achieved by no other existing model. Moreover, it did this as well or better than its more specialized state-of-the-art competitors. MASC's predictive success stems from its inclusion of high-level but core principles of SC organization: an over-representation of foveal information, size-invariant population codes, cascaded population averaging over distorted visual and motor maps, and competition between motor point images for saccade programming, all of which cause further modulation of priority (attention) after projection of saliency and target maps to the SC. Only by incorporating these organizing brain principles into our models can we fully understand the transformation of complex visual information into the saccade programs underlying movements of overt attention. With MASC, a theoretical footing now exists to generate and test computationally explicit predictions of behavioral and neural responses in visually complex real-world contexts. SIGNIFICANCE STATEMENT The superior colliculus (SC) performs a visual-to-motor transformation vital to overt attention, but existing SC models cannot predict saccades to visually complex real-world stimuli. We introduce a brain-inspired SC model that outperforms state-of-the-art image-based competitors in predicting the sequences of fixations made by humans performing a range of everyday tasks (scene viewing and exemplar and categorical search), making clear the value of looking to the brain for model design. This work is significant in that it will drive new research by making computationally explicit predictions of SC neural population activity in response to naturalistic stimuli and tasks. It will also serve as a blueprint for the construction of other brain-inspired models, helping to usher in the next generation of truly intelligent autonomous systems. Copyright © 2017 the authors 0270-6474/17/371453-15$15.00/0.

ACOUSTIC LINERS FOR TURBOFAN ENGINES

NASA Technical Reports Server (NTRS)

Minner, G. L.

1994-01-01

This program was developed to design acoustic liners for turbofan engines. This program combines results from theoretical models of wave alternation in acoustically treated passages with experimental data from full-scale fan noise suppressors. By including experimentally obtained information, the program accounts for real effects such as wall boundary layers, duct terminations, and sound modal structure. The program has its greatest use in generating a number of design specifications to be used for evaluation of trade-offs. The program combines theoretical and empirical data in designing annular acoustic liners. First an estimate of the noise output of the fan is made based on basic fan aerodynamic design variables. Then, using a target noise spectrum after alternation and the estimated fan noise spectrum, a design spectrum is calculated as their difference. Next, the design spectrum is combined with knowledge of acoustic liner performance and the liner design variables to specify the acoustic design. Details of the liner design are calculated by combining the required acoustic impedance with a mathematical model relating acoustic impedance to the physical structure of the liner. Input to the noise prediction part of the program consists of basic fan operating parameters, distance that the target spectrum is to be measured and the target spectrum. The liner design portion of the program requires the required alternation spectrum, desired values of length to height and several option selection parameters. Output from the noise prediction portion is a noise spectrum consisting of discrete tones and broadband noise. This may be used as input to the liner design portion of the program. The liner design portion of the program produces backing depths, open area ratios, and face plate thicknesses. This program is written in FORTRAN V and has been implemented in batch mode on a UNIVAC 1100 series computer with a central memory requirement of 12K (decimal) of 36 bit words.

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal

PubMed Central

Dicko, Ahmadou H.; Lancelot, Renaud; Seck, Momar T.; Guerrini, Laure; Sall, Baba; Lo, Mbargou; Vreysen, Marc J. B.; Lefrançois, Thierry; Fonta, William M.; Peck, Steven L.; Bouyer, Jérémy

2014-01-01

Tsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models’ results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs. PMID:24982143

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal.

PubMed

Dicko, Ahmadou H; Lancelot, Renaud; Seck, Momar T; Guerrini, Laure; Sall, Baba; Lo, Mbargou; Vreysen, Marc J B; Lefrançois, Thierry; Fonta, William M; Peck, Steven L; Bouyer, Jérémy

2014-07-15

Tsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models' results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs.

Surgical Neuroanatomy and Programming in Deep Brain Stimulation for Obsessive Compulsive Disorder

PubMed Central

Morishita, Takashi; Fayad, Sarah M.; Goodman, Wayne K.; Foote, Kelly D.; Chen, Dennis; Peace, David A.; Rhoton, Albert L.; Okun, Michael S.

2014-01-01

Objectives Deep brain stimulation (DBS) has been established as a safe, effective therapy for movement disorders (Parkinson’s disease, essential tremor, etc.), and its application is expanding to the treatment of other intractable neuropsychiatric disorders including Depression and Obsessive-Compulsive Disorder (OCD). Several published studies have supported the efficacy of DBS for severely debilitating OCD. However, questions remain regarding the optimal anatomical target and the lack of a bedside programming paradigm for OCD DBS. Management of OCD DBS can be highly variable and is typically guided by each center’s individual expertise. In this paper, we review the various approaches to targeting and programming for OCD DBS. We also review the clinical experience for each proposed target, and discuss the relevant neuroanatomy. Methods A PubMed review was performed searching for literature on OCD DBS and included all articles published before March 2012. We included all available studies with a clear description of the anatomical targets, programming details, and the outcomes. Results Six different DBS approaches were identified. High frequency stimulation with high voltage was applied in most cases, and predictive factors for favorable outcomes were discussed in the literature. Conclusion DBS remains an experimental treatment for medication refractory OCD. Target selection and programming paradigms are not yet standardized, though, an improved understanding of the relationship between the DBS lead and the surrounding neuroanatomical structures will aid in the selection of targets and the approach to programming. We propose to form a registry to track OCD DBS cases for future clinical study design. PMID:24345303

Surgical neuroanatomy and programming in deep brain stimulation for obsessive compulsive disorder.

PubMed

Morishita, Takashi; Fayad, Sarah M; Goodman, Wayne K; Foote, Kelly D; Chen, Dennis; Peace, David A; Rhoton, Albert L; Okun, Michael S

2014-06-01

Deep brain stimulation (DBS) has been established as a safe, effective therapy for movement disorders (Parkinson's disease, essential tremor, etc.), and its application is expanding to the treatment of other intractable neuropsychiatric disorders including depression and obsessive-compulsive disorder (OCD). Several published studies have supported the efficacy of DBS for severely debilitating OCD. However, questions remain regarding the optimal anatomic target and the lack of a bedside programming paradigm for OCD DBS. Management of OCD DBS can be highly variable and is typically guided by each center's individual expertise. In this paper, we review the various approaches to targeting and programming for OCD DBS. We also review the clinical experience for each proposed target and discuss the relevant neuroanatomy. A PubMed review was performed searching for literature on OCD DBS and included all articles published before March 2012. We included all available studies with a clear description of the anatomic targets, programming details, and the outcomes. Six different DBS approaches were identified. High-frequency stimulation with high voltage was applied in most cases, and predictive factors for favorable outcomes were discussed in the literature. DBS remains an experimental treatment for medication refractory OCD. Target selection and programming paradigms are not yet standardized, though an improved understanding of the relationship between the DBS lead and the surrounding neuroanatomic structures will aid in the selection of targets and the approach to programming. We propose to form a registry to track OCD DBS cases for future clinical study design. © 2013 International Neuromodulation Society.

Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

NASA Astrophysics Data System (ADS)

Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

2017-12-01

The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

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

PubMed

Xiong, Dapeng; Zeng, Jianyang; Gong, Haipeng

2017-09-01

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

Targeting the Poor: Evidence from a Field Experiment in Indonesia

PubMed Central

Alatas, Vivi; Banerjee, Abhijit; Hanna, Rema; Olken, Benjamin A.; Tobias, Julia

2014-01-01

This paper reports an experiment in 640 Indonesian villages on three approaches to target the poor: proxy-means tests (PMT), where assets are used to predict consumption; community targeting, where villagers rank everyone from richest to poorest; and a hybrid. Defining poverty based on PPP$2 per-capita consumption, community targeting and the hybrid perform somewhat worse in identifying the poor than PMT, though not by enough to significantly affect poverty outcomes for a typical program. Elite capture does not explain these results. Instead, communities appear to apply a different concept of poverty. Consistent with this finding, community targeting results in higher satisfaction. PMID:25197099

Comparative Protein Structure Modeling Using MODELLER

PubMed Central

Webb, Benjamin; Sali, Andrej

2016-01-01

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. PMID:27322406

Forecasting long-range atmospheric transport episodes of polychlorinated biphenyls using FLEXPART

NASA Astrophysics Data System (ADS)

Halse, Anne Karine; Eckhardt, Sabine; Schlabach, Martin; Stohl, Andreas; Breivik, Knut

2013-06-01

The analysis of concentrations of persistent organic pollutants (POPs) in ambient air is costly and can only be done for a limited number of samples. It is thus beneficial to maximize the information content of the samples analyzed via a targeted observation strategy. Using polychlorinated biphenyls (PCBs) as an example, a forecasting system to predict and evaluate long-range atmospheric transport (LRAT) episodes of POPs at a remote site in southern Norway has been developed. The system uses the Lagrangian particle transport model FLEXPART, and can be used for triggering extra ("targeted") sampling when LRAT episodes are predicted to occur. The system was evaluated by comparing targeted samples collected over 12-25 h during individual LRAT episodes with monitoring samples regularly collected over one day per week throughout a year. Measured concentrations in all targeted samples were above the 75th percentile of the concentrations obtained from the regular monitoring program and included the highest measured values of all samples. This clearly demonstrates the success of the targeted sampling strategy.

LiveBench-1: continuous benchmarking of protein structure prediction servers.

PubMed

Bujnicki, J M; Elofsson, A; Fischer, D; Rychlewski, L

2001-02-01

We present a novel, continuous approach aimed at the large-scale assessment of the performance of available fold-recognition servers. Six popular servers were investigated: PDB-Blast, FFAS, T98-lib, GenTHREADER, 3D-PSSM, and INBGU. The assessment was conducted using as prediction targets a large number of selected protein structures released from October 1999 to April 2000. A target was selected if its sequence showed no significant similarity to any of the proteins previously available in the structural database. Overall, the servers were able to produce structurally similar models for one-half of the targets, but significantly accurate sequence-structure alignments were produced for only one-third of the targets. We further classified the targets into two sets: easy and hard. We found that all servers were able to find the correct answer for the vast majority of the easy targets if a structurally similar fold was present in the server's fold libraries. However, among the hard targets--where standard methods such as PSI-BLAST fail--the most sensitive fold-recognition servers were able to produce similar models for only 40% of the cases, half of which had a significantly accurate sequence-structure alignment. Among the hard targets, the presence of updated libraries appeared to be less critical for the ranking. An "ideally combined consensus" prediction, where the results of all servers are considered, would increase the percentage of correct assignments by 50%. Each server had a number of cases with a correct assignment, where the assignments of all the other servers were wrong. This emphasizes the benefits of considering more than one server in difficult prediction tasks. The LiveBench program (http://BioInfo.PL/LiveBench) is being continued, and all interested developers are cordially invited to join.

An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

PubMed Central

Naveed, Hammad; Hameed, Umar S.; Harrus, Deborah; Bourguet, William; Arold, Stefan T.; Gao, Xin

2015-01-01

Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development. Availability and implementation: The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx. Contact: xin.gao@kaust.edu.sa and stefan.arold@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26286808

Measuring sustainment of prevention programs and initiatives: a study protocol.

PubMed

Palinkas, Lawrence A; Spear, Suzanne E; Mendon, Sapna J; Villamar, Juan; Valente, Thomas; Chou, Chi-Ping; Landsverk, John; Kellam, Shepperd G; Brown, C Hendricks

2016-07-16

Sustaining prevention efforts directed at substance use and mental health problems is one of the greatest, yet least understood, challenges in the field of implementation science. A large knowledge gap exists regarding the meaning of the term "sustainment" and what factors predict or even measure sustainability of effective prevention programs and support systems. The U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) supports a diverse portfolio of prevention and treatment grant programs that aim to improve population and individual level behavioral health. This study focuses on four SAMHSA prevention grant programs, two of which target substance abuse prevention at the state or single community level, one targets suicide prevention, and one targets prevention of aggressive/disruptive behavior in elementary schools. An examination of all four grant programs simultaneously provides an opportunity to determine what is meant by the term sustainment and identify and support both the unique requirements for improving sustainability for each program as well as for developing a generalizable framework comprised of core components of sustainment across diverse prevention approaches. Based on an analysis of qualitative and quantitative data of 10 grantees supported by these four programs, we will develop a flexible measurement system, with both general and specific components, that can bring precision to monitoring sustainment of infrastructure, activities, and outcomes for each prevention approach. We will then transform this system for use in evaluating and improving the likelihood of achieving prevention effort sustainment. To achieve these goals, we will (1) identify core components of sustainment of prevention programs and their support infrastructures; (2) design a measurement system for monitoring and providing feedback regarding sustainment within the four SAMHSA's prevention-related grant programs; and (3) pilot test the predictability of this multilevel measurement system across these programs and the feasibility and acceptability of a measurement system to evaluate and improve the likelihood of sustainment. This project is intended to improve sustainment of the supporting prevention infrastructure, activities, and outcomes that are funded by federal, state, community, and foundation sources.

Feasibility of Forecasting Highway Safety in Support of Safety Incentive and Safety Target Programs.

DOT National Transportation Integrated Search

2007-11-01

Using the frequency of fatal crashes from the current observation period (e.g. month, year, etc.) as the : prediction of expected future performance does not account for changes in safety that result from : increases in exposure (population, addition...

Biochemical Activities of 320 ToxCast Chemicals Evaluated Across 239 Functional Targets

EPA Science Inventory

EPA’s ToxCast research program is profiling chemical bioactivity in order to generate predictive signatures of toxicity. The present study evaluated 320 chemicals across 239 biochemical assays. ToxCast phase I chemicals include 309 unique structures, most of which are pesticide ...

Modeling Reproductive Toxicity for Chemical Prioritization into an Integrated Testing Strategy

EPA Science Inventory

The EPA ToxCast research program uses a high-throughput screening (HTS) approach for predicting the toxicity of large numbers of chemicals. Phase-I tested 309 well-characterized chemicals in over 500 assays of different molecular targets, cellular responses and cell-states. Of th...

Competition and rural primary care programs.

PubMed

Ricketts, T C

1990-04-01

Rural primary care programs were established in areas where there was thought to be no competition for patients. However, evidence from site visits and surveys of a national sample of subsidized programs revealed a pattern of competitive responses by the clinics. In this study of 193 rural primary care programs, mail and telephone surveys produced uniform data on the organization, operation, finances, and utilization of a representative sample of clinics. The programs were found to compete in terms of: (1) price, (2) service mix, (3) staff availability, (4) structural accessibility, (5) outreach, and (6) targeting a segment of the market. The competitive strategies employed by the clinics had consequences that affected their productivity and financial stability. The strategies were related to the perceived missions of the programs, and depended heavily upon the degree of isolation of the program and the targeting of the services. The competitive strategy chosen by a particular program could not be predicted based on service area population and apparent competitors in the service area. The goals and objectives of the programs had more to do with their competitive responses than with market characteristics. Moreover, the chosen strategies may not meet the demands of those markets.

School-based depression and anxiety prevention programs for young people: A systematic review and meta-analysis.

PubMed

Werner-Seidler, Aliza; Perry, Yael; Calear, Alison L; Newby, Jill M; Christensen, Helen

2017-02-01

Depression and anxiety often emerge for the first time during youth. The school environment provides an ideal context to deliver prevention programs, with potential to offset the trajectory towards disorder. The aim of this review was to provide a comprehensive evaluation of randomised-controlled trials of psychological programs, designed to prevent depression and/or anxiety in children and adolescents delivered in school settings. Medline, PsycINFO and the Cochrane Library were systematically searched for articles published until February 2015. Eighty-one unique studies comprising 31,794 school students met inclusion criteria. Small effect sizes for both depression (g=0.23) and anxiety (g=0.20) prevention programs immediately post-intervention were detected. Small effects were evident after 12-month follow-up for both depression (g=0.11) and anxiety (g=0.13). Overall, the quality of the included studies was poor, and heterogeneity was moderate. Subgroup analyses suggested that universal depression prevention programs had smaller effect sizes at post-test relative to targeted programs. For anxiety, effect sizes were comparable for universal and targeted programs. There was some evidence that externally-delivered interventions were superior to those delivered by school staff for depression, but not anxiety. Meta-regression confirmed that targeted programs predicted larger effect sizes for the prevention of depression. These results suggest that the refinement of school-based prevention programs have the potential to reduce mental health burden and advance public health outcomes. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A computational cognitive model of self-efficacy and daily adherence in mHealth.

PubMed

Pirolli, Peter

2016-12-01

Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress model refines the psychological construct of self-efficacy. To explain and predict the dynamics of self-efficacy and predict individual performance of targeted behaviors, the self-efficacy construct is implemented as a theory-based neurocognitive simulation of the interaction of behavioral goals, memories of past experiences, and behavioral performance.

New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking.

PubMed

Sulimov, Alexey V; Kutov, Danil C; Katkova, Ekaterina V; Ilin, Ivan S; Sulimov, Vladimir B

2017-11-01

Discovery of new inhibitors of the protein associated with a given disease is the initial and most important stage of the whole process of the rational development of new pharmaceutical substances. New inhibitors block the active site of the target protein and the disease is cured. Computer-aided molecular modeling can considerably increase effectiveness of new inhibitors development. Reliable predictions of the target protein inhibition by a small molecule, ligand, is defined by the accuracy of docking programs. Such programs position a ligand in the target protein and estimate the protein-ligand binding energy. Positioning accuracy of modern docking programs is satisfactory. However, the accuracy of binding energy calculations is too low to predict good inhibitors. For effective application of docking programs to new inhibitors development the accuracy of binding energy calculations should be higher than 1kcal/mol. Reasons of limited accuracy of modern docking programs are discussed. One of the most important aspects limiting this accuracy is imperfection of protein-ligand energy calculations. Results of supercomputer validation of several force fields and quantum-chemical methods for docking are presented. The validation was performed by quasi-docking as follows. First, the low energy minima spectra of 16 protein-ligand complexes were found by exhaustive minima search in the MMFF94 force field. Second, energies of the lowest 8192 minima are recalculated with CHARMM force field and PM6-D3H4X and PM7 quantum-chemical methods for each complex. The analysis of minima energies reveals the docking positioning accuracies of the PM7 and PM6-D3H4X quantum-chemical methods and the CHARMM force field are close to one another and they are better than the positioning accuracy of the MMFF94 force field. Copyright © 2017 Elsevier Inc. All rights reserved.

Toward automated biochemotype annotation for large compound libraries.

PubMed

Chen, Xian; Liang, Yizeng; Xu, Jun

2006-08-01

Combinatorial chemistry allows scientists to probe large synthetically accessible chemical space. However, identifying the sub-space which is selectively associated with an interested biological target, is crucial to drug discovery and life sciences. This paper describes a process to automatically annotate biochemotypes of compounds in a library and thus to identify bioactivity related chemotypes (biochemotypes) from a large library of compounds. The process consists of two steps: (1) predicting all possible bioactivities for each compound in a library, and (2) deriving possible biochemotypes based on predictions. The Prediction of Activity Spectra for Substances program (PASS) was used in the first step. In second step, structural similarity and scaffold-hopping technologies are employed. These technologies are used to derive biochemotypes from bioactivity predictions and the corresponding annotated biochemotypes from MDL Drug Data Report (MDDR) database. About a one million (982,889) commercially available compound library (CACL) has been tested using this process. This paper demonstrates the feasibility of automatically annotating biochemotypes for large libraries of compounds. Nevertheless, some issues need to be considered in order to improve the process. First, the prediction accuracy of PASS program has no significant correlation with the number of compounds in a training set. Larger training sets do not necessarily increase the maximal error of prediction (MEP), nor do they increase the hit structural diversity. Smaller training sets do not necessarily decrease MEP, nor do they decrease the hit structural diversity. Second, the success of systematic bioactivity prediction relies on modeling, training data, and the definition of bioactivities (biochemotype ontology). Unfortunately, the biochemotype ontology was not well developed in the PASS program. Consequently, "ill-defined" bioactivities can reduce the quality of predictions. This paper suggests the ways in which the systematic bioactivities prediction program should be improved.

Enhanced sensitivity of CpG island search and primer design based on predicted CpG island position.

PubMed

Park, Hyun-Chul; Ahn, Eu-Ree; Jung, Ju Yeon; Park, Ji-Hye; Lee, Jee Won; Lim, Si-Keun; Kim, Won

2018-05-01

DNA methylation has important biological roles, such as gene expression regulation, as well as practical applications in forensics, such as in body fluid identification and age estimation. DNA methylation often occurs in the CpG site, and methylation within the CpG islands affects various cellular functions and is related to tissue-specific identification. Several programs have been developed to identify CpG islands; however, the size, location, and number of predicted CpG islands are not identical due to different search algorithms. In addition, they only provide structural information for predicted CpG islands without experimental information, such as primer design. We developed an analysis pipeline package, CpGPNP, to integrate CpG island prediction and primer design. CpGPNP predicts CpG islands more accurately and sensitively than other programs, and designs primers easily based on the predicted CpG island locations. The primer design function included standard, bisulfite, and methylation-specific PCR to identify the methylation of particular CpG sites. In this study, we performed CpG island prediction on all chromosomes and compared CpG island search performance of CpGPNP with other CpG island prediction programs. In addition, we compared the position of primers designed for a specific region within the predicted CpG island using other bisulfite PCR primer programs. The primers designed by CpGPNP were used to experimentally verify the amplification of the target region of markers for body fluid identification and age estimation. CpGPNP is freely available at http://forensicdna.kr/cpgpnp/. Copyright © 2018 Elsevier B.V. All rights reserved.

Improving Reading Comprehension by Predicting, Monitoring Comprehension, Remediation, and Personal Response Strategies.

ERIC Educational Resources Information Center

Jacobucci, Leanne; Richert, Judy; Ronan, Susan; Tanis, Ariana

This report describes a program for improving inconsistent reading comprehension. The targeted population consisted of first, third, and fifth grade classrooms in a diverse middle class community located in Illinois. The problems of low academic achievement were documented through teacher observation, reading comprehension test scores, and low…

Acoustics Discipline Overview

NASA Technical Reports Server (NTRS)

Envia, Edmane; Thomas, Russell

2007-01-01

As part of the Fundamental Aeronautics Program Annual Review, a summary of the progress made in 2007 in acoustics research under the Subsonic Fixed Wing project is given. The presentation describes highlights from in-house and external activities including partnerships and NRA-funded research with industry and academia. Brief progress reports from all acoustics Phase 1 NRAs are also included as are outlines of the planned activities for 2008 and all Phase 2 NRAs. N+1 and N+2 technology paths outlined for Subsonic Fixed Wing noise targets. NRA Round 1 progressing with focus on prediction method advancement. NRA Round 2 initiating work focused on N+2 technology, prediction methods, and validation. Excellent partnerships in progress supporting N+1 technology targets and providing key data sets.

Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models.

PubMed

Hussain, Owais A; Junejo, Khurum N

2018-02-20

Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of disease. The World Health Organization (WHO) propagates Directly Observed Therapy Short-course (DOTS) as an effective way to stop the spread of TB in communities with a high burden. But DOTS also adds a significant burden on the financial feasibility of the program. We aim to facilitate TB programs by predicting the outcome of the treatment of a particular patient at the start of treatment so that their health workers can be utilized in a targeted and cost-effective way. The problem was modeled as a classification problem, and the outcome of treatment was predicted using state-of-art implementations of 3 machine learning algorithms. 4213 patients were evaluated, out of which 64.37% completed their treatment. Results were evaluated using 4 performance measures; accuracy, precision, sensitivity, and specificity. The models offer an improvement of more than 12% accuracy over the baseline prediction. Empirical results also revealed some insights to improve TB programs. Overall, our proposed methodology will may help teams running TB programs manage their human resources more effectively, thus saving more lives.

Low-rank regularization for learning gene expression programs.

PubMed

Ye, Guibo; Tang, Mengfan; Cai, Jian-Feng; Nie, Qing; Xie, Xiaohui

2013-01-01

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.

An antiviral RISC isolated from Tobacco rattle virus-infected plants

PubMed Central

Ciomperlik, Jessica J.; Omarov, Rustem T.; Scholthof, Herman B.

2011-01-01

The RNAi model predicts that during antiviral defense a RNA-induced silencing complex (RISC) is programmed with viral short-interfering RNAs (siRNAs) to target the cognate viral RNA for degradation. We show that infection of Nicotiana benthamiana with Tobacco rattle virus (TRV) activates an antiviral nuclease that specifically cleaves TRV RNA in vitro. In agreement with known RISC properties, the nuclease activity was inhibited by NaCl and EDTA and stimulated by divalent metal cations; a novel property was its preferential targeting of elongated RNA molecules. Intriguingly, the specificity of the TRV RISC could be re-programmed by exogenous addition of RNA (containing siRNAs) from plants infected with an unrelated virus, resulting in a newly acquired ability of RISC to target this heterologous genome in vitro. Evidently the virus-specific nuclease complex from N. benthamiana represents a genuine RISC that functions as a readily employable and reprogrammable antiviral defense unit. PMID:21272908

The US ICF Ignition Program and the Inertial Fusion Program

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

Lindl, J D; Hammel, B A; Logan, B G

2003-07-02

There has been rapid progress in inertial fusion in the past few years. This progress spans the construction of ignition facilities, a wide range of target concepts, and the pursuit of integrated programs to develop fusion energy using lasers, ion beams and z-pinches. Two ignition facilities are under construction (NIF in the U.S. and LMJ in France) and both projects are progressing toward an initial experimental capability. The LIL prototype beamline for LMJ and the first 4 beams of NIF will be available for experiments in 2003. The full 192 beam capability of NIF will be available in 2009 andmore » ignition experiments are expected to begin shortly after that time. There is steady progress in the target science and target fabrication in preparation for indirect drive ignition experiments on NIF. Advanced target designs may lead to 5-10 times more yield than initial target designs. There has also been excellent progress on the science of ion beam and z-pinch driven indirect drive targets. Excellent progress on direct-drive targets has been obtained on the Omega laser at the University of Rochester. This includes improved performance of targets with a pulse shape predicted to result in reduced hydrodynamic instability. Rochester has also obtained encouraging results from initial cryogenic implosions. There is widespread interest in the science of fast ignition because of its potential for achieving higher target gain with lower driver energy and relaxed target fabrication requirements. Researchers from Osaka have achieved outstanding implosion and heating results from the Gekko XII Petawatt facility and implosions suitable for fast ignition have been tested on the Omega laser. A broad based program to develop lasers and ions beams for IFE is under way with excellent progress in drivers, chambers, target fabrication and target injection. KrF and Diode Pumped Solid-State lasers (DPSSL) are being developed in conjunction with drywall chambers and direct drive targets. Induction accelerators for heavy ions are being developed in conjunction with thick-liquid protected wall chambers and indirect-drive targets.« less

Upgrade to the Cryogenic Hydrogen Gas Target Monitoring System

NASA Astrophysics Data System (ADS)

Slater, Michael; Tribble, Robert

2013-10-01

The cryogenic hydrogen gas target at Texas A&M is a vital component for creating a secondary radioactive beam that is then used in experiments in the Momentum Achromat Recoil Spectrometer (MARS). A stable beam from the K500 superconducting cyclotron enters the gas cell and some incident particles are transmuted by a nuclear reaction into a radioactive beam, which are separated from the primary beam and used in MARS experiments. The pressure in the target chamber is monitored so that a predictable isotope production rate can be assured. A ``black box'' received the analog pressure data and sent RS232 serial data through an outdated serial connection to an outdated Visual Basic 6 (VB6) program, which plotted the chamber pressure continuously. The black box has been upgraded to an Arduino UNO microcontroller [Atmel Inc.], which can receive the pressure data and output via USB to a computer. It has been programmed to also accept temperature data for future upgrade. A new computer program, with updated capabilities, has been written in Python. The software can send email alerts, create audible alarms through the Arduino, and plot pressure and temperature. The program has been designed to better fit the needs of the users. Funded by DOE and NSF-REU Program.

An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design.

PubMed

Ballante, Flavio; Marshall, Garland R

2016-01-25

Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e., by determining the docking accuracy (DA)) in order to select the most appropriate docking procedure and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different root-mean-square deviation (RMSD) formulas, scoring functions, and format results, it is both difficult and time-consuming to consistently determine and compare their predictive capabilities in order to identify the best protocol to use for the target of interest and to extrapolate the binding poses (i.e., best-docked (BD), best-cluster (BC), and best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures called Clusterizer and DockAccessor have been developed and implemented for use with some common and "free-for-academics" programs such as AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC, and BF poses as well as to perform consistent cluster and DA analyses. Clusterizer and DockAccessor (code available over the Internet) represent two novel tools to collect computationally determined poses and detect the most predictive docking approach. Herein an application to human lysine deacetylase (hKDAC) inhibitors is illustrated.

miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes.

PubMed

Dweep, Harsh; Sticht, Carsten; Pandey, Priyanka; Gretz, Norbert

2011-10-01

MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10 kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6 months. miRWalk is freely available at http://mirwalk.uni-hd.de/. Copyright © 2011 Elsevier Inc. All rights reserved.

PharmDock: a pharmacophore-based docking program

PubMed Central

2014-01-01

Background Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. Results Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking. Conclusion A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available from http://people.pharmacy.purdue.edu/~mlill/software/pharmdock. PMID:24739488

Active Redesign of a Medicaid Care Management Strategy for Greater Return on Investment: Predicting Impactability.

PubMed

DuBard, C Annette; Jackson, Carlos T

2018-04-01

Care management of high-cost/high-needs patients is an increasingly common strategy to reduce health care costs. A variety of targeting methodologies have emerged to identify patients with high historical or predicted health care utilization, but the more pertinent question for program planners is how to identify those who are most likely to benefit from care management intervention. This paper describes the evolution of complex care management targeting strategies in Community Care of North Carolina's (CCNC) work with the statewide non-dual Medicaid population, culminating in the development of an "Impactability Score" that uses administrative data to predict achievable savings. It describes CCNC's pragmatic approach for estimating intervention effects in a historical cohort of 23,455 individuals, using a control population of 14,839 to determine expected spending at an individual level, against which actual spending could be compared. The actual-to-expected spending difference was then used as the dependent variable in a multivariate model to determine the predictive contribution of a multitude of demographic, clinical, and utilization characteristics. The coefficients from this model yielded the information required to build predictive models for prospective use. Model variables related to medication adherence and historical utilization unexplained by disease burden proved to be more important predictors of impactability than any given diagnosis or event, disease profile, or overall costs of care. Comparison of this approach to alternative targeting strategies (emergency department super-utilizers, inpatient super-utilizers, or patients with highest Hierarchical Condition Category risk scores) suggests a 2- to 3-fold higher return on investment using impactability-based targeting.

Prediction of Low Community Sanitation Coverage Using Environmental and Sociodemographic Factors in Amhara Region, Ethiopia

PubMed Central

Oswald, William E.; Stewart, Aisha E. P.; Flanders, W. Dana; Kramer, Michael R.; Endeshaw, Tekola; Zerihun, Mulat; Melaku, Birhanu; Sata, Eshetu; Gessesse, Demelash; Teferi, Tesfaye; Tadesse, Zerihun; Guadie, Birhan; King, Jonathan D.; Emerson, Paul M.; Callahan, Elizabeth K.; Moe, Christine L.; Clasen, Thomas F.

2016-01-01

This study developed and validated a model for predicting the probability that communities in Amhara Region, Ethiopia, have low sanitation coverage, based on environmental and sociodemographic conditions. Community sanitation coverage was measured between 2011 and 2014 through trachoma control program evaluation surveys. Information on environmental and sociodemographic conditions was obtained from available data sources and linked with community data using a geographic information system. Logistic regression was used to identify predictors of low community sanitation coverage (< 20% versus ≥ 20%). The selected model was geographically and temporally validated. Model-predicted probabilities of low community sanitation coverage were mapped. Among 1,502 communities, 344 (22.90%) had coverage below 20%. The selected model included measures for high topsoil gravel content, an indicator for low-lying land, population density, altitude, and rainfall and had reasonable predictive discrimination (area under the curve = 0.75, 95% confidence interval = 0.72, 0.78). Measures of soil stability were strongly associated with low community sanitation coverage, controlling for community wealth, and other factors. A model using available environmental and sociodemographic data predicted low community sanitation coverage for areas across Amhara Region with fair discrimination. This approach could assist sanitation programs and trachoma control programs, scaling up or in hyperendemic areas, to target vulnerable areas with additional activities or alternate technologies. PMID:27430547

Using a Planetarium Software Program to Promote Conceptual Change with Young Children

ERIC Educational Resources Information Center

Hobson, Sally M.; Trundle, Kathy Cabe; Sackes, Mesut

2010-01-01

This study explored young children's understandings of targeted lunar concepts, including when the moon can be observed, observable lunar phase shapes, predictable lunar patterns, and the cause of lunar phases. Twenty-one children (ages 7-9 years) from a multi-aged, self-contained classroom participated in this study. The instructional…

The physics basis for ignition using indirect-drive targets on the National Ignition Facility

NASA Astrophysics Data System (ADS)

Lindl, John D.; Amendt, Peter; Berger, Richard L.; Glendinning, S. Gail; Glenzer, Siegfried H.; Haan, Steven W.; Kauffman, Robert L.; Landen, Otto L.; Suter, Laurence J.

2004-02-01

The 1990 National Academy of Science final report of its review of the Inertial Confinement Fusion Program recommended completion of a series of target physics objectives on the 10-beam Nova laser at the Lawrence Livermore National Laboratory as the highest-priority prerequisite for proceeding with construction of an ignition-scale laser facility, now called the National Ignition Facility (NIF). These objectives were chosen to demonstrate that there was sufficient understanding of the physics of ignition targets that the laser requirements for laboratory ignition could be accurately specified. This research on Nova, as well as additional research on the Omega laser at the University of Rochester, is the subject of this review. The objectives of the U.S. indirect-drive target physics program have been to experimentally demonstrate and predictively model hohlraum characteristics, as well as capsule performance in targets that have been scaled in key physics variables from NIF targets. To address the hohlraum and hydrodynamic constraints on indirect-drive ignition, the target physics program was divided into the Hohlraum and Laser-Plasma Physics (HLP) program and the Hydrodynamically Equivalent Physics (HEP) program. The HLP program addresses laser-plasma coupling, x-ray generation and transport, and the development of energy-efficient hohlraums that provide the appropriate spectral, temporal, and spatial x-ray drive. The HEP experiments address the issues of hydrodynamic instability and mix, as well as the effects of flux asymmetry on capsules that are scaled as closely as possible to ignition capsules (hydrodynamic equivalence). The HEP program also addresses other capsule physics issues associated with ignition, such as energy gain and energy loss to the fuel during implosion in the absence of alpha-particle deposition. The results from the Nova and Omega experiments approach the NIF requirements for most of the important ignition capsule parameters, including drive temperature, drive symmetry, and hydrodynamic instability. This paper starts with a review of the NIF target designs that have formed the motivation for the goals of the target physics program. Following that are theoretical and experimental results from Nova and Omega relevant to the requirements of those targets. Some elements of this work were covered in a 1995 review of indirect-drive [J. D. Lindl, ``Development of the indirect-drive approach to inertial confinement fusion and the target physics basis for ignition and gain,'' Phys. Plasmas 2, 3933 (1995)]. In order to present as complete a picture as possible of the research that has been carried out on indirect drive, key elements of that earlier review are also covered here, along with a review of work carried out since 1995.

Properties of Protein Drug Target Classes

PubMed Central

Bull, Simon C.; Doig, Andrew J.

2015-01-01

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID:25822509

Predicting nursing home placement among home- and community-based services program participants.

PubMed

Greiner, Melissa A; Qualls, Laura G; Iwata, Isao; White, Heidi K; Molony, Sheila L; Sullivan, M Terry; Burke, Bonnie; Schulman, Kevin A; Setoguchi, Soko

2014-12-01

Several states offer publicly funded-care management programs to prevent long-term care placement of high-risk Medicaid beneficiaries. Understanding participant risk factors and services that may prevent long-term care placement can facilitate efficient allocation of program resources. To develop a practical prediction model to identify participants in a home- and community-based services program who are at highest risk for long-term nursing home placement, and to examine participant-level and program-level predictors of nursing home placement. In a retrospective observational study, we used deidentified data for participants in the Connecticut Home Care Program for Elders who completed an annual assessment survey between 2005 and 2010. We analyzed data on patient characteristics, use of program services, and short-term facility admissions in the previous year. We used logistic regression models with random effects to predict nursing home placement. The main outcome measures were long-term nursing home placement within 180 days or 1 year of assessment. Among 10,975 study participants, 1249 (11.4%) had nursing home placement within 1 year of annual assessment. Risk factors included Alzheimer's disease (odds ratio [OR], 1.30; 95% CI, 1.18-1.43), money management dependency (OR, 1.33; 95% CI, 1.18-1.51), living alone (OR, 1.53; 95% CI, 1.31-1.80), and number of prior short-term skilled nursing facility stays (OR, 1.46; 95% CI, 1.31-1.62). Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. The model C statistic was 0.76 in the validation cohort. A model using information from a home- and community-based service program had strong discrimination to predict risk of long-term nursing home placement and can be used to identify high-risk participants for targeted interventions.

Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method.

PubMed

Armutlu, Pelin; Ozdemir, Muhittin E; Uney-Yuksektepe, Fadime; Kavakli, I Halil; Turkay, Metin

2008-10-03

A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

A new frontier: applying comprehensive DM strategies to healthy members.

PubMed

2001-02-01

Extend disease management services to 'healthy' members. Why? Because with all the emphasis on high-cost, chronically ill patients, a health plan's most valuable asset is being ignored. At least that's the case being made by Nashville, TN-based American Healthways, which has rolled out a new program designed to put health plans in touch with all their members, not just the chronically ill. Through the use of predictive modeling, the program is designed to target higher risk members so preventive strategies can be employed.

Frnakenstein: multiple target inverse RNA folding.

PubMed

Lyngsø, Rune B; Anderson, James W J; Sizikova, Elena; Badugu, Amarendra; Hyland, Tomas; Hein, Jotun

2012-10-09

RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Frnakenstein: multiple target inverse RNA folding

PubMed Central

2012-01-01

Background RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard. Results In this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets. Conclusions Our method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein. PMID:23043260

TargetCompare: A web interface to compare simultaneous miRNAs targets

PubMed Central

Moreira, Fabiano Cordeiro; Dustan, Bruno; Hamoy, Igor G; Ribeiro-dos-Santos, André M; dos Santos, Ândrea Ribeiro

2014-01-01

MicroRNAs (miRNAs) are small non-coding nucleotide sequences between 17 and 25 nucleotides in length that primarily function in the regulation of gene expression. A since miRNA has thousand of predict targets in a complex, regulatory cell signaling network. Therefore, it is of interest to study multiple target genes simultaneously. Hence, we describe a web tool (developed using Java programming language and MySQL database server) to analyse multiple targets of pre-selected miRNAs. We cross validated the tool in eight most highly expressed miRNAs in the antrum region of stomach. This helped to identify 43 potential genes that are target of at least six of the referred miRNAs. The developed tool aims to reduce the randomness and increase the chance of selecting strong candidate target genes and miRNAs responsible for playing important roles in the studied tissue. Availability http://lghm.ufpa.br/targetcompare PMID:25352731

TargetCompare: A web interface to compare simultaneous miRNAs targets.

PubMed

Moreira, Fabiano Cordeiro; Dustan, Bruno; Hamoy, Igor G; Ribeiro-Dos-Santos, André M; Dos Santos, Andrea Ribeiro

2014-01-01

MicroRNAs (miRNAs) are small non-coding nucleotide sequences between 17 and 25 nucleotides in length that primarily function in the regulation of gene expression. A since miRNA has thousand of predict targets in a complex, regulatory cell signaling network. Therefore, it is of interest to study multiple target genes simultaneously. Hence, we describe a web tool (developed using Java programming language and MySQL database server) to analyse multiple targets of pre-selected miRNAs. We cross validated the tool in eight most highly expressed miRNAs in the antrum region of stomach. This helped to identify 43 potential genes that are target of at least six of the referred miRNAs. The developed tool aims to reduce the randomness and increase the chance of selecting strong candidate target genes and miRNAs responsible for playing important roles in the studied tissue. http://lghm.ufpa.br/targetcompare.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

PubMed

Bohari, Mohammed H; Sastry, G Narahari

2012-09-01

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

PubMed Central

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-01-01

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. PMID:27958331

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

PubMed

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia.

PubMed

Skene, Nathan G; Roy, Marcia; Grant, Seth Gn

2017-09-12

The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome trajectories defined a sequence of gene expression changes in neuronal, glial and endothelial cell-types, which enabled prediction of age from tissue samples. A major lifespan landmark was the peak change in trajectories occurring in humans at 26 years and in mice at 5 months of age. This species-conserved peak was delayed in females and marked a reorganization of expression of synaptic and schizophrenia-susceptibility genes. The lifespan calendar predicted the characteristic age of onset in young adults and sex differences in schizophrenia. We propose a genomic program generates a lifespan calendar of gene regulation that times age-dependent molecular organization of the brain and mutations that interrupt the program in young adults cause schizophrenia.

A predictive model of hospitalization risk among disabled medicaid enrollees.

PubMed

McAna, John F; Crawford, Albert G; Novinger, Benjamin W; Sidorov, Jaan; Din, Franklin M; Maio, Vittorio; Louis, Daniel Z; Goldfarb, Neil I

2013-05-01

To identify Medicaid patients, based on 1 year of administrative data, who were at high risk of admission to a hospital in the next year, and who were most likely to benefit from outreach and targeted interventions. Observational cohort study for predictive modeling. Claims, enrollment, and eligibility data for 2007 from a state Medicaid program were used to provide the independent variables for a logistic regression model to predict inpatient stays in 2008 for fully covered, continuously enrolled, disabled members. The model was developed using a 50% random sample from the state and was validated against the other 50%. Further validation was carried out by applying the parameters from the model to data from a second state's disabled Medicaid population. The strongest predictors in the model developed from the first 50% sample were over age 65 years, inpatient stay(s) in 2007, and higher Charlson Comorbidity Index scores. The areas under the receiver operating characteristic curve for the model based on the 50% state sample and its application to the 2 other samples ranged from 0.79 to 0.81. Models developed independently for all 3 samples were as high as 0.86. The results show a consistent trend of more accurate prediction of hospitalization with increasing risk score. This is a fairly robust method for targeting Medicaid members with a high probability of future avoidable hospitalizations for possible case management or other interventions. Comparison with a second state's Medicaid program provides additional evidence for the usefulness of the model.

Estimating Landholders’ Probability of Participating in a Stewardship Program, and the Implications for Spatial Conservation Priorities

PubMed Central

Adams, Vanessa M.; Pressey, Robert L.; Stoeckl, Natalie

2014-01-01

The need to integrate social and economic factors into conservation planning has become a focus of academic discussions and has important practical implications for the implementation of conservation areas, both private and public. We conducted a survey in the Daly Catchment, Northern Territory, to inform the design and implementation of a stewardship payment program. We used a choice model to estimate the likely level of participation in two legal arrangements - conservation covenants and management agreements - based on payment level and proportion of properties required to be managed. We then spatially predicted landholders’ probability of participating at the resolution of individual properties and incorporated these predictions into conservation planning software to examine the potential for the stewardship program to meet conservation objectives. We found that the properties that were least costly, per unit area, to manage were also the least likely to participate. This highlights a tension between planning for a cost-effective program and planning for a program that targets properties with the highest probability of participation. PMID:24892520

Reverse screening methods to search for the protein targets of chemopreventive compounds

NASA Astrophysics Data System (ADS)

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-05-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds.

PubMed

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-01-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

PubMed Central

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-01-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction. PMID:29868550

Modeling the Impact and Costs of Semiannual Mass Drug Administration for Accelerated Elimination of Lymphatic Filariasis

PubMed Central

de Vlas, Sake J.; Fischer, Peter U.; Weil, Gary J.; Goldman, Ann S.

2013-01-01

The Global Program to Eliminate Lymphatic Filariasis (LF) has a target date of 2020. This program is progressing well in many countries. However, progress has been slow in some countries, and others have not yet started their mass drug administration (MDA) programs. Acceleration is needed. We studied how increasing MDA frequency from once to twice per year would affect program duration and costs by using computer simulation modeling and cost projections. We used the LYMFASIM simulation model to estimate how many annual or semiannual MDA rounds would be required to eliminate LF for Indian and West African scenarios with varied pre-control endemicity and coverage levels. Results were used to estimate total program costs assuming a target population of 100,000 eligibles, a 3% discount rate, and not counting the costs of donated drugs. A sensitivity analysis was done to investigate the robustness of these results with varied assumptions for key parameters. Model predictions suggested that semiannual MDA will require the same number of MDA rounds to achieve LF elimination as annual MDA in most scenarios. Thus semiannual MDA programs should achieve this goal in half of the time required for annual programs. Due to efficiency gains, total program costs for semiannual MDA programs are projected to be lower than those for annual MDA programs in most scenarios. A sensitivity analysis showed that this conclusion is robust. Semiannual MDA is likely to shorten the time and lower the cost required for LF elimination in countries where it can be implemented. This strategy may improve prospects for global elimination of LF by the target year 2020. PMID:23301115

Laser long-range remote-sensing program experimental results

NASA Astrophysics Data System (ADS)

Highland, Ronald G.; Shilko, Michael L.; Fox, Marsha J.; Gonglewski, John D.; Czyzak, Stanley R.; Dowling, James A.; Kelly, Brian; Pierrottet, Diego F.; Ruffatto, Donald; Loando, Sharon; Matsuura, Chris; Senft, Daniel C.; Finkner, Lyle; Rae, Joe; Gallegos, Joe

1995-12-01

A laser long range remote sensing (LRS) program is being conducted by the United States Air Force Phillips Laboratory (AF/PL). As part of this program, AF/PL is testing the feasibility of developing a long path CO(subscript 2) laser-based DIAL system for remote sensing. In support of this program, the AF/PL has recently completed an experimental series using a 21 km slant- range path (3.05 km ASL transceiver height to 0.067 km ASL target height) at its Phillips Laboratory Air Force Maui Optical Station (AMOS) facility located on Maui, Hawaii. The dial system uses a 3-joule, (superscript 13)C isotope laser coupled into a 0.6 m diameter telescope. The atmospheric optical characterization incorporates information from an infrared scintillometer co-aligned to the laser path, atmospheric profiles from weather balloons launched from the target site, and meteorological data from ground stations at AMOS and the target site. In this paper, we report a description of the experiment configuration, a summary of the results, a summary of the atmospheric conditions and their implications to the LRS program. The capability of such a system for long-range, low-angle, slant-path remote sensing is discussed. System performance issues relating to both coherent and incoherent detection methods, atmospheric limitations, as well as, the development of advanced models to predict performance of long range scenarios are presented.

Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination.

PubMed

Hall, Jane; Kenny, Patricia; King, Madeleine; Louviere, Jordan; Viney, Rosalie; Yeoh, Angela

2002-07-01

Applications of stated preference discrete choice modelling (SPDCM) in health economics have been used to estimate consumer willingness to pay and to broaden the range of consequences considered in economic evaluation. This paper demonstrates how SPDCM can be used to predict participation rates, using the case of varicella (chickenpox) vaccination. Varicella vaccination may be cost effective compared to other public health programs, but this conclusion is sensitive to the proportion of the target population immunised. A choice experiment was conducted on a sample of Australian parents to predict uptake across a range of hypothetical programs. Immunisation rates would be increased by providing immunisation at no cost, by requiring it for school entry, by increasing immunisation rates in the community and decreasing the incidence of mild and severe side effects. There were two significant interactions; price modified the effect of both support from authorities and severe side effects. Country of birth was the only significant demographic characteristic. Depending on aspects of the immunisation program, the immunisation rates of children with Australian-born parents varied from 9% to 99% while for the children with parents born outside Australia they varied from 40% to 99%. This demonstrates how SPDCM can be used to understand the levels of attributes that will induce a change in the decision to immunise, the modification of the effect of one attribute by another, and subgroups in the population. Such insights can contribute to the optimal design and targeting of health programs. Copyright 2002 John Wiley & Sons, Ltd.

The Subseasonal Experiment (SubX) to Advance National Weather Service Predictions for Weeks 3-4

NASA Astrophysics Data System (ADS)

Mariotti, A.; Barrie, D.; Archambault, H. M.

2017-12-01

There is great practical interest in developing skillful predictions of extremes for lead times extending beyond the two-week theoretical predictability skill barrier for weather forecasts to the subseasonal-to-seasonal (S2S) time scale. The processes and phenomena specific to S2S are posited to require a unified approach to science, modeling, and predictions that draws expertise from both the weather and climate/seasonal communities. Based on this premise, in 2016, the NOAA Climate Program Office Modeling, Analysis, Predictions and Projections (MAPP) program, in partnership with the National Weather Service Office of Science and Technology Integration, launched a major research and transition initiative to meet NOAA's emerging research and transition needs for developing skillful S2S predictions. A major component of this initiative is an experiment to test single- and multi-model ensembles for subseasonal prediction, called the Subseasonal Experiment (SubX). SubX, which engages six modeling groups, is producing real time experimental forecasts based on weather, climate, and Earth system models for weeks 3-4. The project investigators are evaluating, testing, and optimizing this system, and the hindcast and real time forecast data are available to the broad community. SubX research is targeted at a number of important decision-making contexts including drought and extremes, as well as the broad variety of phenomena that are meaningful at subseasonal timescales (e.g., MJO, ENSO, stratosphere/troposphere coupling, etc.). This presentation will discuss the design and status of SubX in the broader context of MAPP program S2S prediction research.

Behavioral Activation Therapy for Depressed Cancer Patients: Factors Associated with Treatment Outcome and Attrition

ERIC Educational Resources Information Center

Hopko, D. R.; Robertson, S. M. C.; Colman, L.

2008-01-01

In recent years there has been increased focus on evaluating the efficacy of psychosocial interventions for cancer patients. Among the several limitations inherent to these programs of research, few studies have targeted patients with well-diagnosed clinical depression and little is known about factors that best predict treatment outcome and…

Assessing cross species conservation of ToxCast Assay targets using Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS)

EPA Science Inventory

US EPA’s ToxCast program has screened thousands of chemicals in hundreds of mammalian-based HTS assays for biological activity suggestive of potential toxic effects. These data are being used to prioritize toxicity testing to focus on chemicals likely to lead to adverse health ef...

Using PELA to Predict International Business Students' English Writing Performance with Contextualised English Writing Workshops as Intervention Program

ERIC Educational Resources Information Center

Wong, Caroline; Delante, Nimrod Lawsin; Wang, Pengji

2017-01-01

This study examines the effectiveness of Post-Entry English Language Assessment (PELA) as a predictor of international business students' English writing performance and academic performance. An intervention involving the implementation of contextualised English writing workshops was embedded in a specific business subject targeted at students who…

Transiting Exoplanet Studies and Community Targets for JWST's Early Release Science Program

NASA Technical Reports Server (NTRS)

Stevenson, Kevin B.; Lewis, Nikole K.; Bean, Jacob L.; Beichman, Charles A.; Fraine, Jonathan; Kilpatrick, Brian M.; Krick, J. E.; Lothringer, Joshua D.; Mandell, Avi M.; Valenti, Jeff A.;

Transiting Exoplanet Studies and Community Targets for JWST's Early Release Science Program

NASA Technical Reports Server (NTRS)

Stevenson, Kevin B.; Lewis, Nikole K.; Bean, Jacob L.; Beichman, Charles; Fraine, Jonathan; Kilpatrick, Brian M.; Krick, J. E.; Lothringer, Joshua D.; Mandell, Avi M.; Valenti, Jeff A.;

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

PubMed

Kuu, Wei Y; Nail, Steven L

2009-09-01

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

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ.

PubMed

Simpkin, Adam J; Rigden, Daniel J

2016-07-13

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target. Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface. The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter's druggable intrafilament interface as a potential drug target.

Stopping power of Au for Cu ions with energies below Bragg’s peak

NASA Astrophysics Data System (ADS)

Linares, R.; Freire, J. A.; Ribas, R. V.; Medina, N. H.; Oliveira, J. R. B.; Cybulska, E. W.; Seale, W. A.; Added, N.; Silveira, M. A. G.; Wiedemann, K. T.

2007-10-01

The stopping power of Au for Cu in the energy range 6 < E < 25 MeV was measured using a secondary beam of low velocity heavy ions produced by elastic scattering of an energetic primary beam (typically 28Si or 16O) on a natural Cu target. The results were compared to predictions of the Lindhard, Scharf and Schiott (LSS) theory, the binary theory (BT), and the unitary convolution approximation (UCA) and also to semi-empirical predictions such as the Northcliffe and Schilling tables and the SRIM2003 computer program.

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics.

PubMed

Hu, Ruifeng; Sun, Xiaobo

2016-08-01

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

Reinforcement learning and decision making in monkeys during a competitive game.

PubMed

Lee, Daeyeol; Conroy, Michelle L; McGreevy, Benjamin P; Barraclough, Dominic J

2004-12-01

Animals living in a dynamic environment must adjust their decision-making strategies through experience. To gain insights into the neural basis of such adaptive decision-making processes, we trained monkeys to play a competitive game against a computer in an oculomotor free-choice task. The animal selected one of two visual targets in each trial and was rewarded only when it selected the same target as the computer opponent. To determine how the animal's decision-making strategy can be affected by the opponent's strategy, the computer opponent was programmed with three different algorithms that exploited different aspects of the animal's choice and reward history. When the computer selected its targets randomly with equal probabilities, animals selected one of the targets more often, violating the prediction of probability matching, and their choices were systematically influenced by the choice history of the two players. When the computer exploited only the animal's choice history but not its reward history, animal's choice became more independent of its own choice history but was still related to the choice history of the opponent. This bias was substantially reduced, but not completely eliminated, when the computer used the choice history of both players in making its predictions. These biases were consistent with the predictions of reinforcement learning, suggesting that the animals sought optimal decision-making strategies using reinforcement learning algorithms.

RISC RNA sequencing for context-specific identification of in vivo miR targets

PubMed Central

Matkovich, Scot J; Van Booven, Derek J; Eschenbacher, William H; Dorn, Gerald W

2010-01-01

Rationale MicroRNAs (miRs) are expanding our understanding of cardiac disease and have the potential to transform cardiovascular therapeutics. One miR can target hundreds of individual mRNAs, but existing methodologies are not sufficient to accurately and comprehensively identify these mRNA targets in vivo. Objective To develop methods permitting identification of in vivo miR targets in an unbiased manner, using massively parallel sequencing of mouse cardiac transcriptomes in combination with sequencing of mRNA associated with mouse cardiac RNA-induced silencing complexes (RISCs). Methods and Results We optimized techniques for expression profiling small amounts of RNA without introducing amplification bias, and applied this to anti-Argonaute 2 immunoprecipitated RISCs (RISC-Seq) from mouse hearts. By comparing RNA-sequencing results of cardiac RISC and transcriptome from the same individual hearts, we defined 1,645 mRNAs consistently targeted to mouse cardiac RISCs. We employed this approach in hearts overexpressing miRs from Myh6 promoter-driven precursors (programmed RISC-Seq) to identify 209 in vivo targets of miR-133a and 81 in vivo targets of miR-499. Consistent with the fact that miR-133a and miR-499 have widely differing ‘seed’ sequences and belong to different miR families, only 6 targets were common to miR-133a- and miR-499-programmed hearts. Conclusions RISC-sequencing is a highly sensitive method for general RISC profiling and individual miR target identification in biological context, and is applicable to any tissue and any disease state. Summary MicroRNAs (miRs) are key regulators of mRNA translation in health and disease. While bioinformatic predictions suggest that a single miR may target hundreds of mRNAs, the number of experimentally verified targets of miRs is low. To enable comprehensive, unbiased examination of miR targets, we have performed deep RNA sequencing of cardiac transcriptomes in parallel with cardiac RNA-induced silencing complex (RISC)-associated RNAs (the RISCome), called RISC sequencing. We developed methods that did not require cross-linking of RNAs to RISCs or amplification of mRNA prior to sequencing, making it possible to rapidly perform RISC sequencing from intact tissue while avoiding amplification bias. Comparison of RISCome with transcriptome expression defined the degree of RISC enrichment for each mRNA. The majority of the mRNAs enriched in wild-type cardiac RISComes compared to transcriptomes were bioinformatically predicted to be targets of at least 1 of 139 cardiac-expressed miRs. Programming cardiomyocyte RISCs via transgenic overexpression in adult hearts of miR-133a or miR-499, two miRs that contain entirely different ‘seed’ sequences, elicited differing profiles of RISC-targeted mRNAs. Thus, RISC sequencing represents a highly sensitive method for general RISC profiling and individual miR target identification in biological context. PMID:21030712

Molecular classification of gastric adenocarcinoma: translating new insights from the cancer genome atlas research network.

PubMed

Sunakawa, Yu; Lenz, Heinz-Josef

2015-04-01

Gastric cancer is a heterogenous cancer, which may be classified into several distinct subtypes based on pathology and epidemiology, each with different initiating pathological processes and each possibly having different tumor biology. A classification of gastric cancer should be important to select patients who can benefit from the targeted therapies or to precisely predict prognosis. The Cancer Genome Atlas (TCGA) study collaborated with previous reports regarding subtyping gastric cancer but also proposed a refined classification based on molecular characteristics. The addition of the new molecular classification strategy to a current classical subtyping may be a promising option, particularly stratification by Epstein-Barr virus (EBV) and microsatellite instability (MSI) statuses. According to TCGA study, EBV gastric cancer patients may benefit the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibodies or phosphoinositide 3-kinase (PI3K) inhibitors which are now being developed. The discoveries of predictive biomarkers should improve patient care and individualized medicine in the management since the targeted therapies may have the potential to change the landscape of gastric cancer treatment, moreover leading to both better understanding of the heterogeneity and better outcomes. Patient enrichment by predictive biomarkers for new treatment strategies will be critical to improve clinical outcomes. Additionally, liquid biopsies will be able to enable us to monitor in real-time molecular escape mechanism, resulting in better treatment strategies.

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.

Assessment of cancer and virus antigens for cross-reactivity in human tissues.

PubMed

Jaravine, Victor; Raffegerst, Silke; Schendel, Dolores J; Frishman, Dmitrij

2017-01-01

Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens. We developed a methodology for the calculation of quantitative CR for any defined peptide epitope. Using this approach, we performed assessment of 4 groups of 283 currently known human MHC-class-I epitopes including differentiation antigens, overexpressed proteins, cancer-testis antigens and mutations displayed by tumor cells. In addition, 89 epitopes originating from viral sources were investigated. The natural occurrence of these epitopes in human tissues was assessed based on proteomics abundance data, while the probability of their presentation by MHC-class-I molecules was modelled by the method of Keşmir et al. which combines proteasomal cleavage, TAP affinity and MHC-binding predictions. The results of these analyses for many previously defined peptides are presented as CR indices and tissue profiles. The methodology thus allows for quantitative comparisons of epitopes and is suggested to be suited for the assessment of epitopes of candidate antigens in an early stage of development of adoptive immunotherapy. Our method is implemented as a Java program, with curated datasets stored in a MySQL database. It predicts all naturally possible self-antigens for a given sequence of a therapeutic antigen (or epitope) and after filtering for predicted immunogenicity outputs results as an index and profile of CR to the self-antigens in 22 human tissues. The program is implemented as part of the iCrossR webserver, which is publicly available at http://webclu.bio.wzw.tum.de/icrossr/ CONTACT: d.frishman@wzw.tum.deSupplementary 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.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

Hao, Ming; Bryant, Stephen H; Wang, Yanli

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

Decoding Target Distance and Saccade Amplitude from Population Activity in the Macaque Lateral Intraparietal Area (LIP)

PubMed Central

Bremmer, Frank; Kaminiarz, Andre; Klingenhoefer, Steffen; Churan, Jan

2016-01-01

Primates perform saccadic eye movements in order to bring the image of an interesting target onto the fovea. Compared to stationary targets, saccades toward moving targets are computationally more demanding since the oculomotor system must use speed and direction information about the target as well as knowledge about its own processing latency to program an adequate, predictive saccade vector. In monkeys, different brain regions have been implicated in the control of voluntary saccades, among them the lateral intraparietal area (LIP). Here we asked, if activity in area LIP reflects the distance between fovea and saccade target, or the amplitude of an upcoming saccade, or both. We recorded single unit activity in area LIP of two macaque monkeys. First, we determined for each neuron its preferred saccade direction. Then, monkeys performed visually guided saccades along the preferred direction toward either stationary or moving targets in pseudo-randomized order. LIP population activity allowed to decode both, the distance between fovea and saccade target as well as the size of an upcoming saccade. Previous work has shown comparable results for saccade direction (Graf and Andersen, 2014a,b). Hence, LIP population activity allows to predict any two-dimensional saccade vector. Functional equivalents of macaque area LIP have been identified in humans. Accordingly, our results provide further support for the concept of activity from area LIP as neural basis for the control of an oculomotor brain-machine interface. PMID:27630547

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method.

PubMed

Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.

Loss of PTEN as a Predictive Biomarker of Response to Lithium Chloride, A Potential Targeted Treatment for Breast Cancer

DTIC Science & Technology

2013-11-01

COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER...The grant was awarded on June 1st 2010. Dr. Higgins graduated from her Oncology Fellowship Program in Johns Hopkins Hospital on June 30th and... grant to support Dr. Higgins as she continued this work was submitted and processed in June 2010 in anticipation of this move. Johns Hopkins University

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

PubMed

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

Excitation of levels in Li6 by inelastic electron scattering

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

Bernheim, M.; Bishop, G. R.

1963-07-01

Inelastic scattering of electrons from metallic targets of Li 6 was studied as part of a program to establish the validity of the Born approximation calculation of the cross section. This calculation predicts the separation of the inelastic form factor into two contributions corresponding to the absorption of longitudinal and transverse virtual photons by the bombarded system. (R.E.U.)

Influence of foraging behavior and host spatial distribution on the localized spread of the emerald ash borer, Agrilus planipennis

Treesearch

Rodrigo J. Mercader; Nathan W. Siegert; Andrew M. Liebhold; Deborah G. McCullough

2011-01-01

Management programs for invasive species are often developed at a regional or national level, but physical intervention generally takes place over relatively small areas occupied by newly founded, isolated populations. The ability to predict how local habitat variation affects the expansion of such newly founded populations is essential for efficiently targeting...

Content Not Form Predicts Oral Language Comprehension: The Influence of the Medium on Preschoolers' Story Understanding

ERIC Educational Resources Information Center

Neuman, Susan B.; Wong, Kevin M.; Kaefer, Tanya

2017-01-01

The purpose of this study was to investigate the influence of digital and non-digital storybooks on low-income preschoolers' oral language comprehension. Employing a within-subject design on 38 four-year-olds from a Head Start program, we compared the effect of medium on preschoolers' target words and comprehension of stories. Four digital…

Increasing Male Involvement in Family Planning Decision Making: Trial of a Social-Cognitive Intervention in Rural Vietnam

ERIC Educational Resources Information Center

Ha, Bui Thi Thu; Jayasuriya, Rohan; Owen, Neville

2005-01-01

We tested a social-cognitive intervention to influence contraceptive practices among men living in rural communes in Vietnam. It was predicted that participants who received a stage-targeted program based on the Transtheoretical Model (TTM) would report positive movement in their stage of motivational readiness for their wife to use an…

Post-Flight Assessment of Low Density Supersonic Decelerator Flight Dynamics Test 2 Simulation

NASA Technical Reports Server (NTRS)

Dutta, Soumyo; Bowes, Angela L.; White, Joseph P.; Striepe, Scott A.; Queen, Eric M.; O'Farrel, Clara; Ivanov, Mark C.

2016-01-01

NASA's Low Density Supersonic Decelerator (LDSD) project conducted its second Supersonic Flight Dynamics Test (SFDT-2) on June 8, 2015. The Program to Optimize Simulated Trajectories II (POST2) was one of the flight dynamics tools used to simulate and predict the flight performance and was a major tool used in the post-flight assessment of the flight trajectory. This paper compares the simulation predictions with the reconstructed trajectory. Additionally, off-nominal conditions seen during flight are modeled in the simulation to reconcile the predictions with flight data. These analyses are beneficial to characterize the results of the flight test and to improve the simulation and targeting of the subsequent LDSD flights.

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

PubMed

Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer

PubMed Central

2017-01-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC. PMID:29114473

Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays

PubMed Central

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-01-01

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts. PMID:28068291

Particle swarm optimization for programming deep brain stimulation arrays

NASA Astrophysics Data System (ADS)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-02-01

Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of  <1% between approaches. Significance. The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

Screening for Cryptococcal Antigenaemia in Patients Accessing an Antiretroviral Treatment Program in South Africa

PubMed Central

Jarvis, Joseph N; Lawn, Stephen D; Vogt, Monica; Bangani, Nonzwakazi; Wood, Robin; Harrison, Thomas S

2009-01-01

Background Cryptococcal meningitis is a leading cause of death in AIDS patients and contributes substantially to the high early mortality in antiretroviral treatment (ART) programs in low-resource settings. Screening for cryptococcal antigen (CRAG) in patients enrolling in ART programs may identify those at risk of cryptococcal meningitis and permit targeted use of pre-emptive therapy. Methods In this retrospective study, CRAG was measured in stored plasma samples obtained from patients as they enrolled in a well characterised ART cohort in South Africa. The predictive value of screening for CRAG prior to ART for development of microbiologically confirmed cryptococcal meningitis or death during the first year of follow-up was determined. Results Of 707 participants with a baseline median CD4 count of 97 (IQR 46-157) cells/μL, 46 (7%) had a positive CRAG. Antigenaemia was 100% sensitive for predicting development of cryptococcal meningitis during the first year of ART and in multivariate analysis was an independent predictor of mortality (AHR 3.2, 95%CI 1.5-6.6). Most (92%) cases of cryptococcal meningitis developed in patients with a CD4 count ≤100 cells/μL. In this sub-set of patients, a CRAG titre ≥1 in 8 was 100% sensitive and 96% specific for predicting incident cryptococcal meningitis during the first year of ART in those with no previous history of the disease. Conclusions CRAG screening prior to commencing ART in patients with a CD4 count ≤100 cells/μL is highly effective at identifying those at risk of cryptococcal meningitis and death and might permit implementation of a targeted pre-emptive treatment strategy. PMID:19222372

Workday Sitting Time and Marital Status: Novel Pretreatment Predictors of Weight Loss in Overweight and Obese Men.

PubMed

Morgan, Philip J; Hollis, Jenna L; Young, Myles D; Collins, Clare E; Teixeira, Pedro J

2016-06-20

The evidence base for weight loss programs in men is limited. Gaining a greater understanding of which personal characteristics and pretreatment behaviors predict weight loss and attrition in male-only studies would be useful to inform the development of future interventions for men. In December 2010, 159 overweight/obese men (mean age = 47.5 years; body mass index = 32.7 kg/m 2 ) from the Hunter Region of New South Wales, Australia, participated in a randomized controlled trial testing the effectiveness of two versions of a 3-month gender-targeted weight loss program. In the current analyses, social-cognitive, behavioral, and demographic pretreatment characteristics were examined to determine if they predicted weight loss and attrition in the participants over 6 months. Generalized linear mixed models (intention-to-treat) revealed weight change was associated with education level (p = .02), marital status (p = .03), fat mass (p = .045), sitting time on nonwork (p = .046), and workdays (p = .03). Workday sitting time and marital status accounted for 6.5% (p = .01) of the variance in the final model. Attrition was associated with level of education (p = .01) and body fat percentage (p = .01), accounting for 9.5% (p = .002) of the variance in the final model. This study suggests men who spend a lot of time sitting at work, especially those who are not married, may require additional support to experience success in self-administered weight loss programs targeting males. Additional high-quality evidence is needed to improve the understanding which pretreatment behaviors and characteristics predict weight loss and attrition in men. © The Author(s) 2016.

Thermographer-friendly equipment design for predictive maintenance: baseline thermograms, thermal modeling, and emissivity

NASA Astrophysics Data System (ADS)

Madding, Robert P.

1999-03-01

For years predictive maintenance thermographers have been challenged by industrial targets to determine whether they had a problem, and if they did how big was it. We have struggled with low emissivity and unknown emissivity targets. We have observed thermal patterns and temperatures and asked whether the target was operating normally or if the heat patterns indicated a problem condition. Through years of experience, we have built a body of knowledge. Conferences such as Thermosense are where we share that knowledge with others. From this, we realize that much more could be done if our targets were thermographer-friendly. Now it is time to ask the equipment manufacturers to step up to the plate and acknowledge the viability of thermography as a predictive maintenance and non-destructive test tool. They build the targets we look at. They can help us in a least three areas: (1) We need to work with them to specify a baseline thermal signature for their equipment operating under normal conditions. Thermograms would be included with the operating manual or equipment test results. Thermography would be part of acceptance and installation testing. (2) We need to ask them to include high emissivity coatings in their designs for certain targets. (3) We need to work with them to develop thermal models that will indicate thermal signatures under all types of environmental conditions for both normal and abnormal operation. Thermal modeling programs developed by the defense community that will display a surface thermal image are available for PCs. With the help of target equipment manufacturers, we can significantly advance the state-of-the- art of thermography applications. We can be even more confident of our recommendations. We can evaluate targets that couldn't be evaluated before, expanding our applications. We can have backup on criticality calls with manufacturers' data. In short, we can do our job better.

Musashi2 sustains the mixed-lineage leukemia–driven stem cell regulatory program

PubMed Central

Park, Sun-Mi; Gönen, Mithat; Vu, Ly; Minuesa, Gerard; Tivnan, Patrick; Barlowe, Trevor S.; Taggart, James; Lu, Yuheng; Deering, Raquel P.; Hacohen, Nir; Figueroa, Maria E.; Paietta, Elisabeth; Fernandez, Hugo F.; Tallman, Martin S.; Melnick, Ari; Levine, Ross; Leslie, Christina; Lengner, Christopher J.; Kharas, Michael G.

2015-01-01

Leukemia stem cells (LSCs) are found in most aggressive myeloid diseases and contribute to therapeutic resistance. Leukemia cells exhibit a dysregulated developmental program as the result of genetic and epigenetic alterations. Overexpression of the RNA-binding protein Musashi2 (MSI2) has been previously shown to predict poor survival in leukemia. Here, we demonstrated that conditional deletion of Msi2 in the hematopoietic compartment results in delayed leukemogenesis, reduced disease burden, and a loss of LSC function in a murine leukemia model. Gene expression profiling of these Msi2-deficient animals revealed a loss of the hematopoietic/leukemic stem cell self-renewal program and an increase in the differentiation program. In acute myeloid leukemia patients, the presence of a gene signature that was similar to that observed in Msi2-deficent murine LSCs correlated with improved survival. We determined that MSI2 directly maintains the mixed-lineage leukemia (MLL) self-renewal program by interacting with and retaining efficient translation of Hoxa9, Myc, and Ikzf2 mRNAs. Moreover, depletion of MLL target Ikzf2 in LSCs reduced colony formation, decreased proliferation, and increased apoptosis. Our data provide evidence that MSI2 controls efficient translation of the oncogenic LSC self-renewal program and suggest MSI2 as a potential therapeutic target for myeloid leukemia. PMID:25664853

Regulatory off-gas analysis from the evaporation of Hanford simulated waste spiked with organic compounds.

PubMed

Saito, Hiroshi H; Calloway, T Bond; Ferrara, Daro M; Choi, Alexander S; White, Thomas L; Gibson, Luther V; Burdette, Mark A

2004-10-01

After strontium/transuranics removal by precipitation followed by cesium/technetium removal by ion exchange, the remaining low-activity waste in the Hanford River Protection Project Waste Treatment Plant is to be concentrated by evaporation before being mixed with glass formers and vitrified. To provide a technical basis to permit the waste treatment facility, a relatively organic-rich Hanford Tank 241-AN-107 waste simulant was spiked with 14 target volatile, semi-volatile, and pesticide compounds and evaporated under vacuum in a bench-scale natural circulation evaporator fitted with an industrial stack off-gas sampler at the Savannah River National Laboratory. An evaporator material balance for the target organics was calculated by combining liquid stream mass and analytical data with off-gas emissions estimates obtained using U.S. Environmental Protection Agency (EPA) SW-846 Methods. Volatile and light semi-volatile organic compounds (<220 degrees C BP, >1 mm Hg vapor pressure) in the waste simulant were found to largely exit through the condenser vent, while heavier semi-volatiles and pesticides generally remain in the evaporator concentrate. An OLI Environmental Simulation Program (licensed by OLI Systems, Inc.) evaporator model successfully predicted operating conditions and the experimental distribution of the fed target organics exiting in the concentrate, condensate, and off-gas streams, with the exception of a few semi-volatile and pesticide compounds. Comparison with Henry's Law predictions suggests the OLI Environmental Simulation Program model is constrained by available literature data.

Note on the artefacts in SRIM simulation of sputtering

NASA Astrophysics Data System (ADS)

Shulga, V. I.

2018-05-01

The computer simulation program SRIM, unlike other well-known programs (MARLOWE, TRIM.SP, etc.), predicts non-zero values of the sputter yield at glancing ion bombardment of smooth amorphous targets and, for heavy ions, greatly underestimates the sputter yield at normal incidence. To understand the reasons for this, the sputtering of amorphous silicon bombarded with different ions was modeled here using the author's program OKSANA. Most simulations refer to 1 keV Xe ions, and angles of incidence cover range from 0 (normal incidence) to almost 90°. It has been shown that SRIM improperly simulates the initial stage of the sputtering process. Some other artefacts in SRIM calculations of sputtering are also revealed and discussed.

Processes in the Resolution of Ambiguous Words: Towards a Model of Selective Inhibition. Cognitive Science Program, Technical Report No. 86-6.

ERIC Educational Resources Information Center

Yee, Penny L.

This study investigates the role of specific inhibitory processes in lexical ambiguity resolution. An attentional view of inhibition and a view based on specific automatic inhibition between nodes predict different results when a neutral item is processed between an ambiguous word and a related target. Subjects were 32 English speakers with normal…

Assessing Post Conflict State Building Efforts

DTIC Science & Technology

2013-03-01

Develop a global partnership for development Target: Develop further an open, rule-based, predictable, non-discriminatory trading and finance system...Beginner’s Guide to Nation-Building (Santa Monica, CA: Rand Corporation ), 2007, xvii. 15 Samuel Berger, Brent Scowcroft, and William L. Nash, “In...The Beginner’s Guide, xxi. 25 Richard A. Berk and Peter H. Rossi, Thinking About Program Evaluation 2, (Thousand Oaks, CA: Sage Publications, 1999

Universal school-based substance abuse prevention programs: Modeling targeted mediators and outcomes for adolescent cigarette, alcohol and marijuana use.

PubMed

Stephens, Peggy C; Sloboda, Zili; Stephens, Richard C; Teasdale, Brent; Grey, Scott F; Hawthorne, Richard D; Williams, Joseph

2009-06-01

We examined the relationships among targeted constructs of social influences and competence enhancement prevention curricula and cigarette, alcohol and marijuana use outcomes in a diverse sample of high school students. We tested the causal relationships of normative beliefs, perceptions of harm, attitudes toward use of these substances and refusal, communication, and decision-making skills predicting the self-reported use of each substance. In addition, we modeled the meditation of these constructs through the intentions to use each substance and tested the moderating effects of the skills variables on the relationships between intentions to use and self-reported use of each of these substances. Logistic regression path models were constructed for each of the drug use outcomes. Models were run using the Mplus 5.0 statistical application using the complex sample function to control for the sampling design of students nested within schools; full information maximum likelihood estimates (FIML) were utilized to address missing data. Relationships among targeted constructs and outcomes differed for each of the drugs with communication skills having a potentially iatrogenic effect on alcohol use. Program targets were mediated through the intentions to use these substances. Finally, we found evidence of a moderating effect of decision-making skills on perceptions of harm and attitudes toward use, depending upon the outcome. Prevention curricula may need to target specific drugs. In addition to normative beliefs, perceptions of harm, and refusal and decision-making skills, programs should directly target constructs proximal to behavioral outcomes such as attitudes and intentions. Finally, more research on the effects of communication skills on adolescent substance use should be examined.

Clinical Implementation of Novel Targeted Therapeutics in Advanced Breast Cancer.

PubMed

Chamberlin, Mary D; Bernhardt, Erica B; Miller, Todd W

2016-11-01

The majority of advanced breast cancers have genetic alterations that are potentially targetable with drugs. Through initiatives such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), data can be mined to provide context for next-generation sequencing (NGS) results in the landscape of advanced breast cancer. Therapies for targets other than estrogen receptor alpha (ER) and HER2, such as cyclin-dependent kinases CDK4 and CDK6, were recently approved based on efficacy in patient subpopulations, but no predictive biomarkers have been found, leaving clinicians to continue a trial-and-error approach with each patient. Next-generation sequencing identifies potentially actionable alterations in genes thought to be drivers in the cancerous process including phosphatidylinositol 3-kinase (PI3K), AKT, fibroblast growth factor receptors (FGFRs), and mutant HER2. Epigenetically directed and immunologic therapies have also shown promise for the treatment of breast cancer via histone deacetylases (HDAC) 1 and 3, programmed T cell death 1 (PD-1), and programmed T cell death ligand 1 (PD-L1). Identifying biomarkers to predict primary resistance in breast cancer will ultimately affect clinical decisions regarding adjuvant therapy in the first-line setting. However, the bulk of medical decision-making is currently made in the secondary resistance setting. Herein, we review the clinical potential of PI3K, AKT, FGFRs, mutant HER2, HDAC1/3, PD-1, and PD-L1 as therapeutic targets in breast cancer, focusing on the rationale for therapeutic development and the status of clinical testing. J. Cell. Biochem. 117: 2454-2463, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Deep space target location with Hubble Space Telescope (HST) and Hipparcos data

NASA Technical Reports Server (NTRS)

Null, George W.

1988-01-01

Interplanetary spacecraft navigation requires accurate a priori knowledge of target positions. A concept is presented for attaining improved target ephemeris accuracy using two future Earth-orbiting optical observatories, the European Space Agency (ESA) Hipparcos observatory and the Nasa Hubble Space Telescope (HST). Assuming nominal observatory performance, the Hipparcos data reduction will provide an accurate global star catalog, and HST will provide a capability for accurate angular measurements of stars and solar system bodies. The target location concept employs HST to observe solar system bodies relative to Hipparcos catalog stars and to determine the orientation (frame tie) of these stars to compact extragalactic radio sources. The target location process is described, the major error sources discussed, the potential target ephemeris error predicted, and mission applications identified. Preliminary results indicate that ephemeris accuracy comparable to the errors in individual Hipparcos catalog stars may be possible with a more extensive HST observing program. Possible future ground and spacebased replacements for Hipparcos and HST astrometric capabilities are also discussed.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

PubMed Central

2016-01-01

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement

PubMed Central

Xu, Dong; Zhang, Jian; Roy, Ambrish; Zhang, Yang

2011-01-01

I-TASSER is an automated pipeline for protein tertiary structure prediction using multiple threading alignments and iterative structure assembly simulations. In CASP9 experiments, two new algorithms, QUARK and FG-MD, were added to the I-TASSER pipeline for improving the structural modeling accuracy. QUARK is a de novo structure prediction algorithm used for structure modeling of proteins that lack detectable template structures. For distantly homologous targets, QUARK models are found useful as a reference structure for selecting good threading alignments and guiding the I-TASSER structure assembly simulations. FG-MD is an atomic-level structural refinement program that uses structural fragments collected from the PDB structures to guide molecular dynamics simulation and improve the local structure of predicted model, including hydrogen-bonding networks, torsion angles and steric clashes. Despite considerable progress in both the template-based and template-free structure modeling, significant improvements on protein target classification, domain parsing, model selection, and ab initio folding of beta-proteins are still needed to further improve the I-TASSER pipeline. PMID:22069036

Formidability and the logic of human anger

PubMed Central

Sell, Aaron; Tooby, John; Cosmides, Leda

2009-01-01

Eleven predictions derived from the recalibrational theory of anger were tested. This theory proposes that anger is produced by a neurocognitive program engineered by natural selection to use bargaining tactics to resolve conflicts of interest in favor of the angry individual. The program is designed to orchestrate two interpersonal negotiating tactics (conditionally inflicting costs or conditionally withholding benefits) to incentivize the target of the anger to place greater weight on the welfare of the angry individual. Individuals with enhanced abilities to inflict costs (e.g., stronger individuals) or to confer benefits (e.g., attractive individuals) have a better bargaining position in conflicts; hence, it was predicted that such individuals will be more prone to anger, prevail more in conflicts of interest, and consider themselves entitled to better treatment. These predictions were confirmed. Consistent with an evolutionary analysis, the effect of strength on anger was greater for men and the effect of attractiveness on anger was greater for women. Also as predicted, stronger men had a greater history of fighting than weaker men, and more strongly endorsed the efficacy of force to resolve conflicts—both in interpersonal and international conflicts. The fact that stronger men favored greater use of military force in international conflicts provides evidence that the internal logic of the anger program reflects the ancestral payoffs characteristic of a small-scale social world rather than rational assessments of modern payoffs in large populations. PMID:19666613

Formidability and the logic of human anger.

PubMed

Sell, Aaron; Tooby, John; Cosmides, Leda

2009-09-01

Eleven predictions derived from the recalibrational theory of anger were tested. This theory proposes that anger is produced by a neurocognitive program engineered by natural selection to use bargaining tactics to resolve conflicts of interest in favor of the angry individual. The program is designed to orchestrate two interpersonal negotiating tactics (conditionally inflicting costs or conditionally withholding benefits) to incentivize the target of the anger to place greater weight on the welfare of the angry individual. Individuals with enhanced abilities to inflict costs (e.g., stronger individuals) or to confer benefits (e.g., attractive individuals) have a better bargaining position in conflicts; hence, it was predicted that such individuals will be more prone to anger, prevail more in conflicts of interest, and consider themselves entitled to better treatment. These predictions were confirmed. Consistent with an evolutionary analysis, the effect of strength on anger was greater for men and the effect of attractiveness on anger was greater for women. Also as predicted, stronger men had a greater history of fighting than weaker men, and more strongly endorsed the efficacy of force to resolve conflicts--both in interpersonal and international conflicts. The fact that stronger men favored greater use of military force in international conflicts provides evidence that the internal logic of the anger program reflects the ancestral payoffs characteristic of a small-scale social world rather than rational assessments of modern payoffs in large populations.

Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty.

PubMed

Mattek, Ryan J; Harris, Sara E; Fox, Robert A

2016-01-01

Behavior problems are prevalent in young children and those living in poverty are at increased risk for stable, high-intensity behavioral problems. Research has demonstrated that participation in child and parent therapy (CPT) programs significantly reduces problematic child behaviors while increasing positive behaviors. However, CPT programs, particularly those implemented with low-income populations, frequently report high rates of attrition (over 50%). Parental attributional style has shown some promise as a contributing factor to treatment attendance and termination in previous research. The authors examined if parental attributional style could predict treatment success in a CPT program, specifically targeting low-income urban children with behavior problems. A hierarchical logistic regression was used with a sample of 425 families to assess if parent- and child-referent attributions variables predicted treatment success over and above demographic variables and symptom severity. Parent-referent attributions, child-referent attributions, and child symptom severity were found to be significant predictors of treatment success. Results indicated that caregivers who viewed themselves as a contributing factor for their child's behavior problems were significantly more likely to demonstrate treatment success. Alternatively, caregivers who viewed their child as more responsible for their own behavior problems were less likely to demonstrate treatment success. Additionally, more severe behavior problems were also predictive of treatment success. Clinical and research implications of these results are discussed.

Analyzing multiple data sets by interconnecting RSAT programs via SOAP Web services: an example with ChIP-chip data.

PubMed

Sand, Olivier; Thomas-Chollier, Morgane; Vervisch, Eric; van Helden, Jacques

2008-01-01

This protocol shows how to access the Regulatory Sequence Analysis Tools (RSAT) via a programmatic interface in order to automate the analysis of multiple data sets. We describe the steps for writing a Perl client that connects to the RSAT Web services and implements a workflow to discover putative cis-acting elements in promoters of gene clusters. In the presented example, we apply this workflow to lists of transcription factor target genes resulting from ChIP-chip experiments. For each factor, the protocol predicts the binding motifs by detecting significantly overrepresented hexanucleotides in the target promoters and generates a feature map that displays the positions of putative binding sites along the promoter sequences. This protocol is addressed to bioinformaticians and biologists with programming skills (notions of Perl). Running time is approximately 6 min on the example data set.

Society of Flight Test Engineers, Annual Symposium, 21st, Garden Grove, CA, Aug. 6-10, 1990, Proceedings

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

Not Available

1990-01-01

The present conference on flight testing encompasses avionics, flight-testing programs, technologies for flight-test predictions and measurements, testing tools, analysis methods, targeting techniques, and flightline testing. Specific issues addressed include flight testing of a digital terrain-following system, a digital Doppler rate-of-descent indicator, a high-technology testbed, a low-altitude air-refueling flight-test program, techniques for in-flight frequency-response testing for helicopters, limit-cycle oscillation and flight-flutter testing, and the research flight test of a scaled unmanned air vehicle. Also addressed are AV-8B V/STOL performance analysis, incorporating pilot-response time in failure-case testing, the development of pitot static flightline testing, targeting techniques for ground-based hover testing, a low-profilemore » microsensor for aerodynamic pressure measurement, and the use of a variable-capacitance accelerometer for flight-test measurements.« less

Transiting Exoplanet Studies and Community Targets for JWST's Early Release Science Program

NASA Astrophysics Data System (ADS)

Stevenson, Kevin B.; Lewis, Nikole K.; Bean, Jacob L.; Beichman, Charles; Fraine, Jonathan; Kilpatrick, Brian M.; Krick, J. E.; Lothringer, Joshua D.; Mandell, Avi M.; Valenti, Jeff A.; Agol, Eric; Angerhausen, Daniel; Barstow, Joanna K.; Birkmann, Stephan M.; Burrows, Adam; Charbonneau, David; Cowan, Nicolas B.; Crouzet, Nicolas; Cubillos, Patricio E.; Curry, S. M.; Dalba, Paul A.; de Wit, Julien; Deming, Drake; Désert, Jean-Michel; Doyon, René; Dragomir, Diana; Ehrenreich, David; Fortney, Jonathan J.; García Muñoz, Antonio; Gibson, Neale P.; Gizis, John E.; Greene, Thomas P.; Harrington, Joseph; Heng, Kevin; Kataria, Tiffany; Kempton, Eliza M.-R.; Knutson, Heather; Kreidberg, Laura; Lafrenière, David; Lagage, Pierre-Olivier; Line, Michael R.; Lopez-Morales, Mercedes; Madhusudhan, Nikku; Morley, Caroline V.; Rocchetto, Marco; Schlawin, Everett; Shkolnik, Evgenya L.; Shporer, Avi; Sing, David K.; Todorov, Kamen O.; Tucker, Gregory S.; Wakeford, Hannah R.

2016-09-01

The James Webb Space Telescope (JWST) will likely revolutionize transiting exoplanet atmospheric science, due to a combination of its capability for continuous, long duration observations and its larger collecting area, spectral coverage, and spectral resolution compared to existing space-based facilities. However, it is unclear precisely how well JWST will perform and which of its myriad instruments and observing modes will be best suited for transiting exoplanet studies. In this article, we describe a prefatory JWST Early Release Science (ERS) Cycle 1 program that focuses on testing specific observing modes to quickly give the community the data and experience it needs to plan more efficient and successful transiting exoplanet characterization programs in later cycles. We propose a multi-pronged approach wherein one aspect of the program focuses on observing transits of a single target with all of the recommended observing modes to identify and understand potential systematics, compare transmission spectra at overlapping and neighboring wavelength regions, confirm throughputs, and determine overall performances. In our search for transiting exoplanets that are well suited to achieving these goals, we identify 12 objects (dubbed “community targets”) that meet our defined criteria. Currently, the most favorable target is WASP-62b because of its large predicted signal size, relatively bright host star, and location in JWST's continuous viewing zone. Since most of the community targets do not have well-characterized atmospheres, we recommend initiating preparatory observing programs to determine the presence of obscuring clouds/hazes within their atmospheres. Measurable spectroscopic features are needed to establish the optimal resolution and wavelength regions for exoplanet characterization. Other initiatives from our proposed ERS program include testing the instrument brightness limits and performing phase-curve observations. The latter are a unique challenge compared to transit observations because of their significantly longer durations. Using only a single mode, we propose to observe a full-orbit phase curve of one of the previously characterized, short-orbital-period planets to evaluate the facility-level aspects of long, uninterrupted time-series observations.

Student Characteristics as Predictors of Teachers’ Implementation of a Kindergarten Readiness Program

PubMed Central

Voegler-Lee, Mary Ellen; Kupersmidt, Janis B.; Field, Samuel; Willoughby, Michael T.

2017-01-01

Recent years have seen increasing numbers of classroom-based interventions designed to enhance the school readiness of at-risk preschoolers. Even the most comprehensive, well-designed programs can suffer from limited effectiveness due to low-frequency implementation by teachers. The current study presents findings from the Building Bridges project (BB), an integrated program targeting school readiness in Head Start and low-income child care centers. Previous studies have reported the role of teacher-level and program-level characteristics in predicting teacher implementation of an intervention. The present study examines the role of student characteristics—language and math ability, social skills, and behavioral functioning—in predicting implementation exposure. These associations were examined in the context of program type (Head Start, child care) and intervention condition (consultation, no consultation). 88 classrooms (41 Head Start, 47 child care) participated in the BB intervention. Implementation exposure was predicted by several distinct student characteristics. Teachers whose students exhibited poorer language skills implemented significantly more BB activities, a finding that was consistent across program types and intervention conditions. A marginally significant trend was identified for oppositional behavior when interacted with intervention group in that teachers whose students demonstrated higher rates of oppositional behavior implemented fewer intervention activities when they did not have a consultant. Teachers in child care centers with a BB consultant had higher rates of implementation than did teachers in all other groups. These findings provide important information regarding the student-level characteristics that should be evaluated in order to optimize implementation of an intervention. PMID:22615022

Enhancing emotional-based target prediction

NASA Astrophysics Data System (ADS)

Gosnell, Michael; Woodley, Robert

2008-04-01

This work extends existing agent-based target movement prediction to include key ideas of behavioral inertia, steady states, and catastrophic change from existing psychological, sociological, and mathematical work. Existing target prediction work inherently assumes a single steady state for target behavior, and attempts to classify behavior based on a single emotional state set. The enhanced, emotional-based target prediction maintains up to three distinct steady states, or typical behaviors, based on a target's operating conditions and observed behaviors. Each steady state has an associated behavioral inertia, similar to the standard deviation of behaviors within that state. The enhanced prediction framework also allows steady state transitions through catastrophic change and individual steady states could be used in an offline analysis with additional modeling efforts to better predict anticipated target reactions.

MIRNA-DISTILLER: A Stand-Alone Application to Compile microRNA Data from Databases.

PubMed

Rieger, Jessica K; Bodan, Denis A; Zanger, Ulrich M

2011-01-01

MicroRNAs (miRNA) are small non-coding RNA molecules of ∼22 nucleotides which regulate large numbers of genes by binding to seed sequences at the 3'-untranslated region of target gene transcripts. The target mRNA is then usually degraded or translation is inhibited, although thus resulting in posttranscriptional down regulation of gene expression at the mRNA and/or protein level. Due to the bioinformatic difficulties in predicting functional miRNA binding sites, several publically available databases have been developed that predict miRNA binding sites based on different algorithms. The parallel use of different databases is currently indispensable, but highly uncomfortable and time consuming, especially when working with numerous genes of interest. We have therefore developed a new stand-alone program, termed MIRNA-DISTILLER, which allows to compile miRNA data for given target genes from public databases. Currently implemented are TargetScan, microCosm, and miRDB, which may be queried independently, pairwise, or together to calculate the respective intersections. Data are stored locally for application of further analysis tools including freely definable biological parameter filters, customized output-lists for both miRNAs and target genes, and various graphical facilities. The software, a data example file and a tutorial are freely available at http://www.ikp-stuttgart.de/content/language1/html/10415.asp.

MIRNA-DISTILLER: A Stand-Alone Application to Compile microRNA Data from Databases

PubMed Central

Rieger, Jessica K.; Bodan, Denis A.; Zanger, Ulrich M.

2011-01-01

MicroRNAs (miRNA) are small non-coding RNA molecules of ∼22 nucleotides which regulate large numbers of genes by binding to seed sequences at the 3′-untranslated region of target gene transcripts. The target mRNA is then usually degraded or translation is inhibited, although thus resulting in posttranscriptional down regulation of gene expression at the mRNA and/or protein level. Due to the bioinformatic difficulties in predicting functional miRNA binding sites, several publically available databases have been developed that predict miRNA binding sites based on different algorithms. The parallel use of different databases is currently indispensable, but highly uncomfortable and time consuming, especially when working with numerous genes of interest. We have therefore developed a new stand-alone program, termed MIRNA-DISTILLER, which allows to compile miRNA data for given target genes from public databases. Currently implemented are TargetScan, microCosm, and miRDB, which may be queried independently, pairwise, or together to calculate the respective intersections. Data are stored locally for application of further analysis tools including freely definable biological parameter filters, customized output-lists for both miRNAs and target genes, and various graphical facilities. The software, a data example file and a tutorial are freely available at http://www.ikp-stuttgart.de/content/language1/html/10415.asp PMID:22303335

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Bioinformatics in translational drug discovery.

PubMed

Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

2017-08-31

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

Casting a Wider Net: Data Driven Discovery of Proxies for Target Diagnoses

PubMed Central

Ramljak, Dusan; Davey, Adam; Uversky, Alexey; Roychoudhury, Shoumik; Obradovic, Zoran

2015-01-01

Background: The Hospital Readmissions Reduction Program (HRRP) introduced in October 2012 as part of the Affordable Care Act (ACA), ties hospital reimbursement rates to adjusted 30-day readmissions and mortality performance for a small set of target diagnoses. There is growing concern and emerging evidence that use of a small set of target diagnoses to establish reimbursement rates can lead to unstable results that are susceptible to manipulation (gaming) by hospitals. Methods: We propose a novel approach to identifying co-occurring diagnoses and procedures that can themselves serve as a proxy indicator of the target diagnosis. The proposed approach constructs a Markov Blanket that allows a high level of performance, in terms of predictive accuracy and scalability, along with interpretability of obtained results. In order to scale to a large number of co-occuring diagnoses (features) and hospital discharge records (samples), our approach begins with Google’s PageRank algorithm and exploits the stability of obtained results to rank the contribution of each diagnosis/procedure in terms of presence in a Markov Blanket for outcome prediction. Results: Presence of target diagnoses acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia (PN), and Sepsis in hospital discharge records for Medicare and Medicaid patients in California and New York state hospitals (2009–2011), were predicted using models trained on a subset of California state hospitals (2003–2008). Using repeated holdout evaluation, we used ~30,000,000 hospital discharge records and analyzed the stability of the proposed approach. Model performance was measured using the Area Under the ROC Curve (AUC) metric, and importance and contribution of single features to the final result. The results varied from AUC=0.68 (with SE<1e-4) for PN on cross validation datasets to AUC=0.94, with (SE<1e-7) for Sepsis on California hospitals (2009 – 2011), while the stability of features was consistently better with more training data for each target diagnosis. Prediction accuracy for considered target diagnoses approaches or exceeds accuracy estimates for discharge record data. Conclusions: This paper presents a novel approach to identifying a small subset of relevant diagnoses and procedures that approximate the Markov Blanket for target diagnoses. Accuracy and interpretability of results demonstrate the potential of our approach. PMID:26958243

Inter-kingdom prediction certainty evaluation of protein subcellular localization tools: microbial pathogenesis approach for deciphering host microbe interaction.

PubMed

Khan, Abdul Arif; Khan, Zakir; Kalam, Mohd Abul; Khan, Azmat Ali

2018-01-01

Microbial pathogenesis involves several aspects of host-pathogen interactions, including microbial proteins targeting host subcellular compartments and subsequent effects on host physiology. Such studies are supported by experimental data, but recent detection of bacterial proteins localization through computational eukaryotic subcellular protein targeting prediction tools has also come into practice. We evaluated inter-kingdom prediction certainty of these tools. The bacterial proteins experimentally known to target host subcellular compartments were predicted with eukaryotic subcellular targeting prediction tools, and prediction certainty was assessed. The results indicate that these tools alone are not sufficient for inter-kingdom protein targeting prediction. The correct prediction of pathogen's protein subcellular targeting depends on several factors, including presence of localization signal, transmembrane domain and molecular weight, etc., in addition to approach for subcellular targeting prediction. The detection of protein targeting in endomembrane system is comparatively difficult, as the proteins in this location are channelized to different compartments. In addition, the high specificity of training data set also creates low inter-kingdom prediction accuracy. Current data can help to suggest strategy for correct prediction of bacterial protein's subcellular localization in host cell. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Supporting Research and Development of Security Technologies through Network and Security Data Collection

DTIC Science & Technology

Research and development targeted at identifying and mitigating Internet security threats require current network data. To fulfill this need... researchers working for the Center for Applied Internet Data Analysis (CAIDA), a program at the San Diego Supercomputer Center (SDSC) which is based at the...vetted network and security researchers using the PREDICT/IMPACT portal and legal framework. We have also contributed to community building efforts that

PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

PubMed

Ng, Marcus C K; Fong, Simon; Siu, Shirley W I

2015-06-01

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

PubMed

Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie

2017-01-27

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

Identifying water price and population criteria for meeting future urban water demand targets

NASA Astrophysics Data System (ADS)

Ashoori, Negin; Dzombak, David A.; Small, Mitchell J.

2017-12-01

Predictive models for urban water demand can help identify the set of factors that must be satisfied in order to meet future targets for water demand. Some of the explanatory variables used in such models, such as service area population and changing temperature and rainfall rates, are outside the immediate control of water planners and managers. Others, such as water pricing and the intensity of voluntary water conservation efforts, are subject to decisions and programs implemented by the water utility. In order to understand this relationship, a multiple regression model fit to 44 years of monthly demand data (1970-2014) for Los Angeles, California was applied to predict possible future demand through 2050 under alternative scenarios for the explanatory variables: population, price, voluntary conservation efforts, and temperature and precipitation outcomes predicted by four global climate models with two CO2 emission scenarios. Future residential water demand in Los Angeles is projected to be largely driven by price and population rather than climate change and conservation. A median projection for the year 2050 indicates that residential water demand in Los Angeles will increase by approximately 36 percent, to a level of 620 million m3 per year. The Monte Carlo simulations of the fitted model for water demand were then used to find the set of conditions in the future for which water demand is predicted to be above or below the Los Angeles Department of Water and Power 2035 goal to reduce residential water demand by 25%. Results indicate that increases in price can not ensure that the 2035 water demand target can be met when population increases. Los Angeles must rely on furthering their conservation initiatives and increasing their use of stormwater capture, recycled water, and expanding their groundwater storage. The forecasting approach developed in this study can be utilized by other cities to understand the future of water demand in water-stressed areas. Improving water demand forecasts will help planners understand and optimize future investments in water supply infrastructure and related programs.

Literature-based condition-specific miRNA-mRNA target prediction.

PubMed

Oh, Minsik; Rhee, Sungmin; Moon, Ji Hwan; Chae, Heejoon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2017-01-01

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

A Predictive Model for Readmissions Among Medicare Patients in a California Hospital.

PubMed

Duncan, Ian; Huynh, Nhan

2017-11-17

Predictive models for hospital readmission rates are in high demand because of the Centers for Medicare & Medicaid Services (CMS) Hospital Readmission Reduction Program (HRRP). The LACE index is one of the most popular predictive tools among hospitals in the United States. The LACE index is a simple tool with 4 parameters: Length of stay, Acuity of admission, Comorbidity, and Emergency visits in the previous 6 months. The authors applied logistic regression to develop a predictive model for a medium-sized not-for-profit community hospital in California using patient-level data with more specific patient information (including 13 explanatory variables). Specifically, the logistic regression is applied to 2 populations: a general population including all patients and the specific group of patients targeted by the CMS penalty (characterized as ages 65 or older with select conditions). The 2 resulting logistic regression models have a higher sensitivity rate compared to the sensitivity of the LACE index. The C statistic values of the model applied to both populations demonstrate moderate levels of predictive power. The authors also build an economic model to demonstrate the potential financial impact of the use of the model for targeting high-risk patients in a sample hospital and demonstrate that, on balance, whether the hospital gains or loses from reducing readmissions depends on its margin and the extent of its readmission penalties.

Non-Maxwellian electron distributions by direct laser acceleration in near-critical plasmas

NASA Astrophysics Data System (ADS)

Toncian, T.; Wang, C.; Arefiev, A.; McCary, E.; Meadows, A.; Blakeney, J.; Chester, C.; Roycroft, R.; Fu, H.; Yan, X. Q.; Schreiber, J.; Pomerantz, I.; Quevedo, H.; Dyer, G.; Gaul, E.; Ditmire, T.; Hegelich, B. M.

2015-11-01

The irradiation of few nm thick targets by a finite-contrast high-intensity short-pulse laser results in a strong pre-expansion of these targets at the arrival time of the main pulse. The targets will decompress to near and lower than critical electron densities plasmas extending over lengths of few micrometers. The laser-matter interaction of the main pulse with such a highly localized but inhomogeneous the target leads to the generation of a channel and further self focussing of the laser beam. As measured in a experiment conducted with the GHOST laser system at UT Austin, 2D PIC simulations predict Direct Laser Acceleration of non-Maxwellian electron distribution in the laser propagation direction for such targets. The hereby high density electron bunches have potential applications as injector beams for a further wakefield acceleration stage. This work was supported by NNSA cooperative agreement DE-NA0002008, the DARPA's PULSE program (12-63-PULSE-FP014) and the AFOSR (FA9550-14-1-0045).

Common features of microRNA target prediction tools

PubMed Central

Peterson, Sarah M.; Thompson, Jeffrey A.; Ufkin, Melanie L.; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output. PMID:24600468

Common features of microRNA target prediction tools.

PubMed

Peterson, Sarah M; Thompson, Jeffrey A; Ufkin, Melanie L; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Motion prediction of a non-cooperative space target

NASA Astrophysics Data System (ADS)

Zhou, Bang-Zhao; Cai, Guo-Ping; Liu, Yun-Meng; Liu, Pan

2018-01-01

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler's equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function's gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target.

Comparative Analysis of Predicted Plastid-Targeted Proteomes of Sequenced Higher Plant Genomes

PubMed Central

Schaeffer, Scott; Harper, Artemus; Raja, Rajani; Jaiswal, Pankaj; Dhingra, Amit

2014-01-01

Plastids are actively involved in numerous plant processes critical to growth, development and adaptation. They play a primary role in photosynthesis, pigment and monoterpene synthesis, gravity sensing, starch and fatty acid synthesis, as well as oil, and protein storage. We applied two complementary methods to analyze the recently published apple genome (Malus × domestica) to identify putative plastid-targeted proteins, the first using TargetP and the second using a custom workflow utilizing a set of predictive programs. Apple shares roughly 40% of its 10,492 putative plastid-targeted proteins with that of the Arabidopsis (Arabidopsis thaliana) plastid-targeted proteome as identified by the Chloroplast 2010 project and ∼57% of its entire proteome with Arabidopsis. This suggests that the plastid-targeted proteomes between apple and Arabidopsis are different, and interestingly alludes to the presence of differential targeting of homologs between the two species. Co-expression analysis of 2,224 genes encoding putative plastid-targeted apple proteins suggests that they play a role in plant developmental and intermediary metabolism. Further, an inter-specific comparison of Arabidopsis, Prunus persica (Peach), Malus × domestica (Apple), Populus trichocarpa (Black cottonwood), Fragaria vesca (Woodland Strawberry), Solanum lycopersicum (Tomato) and Vitis vinifera (Grapevine) also identified a large number of novel species-specific plastid-targeted proteins. This analysis also revealed the presence of alternatively targeted homologs across species. Two separate analyses revealed that a small subset of proteins, one representing 289 protein clusters and the other 737 unique protein sequences, are conserved between seven plastid-targeted angiosperm proteomes. Majority of the novel proteins were annotated to play roles in stress response, transport, catabolic processes, and cellular component organization. Our results suggest that the current state of knowledge regarding plastid biology, preferentially based on model systems is deficient. New plant genomes are expected to enable the identification of potentially new plastid-targeted proteins that will aid in studying novel roles of plastids. PMID:25393533

The use of propensity scores to assess the generalizability of results from randomized trials

PubMed Central

Stuart, Elizabeth A.; Cole, Stephen R.; Bradshaw, Catherine P.; Leaf, Philip J.

2014-01-01

Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or “external validity,” of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify, or weight the control group outcomes to the population, assessing how well the propensity score-adjusted outcomes track the outcomes actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. This paper lays out these ideas, discusses the assumptions underlying the approach, and illustrates the metrics using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports. PMID:24926156

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Communication as a protective factor: evaluation of a life skills HIV/AIDS prevention program for Mexican elementary-school students.

PubMed

Pick, Susan; Givaudan, Martha; Sirkin, Jenna; Ortega, Isaac

2007-10-01

Literature suggests that communication is a protective factor against high-risk sexual behavior. This study assessed the impact of a fourth-grade communication-centered life skills program on attitudes, norms, self-efficacy, behaviors, and intentions toward communication about difficult subjects. Participants included 1,581 low-income Mexican elementary-school children, divided into experimental and control groups. Teachers were trained to replicate the program as part of the school curriculum over 15 to 20 weeks. Students completed self-report questionnaires before and after the program. Multilevel analyses demonstrated the program's statistically significant positive impact on communication about attitudes, self-efficacy, intentions, and behavior; perception of sociocultural norms regarding communication transformed as a result of the program. Gender significantly predicted differences in communication: with respect to attitudes, self-efficacy, and intentions. The results show that early intervention programs targeting communication about difficult subjects can prevent risky sexual behavior and its consequences (e.g., HIV/AIDS) and influence perception of norms and gender roles.

PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

PubMed

Shin, Woong-Hee; Bures, Mark Gregory; Kihara, Daisuke

2016-01-15

Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy.

PubMed

Ogorzalek, Tadeusz L; Hura, Greg L; Belsom, Adam; Burnett, Kathryn H; Kryshtafovych, Andriy; Tainer, John A; Rappsilber, Juri; Tsutakawa, Susan E; Fidelis, Krzysztof

2018-03-01

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. © 2018 Wiley Periodicals, Inc.

Identifying Drug-Target Interactions with Decision Templates.

PubMed

Yan, Xiao-Ying; Zhang, Shao-Wu

2018-01-01

During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

PubMed

Pembleton, Luke W; Inch, Courtney; Baillie, Rebecca C; Drayton, Michelle C; Thakur, Preeti; Ogaji, Yvonne O; Spangenberg, German C; Forster, John W; Daetwyler, Hans D; Cogan, Noel O I

2018-06-02

Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.

Specific attitudes which predict psychology students' intentions to seek help for psychological distress.

PubMed

Thomas, Susan J; Caputi, Peter; Wilson, Coralie J

2014-03-01

Although many postgraduate psychology programs address students' mental health, there are compelling indications that earlier, undergraduate, interventions may be optimal. We investigated specific attitudes that predict students' intentions to seek treatment for psychological distress to inform targeted interventions. Psychology students (N = 289; mean age = 19.75 years) were surveyed about attitudes and intentions to seek treatment for stress, anxiety, or depression. Less than one quarter of students reported that they would be likely to seek treatment should they develop psychological distress. Attitudes that predicted help-seeking intentions related to recognition of symptoms and the benefits of professional help, and openness to treatment for emotional problems. The current study identified specific attitudes which predict help-seeking intentions in psychology students. These attitudes could be strengthened in undergraduate educational interventions promoting well-being and appropriate treatment uptake among psychology students. © 2013 Wiley Periodicals, Inc.

Perceptions of, and assistance provided to, a hypothetical rape victim: differences between rape disclosure recipients and nonrecipients.

PubMed

Paul, Lisa A; Kehn, Andre; Gray, Matt J; Salapska-Gelleri, Joanna

2014-01-01

Undergraduate rape disclosure recipients' and nonrecipients' sociodemographic and life experience variables, attitudes towards rape, and responses to a hypothetical rape disclosure were compared to determine differences between them. One hundred ninety-two undergraduates at 3 universities participated in this online survey between November 2011 and April 2012. Participants reported on their rape myth acceptance (RMA) and personal direct and indirect (ie, disclosure receipt) experiences with sexual assault. Participants also responded to a hypothetical rape disclosure. Disclosure recipients were more likely to report a victimization history, and less confusion and perceived ineffectiveness in helping the hypothetical victim. RMA and nonrecipient status predicted perceived victim responsibility; these variables and childhood victimization predicted confusion about helping. RMA also predicted perceived ineffectiveness of one's helping behaviors. Victimization history and female gender predicted victim empathy. These findings can inform sexual assault-related programming for undergraduates through the provision of targeted assistance and corrective information.

TargetSpy: a supervised machine learning approach for microRNA target prediction.

PubMed

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

TargetSpy: a supervised machine learning approach for microRNA target prediction

PubMed Central

2010-01-01

Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org. PMID:20509939

MollDE: a homology modeling framework you can click with.

PubMed

Canutescu, Adrian A; Dunbrack, Roland L

2005-06-15

Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. http://dunbrack.fccc.edu/molide/molide.php rl_dunbrack@fccc.edu.

Comparative Protein Structure Modeling Using MODELLER.

PubMed

Webb, Benjamin; Sali, Andrej

2014-09-08

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Copyright © 2014 John Wiley & Sons, Inc.

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Validity of Teacher Ratings in Selecting Influential Aggressive Adolescents for a Targeted Preventive Intervention

PubMed Central

Henry, David B.; Miller-Johnson, Shari; Simon, Thomas R.; Schoeny, Michael E.

2009-01-01

This study describes a method for using teacher nominations and ratings to identify socially influential, aggressive middle school students for participation in a targeted violence prevention intervention. The teacher nomination method is compared with peer nominations of aggression and influence to obtain validity evidence. Participants were urban, predominantly African American and Latino sixth-grade students who were involved in a pilot study for a large multi-site violence prevention project. Convergent validity was suggested by the high correlation of teacher ratings of peer influence and peer nominations of social influence. The teacher ratings of influence demonstrated acceptable sensitivity and specificity when predicting peer nominations of influence among the most aggressive children. Results are discussed m terms of the application of teacher nominations and ratings in large trials and full implementation of targeted prevention programs. PMID:16378226

Stability analyses of the mass abrasive projectile high-speed penetrating into a concrete target Part III: Terminal ballistic trajectory analyses

NASA Astrophysics Data System (ADS)

Wu, H.; Chen, X. W.; Fang, Q.; Kong, X. Z.; He, L. L.

2015-08-01

During the high-speed penetration of projectiles into concrete targets (the impact velocity ranges from 1.0 to 1.5 km/s), important factors such as the incident oblique and attacking angles, as well as the asymmetric abrasions of the projectile nose induced by the target-projectile interactions, may lead to obvious deviation of the terminal ballistic trajectory and reduction of the penetration efficiency. Based on the engineering model for the mass loss and nose-blunting of ogive-nosed projectiles established, by using the Differential Area Force Law (DAFL) method and semi-empirical resistance function, a finite differential approach was programmed (PENTRA2D) for predicting the terminal ballistic trajectory of mass abrasive high-speed projectiles penetrating into concrete targets. It accounts for the free-surface effects on the drag force acting on the projectile, which are attributed to the oblique and attacking angles, as well as the asymmetric nose abrasion of the projectile. Its validation on the prediction of curvilinear trajectories of non-normal high-speed penetrators into concrete targets is verified by comparison with available test data. Relevant parametric influential analyses show that the most influential factor for the stability of terminal ballistic trajectories is the attacking angle, followed by the oblique angle, the discrepancy of asymmetric nose abrasion, and the location of mass center of projectile. The terminal ballistic trajectory deviations are aggravated as the above four parameters increase.

From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data.

PubMed

Bottini, Silvia; Hamouda-Tekaya, Nedra; Tanasa, Bogdan; Zaragosi, Laure-Emmanuelle; Grandjean, Valerie; Repetto, Emanuela; Trabucchi, Michele

2017-05-19

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data

PubMed Central

Bottini, Silvia; Hamouda-Tekaya, Nedra; Tanasa, Bogdan; Zaragosi, Laure-Emmanuelle; Grandjean, Valerie; Repetto, Emanuela

2017-01-01

Abstract Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification. PMID:28108660

Changes in predictive cuing modulate the hemispheric distribution of the P1 inhibitory response to attentional targets.

PubMed

Lasaponara, Stefano; D' Onofrio, Marianna; Dragone, Alessio; Pinto, Mario; Caratelli, Ludovica; Doricchi, Fabrizio

2017-05-01

Brain activity related to orienting of attention with spatial cues and brain responses to attentional targets are influenced the probabilistic contingency between cues and targets. Compared to predictive cues, cues predicting at chance the location of targets reduce the filtering out of uncued locations and the costs in reorienting attention to targets presented at these locations. Slagter et al. (2016) have recently suggested that the larger target related P1 component that is found in the hemisphere ipsilateral to validly cued targets reflects stimulus-driven inhibition in the processing of the unstimulated side of space contralateral to the same hemisphere. Here we verified whether the strength of this inhibition and the amplitude of the corresponding P1 wave are modulated by the probabilistic link between cues and targets. Healthy participants performed a task of endogenous orienting once with predictive and once with non-predictive directional cues. In the non-predictive condition we observed a drop in the amplitude of the P1 ipsilateral to the target and in the costs of reorienting. No change in the inter-hemispheric latencies of the P1 was found between the two predictive conditions. The N1 facilitatory component was unaffected by predictive cuing. These results show that the predictive context modulates the strength of the inhibitory P1 response and that this modulation is not matched with changes in the inter-hemispheric interaction between the P1 generators of the two hemispheres. Copyright © 2017. Published by Elsevier Ltd.

A gene expression biomarker accurately predicts estrogen ...

EPA Pesticide Factsheets

The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

Risk and protective factors as predictors of outcome in adolescents with psychiatric disorder and aggression.

PubMed

Vance, J Eric; Bowen, Natasha K; Fernandez, Gustavo; Thompson, Shealy

2002-01-01

To identify predictors of behavioral outcomes in high-risk adolescents with aggression and serious emotional disturbance (SED). Three hundred thirty-seven adolescents from a statewide North Carolina treatment program for aggressive youths with SED were followed between July 1995 and June 1999 from program entry (T1) to approximately 1 year later (T2). Historical and current psychosocial risk and protective factors as well as psychiatric symptom severity at T1 were tested as predictors of high and low behavioral functioning at T2. Behavioral functioning was a composite based on the frequency of risk-taking, self-injurious, threatening, and assaultive behavior. Eleven risk and protective factors were predictive of T2 behavioral functioning, while none of the measured T1 psychiatric symptoms was predictive. A history of aggression and negative parent-child relationships in childhood was predictive of worse T2 behavior, as was lower IQ. Better T2 behavioral outcomes were predicted by a history of consistent parental employment and positive parent-child relations, higher levels of current family support, contact with prosocial peers, higher reading level, good problem-solving abilities, and superior interpersonal skills. Among high-risk adolescents with aggression and SED, psychiatric symptom severity may be a less important predictor of behavioral outcomes than certain risk and protective factors. Several factors predictive of good behavioral functioning represent feasible intervention targets.

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.

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM.

PubMed

Tuncbag, Nurcan; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

2011-08-11

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

A hadoop-based method to predict potential effective drug combination.

PubMed

Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

A Hadoop-Based Method to Predict Potential Effective Drug Combination

PubMed Central

Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. PMID:25147789

Safety testing of monoclonal antibodies in non-human primates: Case studies highlighting their impact on human risk assessment.

PubMed

Brennan, Frank R; Cavagnaro, Joy; McKeever, Kathleen; Ryan, Patricia C; Schutten, Melissa M; Vahle, John; Weinbauer, Gerhard F; Marrer-Berger, Estelle; Black, Lauren E

2018-01-01

Monoclonal antibodies (mAbs) are improving the quality of life for patients suffering from serious diseases due to their high specificity for their target and low potential for off-target toxicity. The toxicity of mAbs is primarily driven by their pharmacological activity, and therefore safety testing of these drugs prior to clinical testing is performed in species in which the mAb binds and engages the target to a similar extent to that anticipated in humans. For highly human-specific mAbs, this testing often requires the use of non-human primates (NHPs) as relevant species. It has been argued that the value of these NHP studies is limited because most of the adverse events can be predicted from the knowledge of the target, data from transgenic rodents or target-deficient humans, and other sources. However, many of the mAbs currently in development target novel pathways and may comprise novel scaffolds with multi-functional domains; hence, the pharmacological effects and potential safety risks are less predictable. Here, we present a total of 18 case studies, including some of these novel mAbs, with the aim of interrogating the value of NHP safety studies in human risk assessment. These studies have identified mAb candidate molecules and pharmacological pathways with severe safety risks, leading to candidate or target program termination, as well as highlighting that some pathways with theoretical safety concerns are amenable to safe modulation by mAbs. NHP studies have also informed the rational design of safer drug candidates suitable for human testing and informed human clinical trial design (route, dose and regimen, patient inclusion and exclusion criteria and safety monitoring), further protecting the safety of clinical trial participants.

MicroRNAs as Potential Regulators of Glutathione Peroxidases Expression and Their Role in Obesity and Related Pathologies.

PubMed

Matoušková, Petra; Hanousková, Barbora; Skálová, Lenka

2018-04-14

Glutathione peroxidases (GPxs) belong to the eight-member family of phylogenetically related enzymes with different cellular localization, but distinct antioxidant function. Several GPxs are important selenoproteins. Dysregulated GPx expression is connected with severe pathologies, including obesity and diabetes. We performed a comprehensive bioinformatic analysis using the programs miRDB, miRanda, TargetScan, and Diana in the search for hypothetical microRNAs targeting 3'untranslated regions (3´UTR) of GPxs. We cross-referenced the literature for possible intersections between our results and available reports on identified microRNAs, with a special focus on the microRNAs related to oxidative stress, obesity, and related pathologies. We identified many microRNAs with an association with oxidative stress and obesity as putative regulators of GPxs. In particular, miR-185-5p was predicted by a larger number of programs to target six GPxs and thus could play the role as their master regulator. This microRNA was altered by selenium deficiency and can play a role as a feedback control of selenoproteins' expression. Through the bioinformatics analysis we revealed the potential connection of microRNAs, GPxs, obesity, and other redox imbalance related diseases.

Community food environments and healthy food access among older adults: A review of the evidence for the Senior Farmers' Market Nutrition Program (SFMNP).

PubMed

O'Dare Wilson, Kellie

2017-04-01

Although an array of federal, state, and local programs exist that target food insecurity and the specific nutritional needs of seniors, food insecurity among older adults in the United States remains a persistent problem, particularly in minority and rural populations. Food insecurity is highly predictive of inadequate fresh fruit and vegetable (FFV) consumption in particular. The Senior Farmers' Market Nutrition Program (SFMNP) is a community-based program to help seniors purchase FFVs at farmer's markets in their neighborhoods. The SFMNP continues to grow; however, little is known about the effectiveness of the program. The purposes of this article are to (1) highlight the importance of community and neighborhood based food insecurity programs, specifically emphasizing the importance of FFV access for seniors, (2) review the current state of the evidence on the SFMNP, and (3) provide recommendations for researchers and policy-makers wishing to continue to advance the knowledge base in neighborhood-based food security among older adults.

Aging and immortality: quasi-programmed senescence and its pharmacologic inhibition.

PubMed

Blagosklonny, Mikhail V

2006-09-01

While ruling out programmed aging, evolutionary theory predicts a quasi-program for aging, a continuation of the developmental program that is not turned off, is constantly on, becoming hyper-functional and damaging, causing diseases of aging. Could it be switched off pharmacologically? This would require identification of a molecular target involved in cell senescence, organism aging and diseases of aging. Notably, cell senescence is associated with activation of the TOR (target of rapamycin) nutrient- and mitogen-sensing pathway, which promotes cell growth, even though cell cycle is blocked. Is TOR involved in organism aging? In fact, in yeast (where the cell is the organism), caloric restriction, rapamycin and mutations that inhibit TOR all slow down aging. In animals from worms to mammals caloric restrictions, life-extending agents, and numerous mutations that increase longevity all converge on the TOR pathway. And, in humans, cell hypertrophy, hyper-function and hyperplasia, typically associated with activation of TOR, contribute to diseases of aging. Theoretical and clinical considerations suggest that rapamycin may be effective against atherosclerosis, hypertension and hyper-coagulation (thus, preventing myocardial infarction and stroke), osteoporosis, cancer, autoimmune diseases and arthritis, obesity, diabetes, macula-degeneration, Alzheimer's and Parkinson's diseases. Finally, I discuss that extended life span will reveal new causes for aging (e.g., ROS, 'wear and tear', Hayflick limit, stem cell exhaustion) that play a limited role now, when quasi-programmed senescence kills us first.

Hidden Markov models-based system (HMMSPECTR) for detecting structural homologies on the basis of sequential information.

PubMed

Tsigelny, Igor; Sharikov, Yuriy; Ten Eyck, Lynn F

2002-05-01

HMMSPECTR is a tool for finding putative structural homologs for proteins with known primary sequences. HMMSPECTR contains four major components: a data warehouse with the hidden Markov models (HMM) and alignment libraries; a search program which compares the initial protein sequences with the libraries of HMMs; a secondary structure prediction and comparison program; and a dominant protein selection program that prepares the set of 10-15 "best" proteins from the chosen HMMs. The data warehouse contains four libraries of HMMs. The first two libraries were constructed using different HHM preparation options of the HAMMER program. The third library contains parts ("partial HMM") of initial alignments. The fourth library contains trained HMMs. We tested our program against all of the protein targets proposed in the CASP4 competition. The data warehouse included libraries of structural alignments and HMMs constructed on the basis of proteins publicly available in the Protein Data Bank before the CASP4 meeting. The newest fully automated versions of HMMSPECTR 1.02 and 1.02ss produced better results than the best result reported at CASP4 either by r.m.s.d. or by length (or both) in 64% (HMMSPECTR 1.02) and 79% (HMMSPECTR 1.02ss) of the cases. The improvement is most notable for the targets with complexity 4 (difficult fold recognition cases).

Psychosocial Profile of Children Having Participated in an Intervention Program for Their Sexual Behavior Problems: The Predictor Role of Maltreatment.

PubMed

Tougas, Anne-Marie; Boisvert, Isabelle; Tourigny, Marc; Lemieux, Annie; Tremblay, Claudia; Gagnon, Mélanie M

2016-01-01

This study sought to verify if a history of maltreatment may predict the psychosocial profile of children who participated in an intervention program aiming at reducing sexual behavior problems. Data were collected at both the beginning and the end of the intervention program using a clinical protocol and standardized tests selected on the basis of the intervention targets. In general, the results indicate that children who had experienced maltreatment display a psychosocial profile that is similar to that of children who had not experienced maltreatment. However, children who had experienced psychological abuse or neglect may display greater externalized or sexualized behaviors, whereas children who have a parent who had been a victim of sexual abuse may display fewer sexualized behaviors.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

PubMed

Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

Stop bugging me: an examination of adolescents' protection behavior against online harassment.

PubMed

Lwin, May O; Li, Benjamin; Ang, Rebecca P

2012-02-01

Online harassment is a widespread phenomenon with consequential implications, especially for adolescents, who tend to engage in high-risk behavior online. Through the use of Protection Motivation Theory (PMT), we examine the predictors motivating the intention of youths to adopt protection behavior against online harassment. A survey was conducted with 537 youths from a stratified sample in Singapore. Regression analyses showed that perceived severity of online harassment, response efficacy and self efficacy of online protective behavior were significant predictors of behavioral intention with varying weights. The sole exception was perceived susceptibility to online harassment, which did not significantly predict behavioral intention. Gender and age were also found to moderate adolescents' uptake of protective behavior. The results suggest that public service programs targeted at educating youths should aim to increase coping appraisals and emphasize the severity of online harassment. Targeted educational programs could include those aimed at specific age or gender groups. Copyright © 2011 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

M Dwarf Flares: Exoplanet Detection Implications

NASA Astrophysics Data System (ADS)

Tofflemire, B. M.; Wisniewski, J. P.; Hilton, E. J.; Kowalski, A. F.; Kundurthy, P.; Schmidt, S. J.; Hawley, S. L.; Holtzman, J. A.

2011-12-01

Low mass stars such as M dwarfs have become prime targets for exoplanet transit searches as their low luminosities and small stellar radii could enable the detection of super-Earths residing in their habitable zones. While promising transit targets, M dwarfs are also inherently variable and can exhibit up to ˜6 magnitude flux enhancements in the optical U-band. This is significantly higher than the predicted transit depths of habitable zone super-Earths (0.005 magnitude flux decrease). The behavior of flares at infrared (IR) wavelengths, particularly those likely to be used to study and characterize M dwarf exoplanets using facilities such as the James Web Space Telescope (JWST), remains largely unknown. To address these uncertainties, we are executing a coordinated, contemporaneous monitoring program of the optical and IR flux of M dwarfs known to regularly flare. A suite of telescopes located at the Kitt Peak National Observatory and the Apache Point Observatory are used for the observations. We present the initial results of this program.

Combinatorial therapy discovery using mixed integer linear programming.

PubMed

Pang, Kaifang; Wan, Ying-Wooi; Choi, William T; Donehower, Lawrence A; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong

2014-05-15

Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. zhandong.liu@bcm.edu Supplementary data are available at Bioinformatics online.

Conflict Adaptation and Cue Competition during Learning in an Eriksen Flanker Task

PubMed Central

Ghinescu, Rodica; Ramsey, Ashley K.; Gratton, Gabriele; Fabiani, Monica

2016-01-01

Two experiments investigated competition between cues that predicted the correct target response to a target stimulus in a response conflict procedure using a flanker task. Subjects received trials with five-character arrays with a central target character and distractor flanker characters that matched (compatible) or did not match (incompatible) the central target. Subjects’ expectancies for compatible and incompatible trials were manipulated by presenting pre-trial cues that signaled the occurrence of compatible or incompatible trials. On some trials, a single cue predicted the target stimulus and the required target response. On other trials, a second redundant, predictive cue was also present on such trials. The results showed an effect of competition between cues for control over strategic responding to the target stimuli, a finding that is predicted by associative learning theories. The finding of competition between pre-trial cues that predict incompatible trials, but not cues that predict compatible trials, suggests that different strategic processes may occur during adaptation to conflict when different kinds of trials are expected. PMID:27941977

Global elimination of leprosy by 2020: are we on track?

PubMed

Blok, David J; De Vlas, Sake J; Richardus, Jan Hendrik

2015-10-22

Every year more than 200,000 new leprosy cases are registered globally. This number has been fairly stable over the past 8 years. WHO has set a target to interrupt the transmission of leprosy globally by 2020. The aim of this study is to investigate whether this target, interpreted as global elimination, is feasible given the current control strategy. We focus on the three most important endemic countries, India, Brazil and Indonesia, which together account for more than 80 % of all newly registered leprosy cases. We used the existing individual-based model SIMCOLEP to predict future trends of leprosy incidence given the current control strategy in each country. SIMCOLEP simulates the spread of M. leprae in a population that is structured in households. Current control consists of passive and active case detection, and multidrug therapy (MDT). Predictions of leprosy incidence were made for each country as well as for one high-endemic region within each country: Chhattisgarh (India), Pará State (Brazil) and Madura (Indonesia). Data for model quantification came from: National Leprosy Elimination Program (India), SINAN database (Brazil), and Netherlands Leprosy Relief (Indonesia). Our projections of future leprosy incidence all show a downward trend. In 2020, the country-level leprosy incidence has decreased to 6.2, 6.1 and 3.3 per 100,000 in India, Brazil and Indonesia, respectively, meeting the elimination target of less than 10 per 100,000. However, elimination may not be achieved in time for the high-endemic regions. The leprosy incidence in 2020 is predicted to be 16.2, 21.1 and 19.3 per 100,000 in Chhattisgarh, Pará and Madura, respectively, and the target may only be achieved in another 5 to 10 years. Our predictions show that although country-level elimination is reached by 2020, leprosy is likely to remain a problem in the high-endemic regions (i.e. states, districts and provinces with multimillion populations), which account for most of the cases in a country.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

PubMed

Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Prediction of miRNA targets.

PubMed

Oulas, Anastasis; Karathanasis, Nestoras; Louloupi, Annita; Pavlopoulos, Georgios A; Poirazi, Panayiota; Kalantidis, Kriton; Iliopoulos, Ioannis

2015-01-01

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Predictable Programming on a Precision Timed Architecture

DTIC Science & Technology

2008-04-18

Application: A Video Game Figure 6: Structure of the Video Game Example Inspired by an example game sup- plied with the Hydra development board [17...we implemented a sim- ple video game in C targeted to our PRET architecture. Our example centers on rendering graphics and is otherwise fairly simple...background image. 13 Figure 10: A Screen Dump From Our Video Game Ultimately, each displayed pixel is one of only four col- ors, but the pixels in

Predicting water quality by relating secchi-disk transparency and chlorophyll a measurements to satellite imagery for Michigan Inland Lakes, August 2002

USGS Publications Warehouse

Fuller, L.M.; Aichele, Stephen S.; Minnerick, R.J.

2004-01-01

Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Environmental Quality have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Through this program, approximately 730 of Michigan's 11,000 inland lakes will be monitored once during this 15-year study. Targeted lakes will be sampled during spring turnover and again in late summer to characterize water quality. Because more extensive and more frequent sampling is not economically feasible in the Lake Water Quality Assessment program, the U.S. Geological Survey and Michigan Department of Environmental Quality investigate the use of satellite imagery as a means of estimating water quality in unsampled lakes. Satellite imagery has been successfully used in Minnesota, Wisconsin, and elsewhere to compute the trophic state of inland lakes from predicted secchi-disk measurements. Previous attempts of this kind in Michigan resulted in a poorer fit between observed and predicted data than was found for Minnesota or Wisconsin. This study tested whether estimates could be improved by using atmospherically corrected satellite imagery, whether a more appropriate regression model could be obtained for Michigan, and whether chlorophyll a concentrations could be reliably predicted from satellite imagery in order to compute trophic state of inland lakes. Although the atmospheric-correction did not significantly improve estimates of lake-water quality, a new regression equation was identified that consistently yielded better results than an equation obtained from the literature. A stepwise regression was used to determine an equation that accurately predicts chlorophyll a concentrations in northern Lower Michigan.

Using Targeting Outcomes of Programs as a Framework to Target Photographic Events in Nonformal Educational Programs

ERIC Educational Resources Information Center

Rockwell, S. Kay; Albrecht, Julie A.; Nugent, Gwen C.; Kunz, Gina M.

2012-01-01

Targeting Outcomes of Programs (TOP) is a seven-step hierarchical programming model in which the program development and performance sides are mirror images of each other. It served as a framework to identify a simple method for targeting photographic events in nonformal education programs, indicating why, when, and how photographs would be useful…

In silico prediction of novel therapeutic targets using gene-disease association data.

PubMed

Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

2017-08-29

Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention

PubMed Central

Parizo, Justin; Sturrock, Hugh J. W.; Dhiman, Ramesh C.; Greenhouse, Bryan

2016-01-01

The world population, especially in developing countries, has experienced a rapid progression of urbanization over the last half century. Urbanization has been accompanied by a rise in cases of urban infectious diseases, such as malaria. The complexity and heterogeneity of the urban environment has made study of specific urban centers vital for urban malaria control programs, whereas more generalizable risk factor identification also remains essential. Ahmedabad city, India, is a large urban center located in the state of Gujarat, which has experienced a significant Plasmodium vivax and Plasmodium falciparum disease burden. Therefore, a targeted analysis of malaria in Ahmedabad city was undertaken to identify spatiotemporal patterns of malaria, risk factors, and methods of predicting future malaria cases. Malaria incidence in Ahmedabad city was found to be spatially heterogeneous, but temporally stable, with high spatial correlation between species. Because of this stability, a prediction method utilizing historic cases from prior years and seasons was used successfully to predict which areas of Ahmedabad city would experience the highest malaria burden and could be used to prospectively target interventions. Finally, spatial analysis showed that normalized difference vegetation index, proximity to water sources, and location within Ahmedabad city relative to the dense urban core were the best predictors of malaria incidence. Because of the heterogeneity of urban environments and urban malaria itself, the study of specific large urban centers is vital to assist in allocating resources and informing future urban planning. PMID:27382081

TK Modeler version 1.0, a Microsoft® Excel®-based modeling software for the prediction of diurnal blood/plasma concentration for toxicokinetic use.

PubMed

McCoy, Alene T; Bartels, Michael J; Rick, David L; Saghir, Shakil A

2012-07-01

TK Modeler 1.0 is a Microsoft® Excel®-based pharmacokinetic (PK) modeling program created to aid in the design of toxicokinetic (TK) studies. TK Modeler 1.0 predicts the diurnal blood/plasma concentrations of a test material after single, multiple bolus or dietary dosing using known PK information. Fluctuations in blood/plasma concentrations based on test material kinetics are calculated using one- or two-compartment PK model equations and the principle of superposition. This information can be utilized for the determination of appropriate dosing regimens based on reaching a specific desired C(max), maintaining steady-state blood/plasma concentrations, or other exposure target. This program can also aid in the selection of sampling times for accurate calculation of AUC(24h) (diurnal area under the blood concentration time curve) using sparse-sampling methodologies (one, two or three samples). This paper describes the construction, use and validation of TK Modeler. TK Modeler accurately predicted blood/plasma concentrations of test materials and provided optimal sampling times for the calculation of AUC(24h) with improved accuracy using sparse-sampling methods. TK Modeler is therefore a validated, unique and simple modeling program that can aid in the design of toxicokinetic studies. Copyright © 2012 Elsevier Inc. All rights reserved.

The influence of maternal health literacy and child's age on participation in social welfare programs.

PubMed

Pati, Susmita; Siewert, Elizabeth; Wong, Angie T; Bhatt, Suraj K; Calixte, Rose E; Cnaan, Avital

2014-07-01

The objective of this study is to determine the influence of maternal health literacy and child's age on participation in social welfare programs benefiting children. In a longitudinal prospective cohort study of 560 Medicaid-eligible mother-infant dyads recruited in Philadelphia, maternal health literacy was assessed using the test of functional health literacy in adults (short version). Participation in social welfare programs [Temporary Assistance to Needy Families (TANF), Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), child care subsidy, and public housing] was self-reported at child's birth, and at the 6, 12, 18, 24 month follow-up interviews. Generalized estimating equations quantified the strength of maternal health literacy as an estimator of program participation. The mothers were primarily African-Americans (83%), single (87%), with multiple children (62%). Nearly 24% of the mothers had inadequate or marginal health literacy. Children whose mothers had inadequate health literacy were less likely to receive child care subsidy (adjusted OR = 0.54, 95% CI 0.34-0.85) than children whose mothers had adequate health literacy. Health literacy was not a significant predictor for TANF, SNAP, WIC or housing assistance. The predicted probability for participation in all programs decreased from birth to 24 months. Most notably, predicted WIC participation declined rapidly after age one. During the first 24 months, mothers with inadequate health literacy could benefit from simplified or facilitated child care subsidy application processes. Targeted outreach and enrollment efforts conducted by social welfare programs need to take into account the changing needs of families as children age.

In-silico and in-vivo analyses of EST databases unveil conserved miRNAs from Carthamus tinctorius and Cynara cardunculus

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are small RNAs (21-24 bp) providing an RNA-based system of gene regulation highly conserved in plants and animals. In plants, miRNAs control mRNA degradation or restrain translation, affecting development and responses to stresses. Plant miRNAs show imperfect but extensive complementarity to mRNA targets, making their computational prediction possible, useful when data mining is applied on different species. In this study we used a comparative approach to identify both miRNAs and their targets, in artichoke and safflower. Results Two complete expressed sequence tags (ESTs) datasets from artichoke (3.6·104 entries) and safflower (4.2·104), were analysed with a bioinformatic pipeline and in vitro experiments, identifying 17 potential miRNAs. For each EST, using RNAhybrid program and 953 non redundant miRNA mature sequences, available in mirBase as reference, we searched matching putative targets. 8730 out of 42011 ESTs from safflower and 7145 of 36323 ESTs from artichoke showed at least one predicted miRNA target. BLAST analysis showed that 75% of all ESTs shared at least a common homologous region (E-value < 10-4) and about 50% of these displayed 400 bp or longer aligned sequences as conserved homologous/orthologous (COS) regions. 960 and 890 ESTs of safflower and artichoke organized in COS shared 79 different miRNA targets, considered functionally conserved, and statistically significant when compared with random sequences (signal to noise ratio > 2 and specificity ≥ 0.85). Four highly significant miRNAs selected from in silico data were experimentally validated in globe artichoke leaves. Conclusions Mature miRNAs and targets were predicted within EST sequences of safflower and artichoke. Most of the miRNA targets appeared highly/moderately conserved, highlighting an important and conserved function. In this study we introduce a stringent parameter for the comparative sequence analysis, represented by the identification of the same target in the COS region. After statistical analysis 79 targets, found on the COS regions and belonging to 60 miRNA families, have a signal to noise ratio > 2, with ≥ 0.85 specificity. The putative miRNAs identified belong to 55 dicotyledon plants and to 24 families only in monocotyledon. PMID:22536958

Timing of target discrimination in human frontal eye fields.

PubMed

O'Shea, Jacinta; Muggleton, Neil G; Cowey, Alan; Walsh, Vincent

2004-01-01

Frontal eye field (FEF) neurons discharge in response to behaviorally relevant stimuli that are potential targets for saccades. Distinct visual and motor processes have been dissociated in the FEF of macaque monkeys, but little is known about the visual processing capacity of FEF in humans. We used double-pulse transcranial magnetic stimulation [(d)TMS] to investigate the timing of target discrimination during visual conjunction search. We applied dual TMS pulses separated by 40 msec over the right FEF and vertex. These were applied in five timing conditions to sample separate time windows within the first 200 msec of visual processing. (d)TMS impaired search performance, reflected in reduced d' scores. This effect was limited to a time window between 40 and 80 msec after search array onset. These parameters correspond with single-cell activity in FEF that predicts monkeys' behavioral reports on hit, miss, false alarm, and correct rejection trials. Our findings demonstrate a crucial early role for human FEF in visual target discrimination that is independent of saccade programming.

Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma.

PubMed

Creighton, Chad J; Hernandez-Herrera, Anadulce; Jacobsen, Anders; Levine, Douglas A; Mankoo, Parminder; Schultz, Nikolaus; Du, Ying; Zhang, Yiqun; Larsson, Erik; Sheridan, Robert; Xiao, Weimin; Spellman, Paul T; Getz, Gad; Wheeler, David A; Perou, Charles M; Gibbs, Richard A; Sander, Chris; Hayes, D Neil; Gunaratne, Preethi H

2012-01-01

The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets. Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3'-untranslated region (with less frequency observed for coding sequence and 5'-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability. This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers.

Identification of MicroRNA Targets of Capsicum spp. Using MiRTrans—a Trans-Omics Approach

PubMed Central

Zhang, Lu; Qin, Cheng; Mei, Junpu; Chen, Xiaocui; Wu, Zhiming; Luo, Xirong; Cheng, Jiaowen; Tang, Xiangqun; Hu, Kailin; Li, Shuai C.

2017-01-01

The microRNA (miRNA) can regulate the transcripts that are involved in eukaryotic cell proliferation, differentiation, and metabolism. Especially for plants, our understanding of miRNA targets, is still limited. Early attempts of prediction on sequence alignments have been plagued by enormous false positives. It is helpful to improve target prediction specificity by incorporating the other data sources such as the dependency between miRNA and transcript expression or even cleaved transcripts by miRNA regulations, which are referred to as trans-omics data. In this paper, we developed MiRTrans (Prediction of MiRNA targets by Trans-omics data) to explore miRNA targets by incorporating miRNA sequencing, transcriptome sequencing, and degradome sequencing. MiRTrans consisted of three major steps. First, the target transcripts of miRNAs were predicted by scrutinizing their sequence characteristics and collected as an initial potential targets pool. Second, false positive targets were eliminated if the expression of miRNA and its targets were weakly correlated by lasso regression. Third, degradome sequencing was utilized to capture the miRNA targets by examining the cleaved transcripts that regulated by miRNAs. Finally, the predicted targets from the second and third step were combined by Fisher's combination test. MiRTrans was applied to identify the miRNA targets for Capsicum spp. (i.e., pepper). It can generate more functional miRNA targets than sequence-based predictions by evaluating functional enrichment. MiRTrans identified 58 miRNA-transcript pairs with high confidence from 18 miRNA families conserved in eudicots. Most of these targets were transcription factors; this lent support to the role of miRNA as key regulator in pepper. To our best knowledge, this work is the first attempt to investigate the miRNA targets of pepper, as well as their regulatory networks. Surprisingly, only a small proportion of miRNA-transcript pairs were shared between degradome sequencing and expression dependency predictions, suggesting that miRNA targets predicted by a single technology alone may be prone to report false negatives. PMID:28443105

How reliable are ligand-centric methods for Target Fishing?

NASA Astrophysics Data System (ADS)

Peon, Antonio; Dang, Cuong; Ballester, Pedro

2016-04-01

Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict.

Cultural and Linguistic Adaptation of a Healthy Diet Text Message Intervention for Hispanic Adults

PubMed Central

Cameron, Linda D.; Durazo, Arturo; Ramirez, A. Susana; Corona, Roberto; Ultreras, Mayra; Piva, Sonia

2017-01-01

Hispanics represent a critical target for culturally-adapted diet interventions. In this formative research, we translated HealthyYouTXT, an mHealth program developed by the U.S. National Cancer Institute, into HealthyYouTXT en Español, a linguistically and culturally appropriate version for Spanish speakers. We report a three-stage, mixed-methods process through which we culturally adapted the text messages, evaluated their acceptability, and revised the program based on the findings. In Stage 1, we conducted initial translations and adaptations of the text libraries using an iterative, principle-guided process. In Stage 2, we used mixed methods including focus groups and surveys with 109 Hispanic adults to evaluate the acceptability and cultural appropriateness of the program. Further, we used survey data to evaluate whether Self-Determination Theory factors (used to develop HealthyYouTXT) of autonomous motivation, controlled motivation, and amotivation and Hispanic cultural beliefs about familism, fatalism, and destiny predict program interest and its perceived efficacy. Mixed-methods analyses revealed substantial interest in HealthyYouTXT, with most participants expressing substantial interest in using it and viewing it as highly efficacious. Both cultural beliefs (i.e., beliefs in destiny and, for men, high familism) and SDT motivations (i.e., autonomy) predicted HealthyYouTXT evaluations, suggesting utility in emphasizing them in messages. Higher destiny beliefs predicted lower interest and perceived efficacy, suggesting they could impede program use. In Stage 3, we implemented the mixed-methods findings to generate a revised HealthyYouTXT en Español. The emergent linguistic principles and multi-stage, multi-methods process can be applied beneficially in health communication adaptations. PMID:28248628

Cultural and Linguistic Adaptation of a Healthy Diet Text Message Intervention for Hispanic Adults Living in the United States.

PubMed

Cameron, Linda D; Durazo, Arturo; Ramírez, A Susana; Corona, Roberto; Ultreras, Mayra; Piva, Sonia

2017-03-01

Hispanics represent a critical target for culturally adapted diet interventions. In this formative research, we translated HealthyYouTXT, an mHealth program developed by the U.S. National Cancer Institute, into HealthyYouTXT en Español, a linguistically and culturally appropriate version for Spanish speakers in the United States. We report a three-stage, mixed-methods process through which we culturally adapted the text messages, evaluated their acceptability, and revised the program based on the findings. In Stage 1, we conducted initial translations and adaptations of the text libraries using an iterative, principle-guided process. In Stage 2, we used mixed methods including focus groups and surveys with 109 Hispanic adults to evaluate the acceptability and cultural appropriateness of the program. We used survey data to evaluate whether self-determination theory (SDT) factors (used to develop HealthyYouTXT) of autonomous motivation, controlled motivation, and amotivation and Hispanic cultural beliefs about familism, fatalism, and destiny predict program interest and its perceived efficacy. Mixed-methods analyses revealed substantial interest in HealthyYouTXT, with most participants desiring to use it and viewing it as highly efficacious. Both cultural beliefs (i.e., beliefs in destiny and, for men, high familism) and SDT motivations (i.e., autonomy) predicted HealthyYouTXT evaluations, suggesting utility in emphasizing them in messages. Higher destiny beliefs predicted lower interest, suggesting that they could impede program use. In Stage 3, we implemented the mixed-methods findings to finalize HealthyYouTXT en Español. The emergent linguistic principles and multistage, multimethods process can be applied in health communication adaptations.

Intersection of toxicogenomics and high throughput screening in the Tox21 program: an NIEHS perspective.

PubMed

Merrick, B Alex; Paules, Richard S; Tice, Raymond R

Humans are exposed to thousands of chemicals with inadequate toxicological data. Advances in computational toxicology, robotic high throughput screening (HTS), and genome-wide expression have been integrated into the Tox21 program to better predict the toxicological effects of chemicals. Tox21 is a collaboration among US government agencies initiated in 2008 that aims to shift chemical hazard assessment from traditional animal toxicology to target-specific, mechanism-based, biological observations using in vitro assays and lower organism models. HTS uses biocomputational methods for probing thousands of chemicals in in vitro assays for gene-pathway response patterns predictive of adverse human health outcomes. In 1999, NIEHS began exploring the application of toxicogenomics to toxicology and recent advances in NextGen sequencing should greatly enhance the biological content obtained from HTS platforms. We foresee an intersection of new technologies in toxicogenomics and HTS as an innovative development in Tox21. Tox21 goals, priorities, progress, and challenges will be reviewed.

Demographic and Predeparture Factors Associated With Drinking and Alcohol-Related Consequences for College Students Completing Study Abroad Experiences

PubMed Central

Pedersen, Eric R.; Skidmore, Jessica R.; Aresi, Giovanni

2014-01-01

Objective: Study abroad students are at-risk for increased and problematic drinking behavior. As few efforts have been made to examine this at-risk population, we predicted drinking and alcohol-related consequences abroad from predeparture and site-specific factors. Participants: The sample consisted of 339 students completing study abroad programs. Method: Participants filled out online measures at predeparture, abroad, and at post-return. Results: We found drinking and consequences abroad were predicted by a number of factors including demographics (e.g., younger age, male sex, Greek affiliation, White ethnicity), student factors (e.g. low GPA, major area of study), study abroad site factors (e.g., apartment living abroad, study in Europe), predeparture levels of drinking and consequences, sensation seeking, and goals related to social gathering. Conclusions: Findings can be used to inform campus policies for admission to study abroad programs as well as assist in the development of interventions targeted toward preventing risk for students during abroad experiences. PMID:24499190

Demographic and predeparture factors associated with drinking and alcohol-related consequences for college students completing study abroad experiences.

PubMed

Pedersen, Eric R; Skidmore, Jessica R; Aresi, Giovanni

2014-01-01

Study abroad students are at risk for increased and problematic drinking behavior. As few efforts have been made to examine this at-risk population, the authors predicted drinking and alcohol-related consequences abroad from predeparture and site-specific factors. The sample consisted of 339 students completing study abroad programs. Participants filled out online measures at predeparture, abroad, and at postreturn. The authors found that drinking and consequences abroad were predicted by a number of factors, including demographics (eg, younger age, male sex, Greek affiliation, white ethnicity), student factors (eg, low GPA, major area of study), study abroad site factors (eg, apartment living abroad, study in Europe), predeparture levels of drinking and consequences, sensation seeking, and goals related to social gathering. Findings can be used to inform campus policies for admission to study abroad programs as well as assist in the development of interventions targeted toward preventing risk for students during abroad experiences.

Combat Wound Initiative program.

PubMed

Stojadinovic, Alexander; Elster, Eric; Potter, Benjamin K; Davis, Thomas A; Tadaki, Doug K; Brown, Trevor S; Ahlers, Stephen; Attinger, Christopher E; Andersen, Romney C; Burris, David; Centeno, Jose; Champion, Hunter; Crumbley, David R; Denobile, John; Duga, Michael; Dunne, James R; Eberhardt, John; Ennis, William J; Forsberg, Jonathan A; Hawksworth, Jason; Helling, Thomas S; Lazarus, Gerald S; Milner, Stephen M; Mullick, Florabel G; Owner, Christopher R; Pasquina, Paul F; Patel, Chirag R; Peoples, George E; Nissan, Aviram; Ring, Michael; Sandberg, Glenn D; Schaden, Wolfgang; Schultz, Gregory S; Scofield, Tom; Shawen, Scott B; Sheppard, Forest R; Stannard, James P; Weina, Peter J; Zenilman, Jonathan M

2010-07-01

The Combat Wound Initiative (CWI) program is a collaborative, multidisciplinary, and interservice public-private partnership that provides personalized, state-of-the-art, and complex wound care via targeted clinical and translational research. The CWI uses a bench-to-bedside approach to translational research, including the rapid development of a human extracorporeal shock wave therapy (ESWT) study in complex wounds after establishing the potential efficacy, biologic mechanisms, and safety of this treatment modality in a murine model. Additional clinical trials include the prospective use of clinical data, serum and wound biomarkers, and wound gene expression profiles to predict wound healing/failure and additional clinical patient outcomes following combat-related trauma. These clinical research data are analyzed using machine-based learning algorithms to develop predictive treatment models to guide clinical decision-making. Future CWI directions include additional clinical trials and study centers and the refinement and deployment of our genetically driven, personalized medicine initiative to provide patient-specific care across multiple medical disciplines, with an emphasis on combat casualty care.

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

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

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

Building uncertainty into cost-effectiveness rankings: portfolio risk-return tradeoffs and implications for decision rules.

PubMed

O'Brien, B J; Sculpher, M J

2000-05-01

Current principles of cost-effectiveness analysis emphasize the rank ordering of programs by expected economic return (eg, quality-adjusted life-years gained per dollar expended). This criterion ignores the variance associated with the cost-effectiveness of a program, yet variance is a common measure of risk when financial investment options are appraised. Variation in health care program return is likely to be a criterion of program selection for health care managers with fixed budgets and outcome performance targets. Characterizing health care resource allocation as a risky investment problem, we show how concepts of portfolio analysis from financial economics can be adopted as a conceptual framework for presenting cost-effectiveness data from multiple programs as mean-variance data. Two specific propositions emerge: (1) the current convention of ranking programs by expected return is a special case of the portfolio selection problem in which the decision maker is assumed to be indifferent to risk, and (2) for risk-averse decision makers, the degree of joint risk or covariation in cost-effectiveness between programs will create incentives to diversify an investment portfolio. The conventional normative assumption of risk neutrality for social-level public investment decisions does not apply to a large number of health care resource allocation decisions in which health care managers seek to maximize returns subject to budget constraints and performance targets. Portfolio theory offers a useful framework for studying mean-variance tradeoffs in cost-effectiveness and offers some positive predictions (and explanations) of actual decision making in the health care sector.

Optimal de novo design of MRM experiments for rapid assay development in targeted proteomics.

PubMed

Bertsch, Andreas; Jung, Stephan; Zerck, Alexandra; Pfeifer, Nico; Nahnsen, Sven; Henneges, Carsten; Nordheim, Alfred; Kohlbacher, Oliver

2010-05-07

Targeted proteomic approaches such as multiple reaction monitoring (MRM) overcome problems associated with classical shotgun mass spectrometry experiments. Developing MRM quantitation assays can be time consuming, because relevant peptide representatives of the proteins must be found and their retention time and the product ions must be determined. Given the transitions, hundreds to thousands of them can be scheduled into one experiment run. However, it is difficult to select which of the transitions should be included into a measurement. We present a novel algorithm that allows the construction of MRM assays from the sequence of the targeted proteins alone. This enables the rapid development of targeted MRM experiments without large libraries of transitions or peptide spectra. The approach relies on combinatorial optimization in combination with machine learning techniques to predict proteotypicity, retention time, and fragmentation of peptides. The resulting potential transitions are scheduled optimally by solving an integer linear program. We demonstrate that fully automated construction of MRM experiments from protein sequences alone is possible and over 80% coverage of the targeted proteins can be achieved without further optimization of the assay.

MuPeXI: prediction of neo-epitopes from tumor sequencing data.

PubMed

Bjerregaard, Anne-Mette; Nielsen, Morten; Hadrup, Sine Reker; Szallasi, Zoltan; Eklund, Aron Charles

2017-09-01

Personalization of immunotherapies such as cancer vaccines and adoptive T cell therapy depends on identification of patient-specific neo-epitopes that can be specifically targeted. MuPeXI, the mutant peptide extractor and informer, is a program to identify tumor-specific peptides and assess their potential to be neo-epitopes. The program input is a file with somatic mutation calls, a list of HLA types, and optionally a gene expression profile. The output is a table with all tumor-specific peptides derived from nucleotide substitutions, insertions, and deletions, along with comprehensive annotation, including HLA binding and similarity to normal peptides. The peptides are sorted according to a priority score which is intended to roughly predict immunogenicity. We applied MuPeXI to three tumors for which predicted MHC-binding peptides had been screened for T cell reactivity, and found that MuPeXI was able to prioritize immunogenic peptides with an area under the curve of 0.63. Compared to other available tools, MuPeXI provides more information and is easier to use. MuPeXI is available as stand-alone software and as a web server at http://www.cbs.dtu.dk/services/MuPeXI .

Rapid computational identification of the targets of protein kinase inhibitors.

PubMed

Rockey, William M; Elcock, Adrian H

2005-06-16

We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.

Predicting preterm birth among participants of North Carolina’s Pregnancy Medical Home Program

PubMed Central

Tucker, Christine M.; Berrien, Kate; Menard, M. Kathryn; Herring, Amy H.; Daniels, Julie; Rowley, Diane L.; Halpern, Carolyn Tucker

2016-01-01

Objective To determine which combination of risk factors from Community Care of North Carolina’s (CCNC) Pregnancy Medical Home (PMH) risk screening form was most predictive of preterm birth (PTB) by parity and race/ethnicity. Methods This retrospective cohort included pregnant Medicaid patients screened by the PMH program before 24 weeks gestation who delivered a live birth in North Carolina between September 2011-September 2012 (N=15,428). Data came from CCNC’s Case Management Information System, Medicaid claims, and birth certificates. Logistic regression with backward stepwise elimination was used to arrive at the final models. To internally validate the predictive model, we used bootstrapping techniques. Results The prevalence of PTB was 11%. Multifetal gestation, a previous PTB, cervical insufficiency, diabetes, renal disease, and hypertension were the strongest risk factors with odds ratios ranging from 2.34 to 10.78. Non-Hispanic black race, underweight, smoking during pregnancy, asthma, other chronic conditions, nulliparity, and a history of a low birth weight infant or fetal death/second trimester loss were additional predictors in the final predictive model. About half of the risk factors prioritized by the PMH program remained in our final model (ROC=0.66). The odds of PTB associated with food insecurity and obesity differed by parity. The influence of unsafe or unstable housing and short interpregnancy interval on PTB differed by race/ethnicity. Conclusions Evaluation of the PMH risk screen provides insight to ensure women at highest risk are prioritized for care management. Using multiple data sources, salient risk factors for PTB were identified, allowing for better-targeted approaches for PTB prevention. PMID:26112751

MicroRNA-20a-5p promotes colorectal cancer invasion and metastasis by downregulating Smad4.

PubMed

Cheng, Dantong; Zhao, Senlin; Tang, Huamei; Zhang, Dongyuan; Sun, Hongcheng; Yu, Fudong; Jiang, Weiliang; Yue, Ben; Wang, Jingtao; Zhang, Meng; Yu, Yang; Liu, Xisheng; Sun, Xiaofeng; Zhou, Zongguang; Qin, Xuebin; Zhang, Xin; Yan, Dongwang; Wen, Yugang; Peng, Zhihai

2016-07-19

Tumor metastasis is one of the leading causes of poor prognosis for colorectal cancer (CRC) patients. Loss of Smad4 contributes to aggression process in many human cancers. However, the underlying precise mechanism of aberrant Smad4 expression in CRC development is still little known. miR-20a-5p negatively regulated Smad4 by directly targeting its 3'UTR in human colorectal cancer cells. miR-20a-5p not only promoted CRC cells aggression capacity in vitro and liver metastasis in vivo, but also promoted the epithelial-to-mesenchymal transition process by downregulating Smad4 expression. In addition, tissue microarray analysis obtained from 544 CRC patients' clinical characters showed that miR-20a-5p was upregulated in human CRC tissues, especially in the tissues with metastasis. High level of miR-20a-5p predicted poor prognosis in CRC patients. Five miRNA target prediction programs were applied to identify potential miRNA(s) that target(s) Smad4 in CRC. Luciferase reporter assay and transfection technique were used to validate the correlation between miR-20a-5p and Smad4 in CRC. Wound healing, transwell and tumorigenesis assays were used to explore the function of miR-20a-5p and Smad4 in CRC progression in vitro and in vivo. The association between miR-20a-5p expression and the prognosis of CRC patients was evaluated by Kaplan-Meier analysis and multivariate cox proportional hazard analyses based on tissue microarray data. miR-20a-5p, as an onco-miRNA, promoted the invasion and metastasis ability by suppressing Smad4 expression in CRC cells, and high miR-20a-5p predicted poor prognosis for CRC patients, providing a novel and promising therapeutic target in human colorectal cancer.

MicroRNA-20a-5p promotes colorectal cancer invasion and metastasis by downregulating Smad4

PubMed Central

Zhang, Dongyuan; Sun, Hongcheng; Yu, Fudong; Yue, Ben; Wang, Jingtao; Zhang, Meng; Yu, Yang; Liu, Xisheng; Sun, Xiaofeng; Zhou, Zongguang; Qin, Xuebin; Zhang, Xin; Yan, Dongwang; Wen, Yugang; Peng, Zhihai

2016-01-01

Background Tumor metastasis is one of the leading causes of poor prognosis for colorectal cancer (CRC) patients. Loss of Smad4 contributes to aggression process in many human cancers. However, the underlying precise mechanism of aberrant Smad4 expression in CRC development is still little known. Results miR-20a-5p negatively regulated Smad4 by directly targeting its 3′UTR in human colorectal cancer cells. miR-20a-5p not only promoted CRC cells aggression capacity in vitro and liver metastasis in vivo, but also promoted the epithelial-to-mesenchymal transition process by downregulating Smad4 expression. In addition, tissue microarray analysis obtained from 544 CRC patients’ clinical characters showed that miR-20a-5p was upregulated in human CRC tissues, especially in the tissues with metastasis. High level of miR-20a-5p predicted poor prognosis in CRC patients. Methods Five miRNA target prediction programs were applied to identify potential miRNA(s) that target(s) Smad4 in CRC. Luciferase reporter assay and transfection technique were used to validate the correlation between miR-20a-5p and Smad4 in CRC. Wound healing, transwell and tumorigenesis assays were used to explore the function of miR-20a-5p and Smad4 in CRC progression in vitro and in vivo. The association between miR-20a-5p expression and the prognosis of CRC patients was evaluated by Kaplan–Meier analysis and multivariate cox proportional hazard analyses based on tissue microarray data. Conclusions miR-20a-5p, as an onco-miRNA, promoted the invasion and metastasis ability by suppressing Smad4 expression in CRC cells, and high miR-20a-5p predicted poor prognosis for CRC patients, providing a novel and promising therapeutic target in human colorectal cancer. PMID:27286257

Effects of the Multidisciplinary Risk Assessment and Management Program for Patients with Diabetes Mellitus (RAMP-DM) on biomedical outcomes, observed cardiovascular events and cardiovascular risks in primary care: a longitudinal comparative study.

PubMed

Jiao, Fang Fang; Fung, Colman Siu Cheung; Wong, Carlos King Ho; Wan, Yuk Fai; Dai, Daisy; Kwok, Ruby; Lam, Cindy Lo Kuen

2014-08-21

To assess whether the Multidisciplinary Risk Assessment and Management Program for Patients with Diabetes Mellitus (RAMP-DM) led to improvements in biomedical outcomes, observed cardiovascular events and predicted cardiovascular risks after 12-month intervention in the primary care setting. A random sample of 1,248 people with diabetes enrolled to RAMP-DM for at least 12 months was selected and 1,248 people with diabetes under the usual primary care were matched by age, sex, and HbA1c level at baseline as the usual care group. Biomedical and cardiovascular outcomes were measured at baseline and at 12-month after the enrollment. Difference-in-differences approach was employed to measure the effect of RAMP-DM on the changes in biomedical outcomes, proportion of subjects reaching treatment targets, observed and predicted cardiovascular risks. Compared to the usual care group, RAMP-DM group had lower cardiovascular events incidence (1.21% vs 2.89%, P = 0.003), and net decrease in HbA1c (-0.20%, P < 0.01), SBP (-3.62 mmHg, P < 0.01) and 10-year cardiovascular disease (CVD) risks (total CVD risk, -2.06%, P < 0.01; coronary heart disease (CHD) risk, -1.43%, P < 0.01; stroke risk, -0.71%, P < 0.01). The RAMP-DM subjects witnessed significant rises in the proportion of reaching treatment targets of HbA1c, and SBP/DBP. After adjusting for confounding variables, the significance remained for HbA1c, predicted CHD and stroke risks. The RAMP-DM resulted in greater improvements in HbA1c and reduction in observed and predicted cardiovascular risks at 12 months follow-up, which indicated a risk-stratification multidisciplinary intervention was an effective strategy for managing Chinese people with diabetes in the primary care setting. ClinicalTrials.gov, NCT02034695.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

PubMed

Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

2017-12-04

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Community-Based Mindfulness Program for Disease Prevention and Health Promotion: Targeting Stress Reduction.

PubMed

Galla, Brian M; O'Reilly, Gillian A; Kitil, M Jennifer; Smalley, Susan L; Black, David S

2015-01-01

Poorly managed stress leads to detrimental physical and psychological consequences that have implications for individual and community health. Evidence indicates that U.S. adults predominantly use unhealthy strategies for stress management. This study examines the impact of a community-based mindfulness training program on stress reduction. This study used a one-group pretest-posttest design. The study took place at the UCLA Mindful Awareness Research Center in urban Los Angeles. A sample of N = 127 community residents (84% Caucasian, 74% female) were included in the study. Participants received mindfulness training through the Mindful Awareness Practices (MAPs) for Daily Living I. Mindfulness, self-compassion, and perceived stress were measured at baseline and postintervention. Paired-sample t-tests were used to test for changes in outcome measures from baseline to postintervention. Hierarchical regression analysis was fit to examine whether change in self-reported mindfulness and self-compassion predicted postintervention perceived stress scores. There were statistically significant improvements in self-reported mindfulness (t = -10.67, p < .001, d = .90), self-compassion (t = -8.50, p < .001, d = .62), and perceived stress (t = 9.28, p < .001, d = -.78) at postintervention. Change in self-compassion predicted postintervention perceived stress (β = -.44, t = -5.06, p < .001), but change in mindfulness did not predict postintervention perceived stress (β = -.04, t = -.41, p = .68). These results indicate that a community-based mindfulness training program can lead to reduced levels of psychological stress. Mindfulness training programs such as MAPs may offer a promising approach for general public health promotion through improving stress management in the urban community.

Changes in sunburn and tanning attitudes among lifeguards over a summer season.

PubMed

Hiemstra, Marieke; Glanz, Karen; Nehl, Eric

2012-03-01

Skin cancer is one of the most common cancers in the United States. Lifeguards are at increased risk of excessive sun exposure and sunburn. We sought to examine changes in: (1) sunburn frequency over a summer while controlling for sun exposure, sun protection habits, and participation in a skin cancer prevention program; and (2) tanning attitudes while controlling for participation in the program. Participants in this study were lifeguards (n = 3014) at swimming pools participating in the Pool Cool program in 2005. Lifeguards completed surveys at the beginning and end of the summer. Sequential regression analyses were used to assess changes in sunburn frequency and tanning attitudes. Sunburn frequency decreased between baseline and follow-up. Having a sunburn over the summer was significantly predicted by baseline sunburn history, ethnicity, skin cancer risk, and sun exposure. The tanning attitude, "People are more attractive if they have a tan," was significantly predicted from baseline tanning attitude and ethnicity. The second tanning attitude, "It helps to have a good base suntan," was significantly predicted by baseline tanning attitude, ethnicity, basic/enhanced group, and moderate skin cancer risk. Self-reported data and limited generalizability to lifeguards at other outdoor pools are limitations. The findings showed that previous sunburn history is an important predictor of sunburn prospectively. In addition, a more risky tanning attitude is an important predictor of future attitudes toward tanning. Active involvement in targeted prevention programs may help to increase preventive behavior and health risk reduction. Copyright © 2010 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Changes in sunburn and tanning attitudes among lifeguards over a summer season

PubMed Central

Hiemstra, Marieke; Glanz, Karen; Nehl, Eric

2013-01-01

Background Skin cancer is one of the most common cancers in the United States. Lifeguards are at increased risk of excessive sun exposure and sunburn. Objectives We sought to examine changes in: (1) sunburn frequency over a summer while controlling for sun exposure, sun protection habits, and participation in a skin cancer prevention program; and (2) tanning attitudes while controlling for participation in the program. Methods Participants in this study were lifeguards (n = 3014) at swimming pools participating in the Pool Cool program in 2005. Lifeguards completed surveys at the beginning and end of the summer. Sequential regression analyses were used to assess changes in sunburn frequency and tanning attitudes. Results Sunburn frequency decreased between baseline and follow-up. Having a sunburn over the summer was significantly predicted by baseline sunburn history, ethnicity, skin cancer risk, and sun exposure. The tanning attitude, “People are more attractive if they have a tan,” was significantly predicted from baseline tanning attitude and ethnicity. The second tanning attitude, “It helps to have a good base suntan,” was significantly predicted by baseline tanning attitude, ethnicity, basic/enhanced group, and moderate skin cancer risk. Limitations Self-reported data and limited generalizability to lifeguards at other outdoor pools are limitations. Conclusion The findings showed that previous sunburn history is an important predictor of sunburn prospectively. In addition, a more risky tanning attitude is an important predictor of future attitudes toward tanning. Active involvement in targeted prevention programs may help to increase preventive behavior and health risk reduction. PMID:21745696

Predicting Sensitivity of Breast Tumors to Src-Targeted Therapies through Assessment of Cas/Src/BCAR3 Activity

DTIC Science & Technology

2016-10-01

Amy H. Bouton, Ph.D. Associate Dean of Graduate and Medical ScienXst Programs Professor of Microbiology , Immunology, and Cancer Biology Box...We found that all of the BCAR3 in invasive breast cancer cells is present in a complex with Cas and 1Department of Microbiology , Immunology and Cancer...Harrisonburg, VA, USA. Correspondence: Dr AH Bouton, Department of Microbiology , Immunology and Cancer Biology, University of Virginia School of Medicine, Box

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

PubMed

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

PubMed

Lingner, Thomas; Kataya, Amr R; Antonicelli, Gerardo E; Benichou, Aline; Nilssen, Kjersti; Chen, Xiong-Yan; Siemsen, Tanja; Morgenstern, Burkhard; Meinicke, Peter; Reumann, Sigrun

2011-04-01

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

The CF Canada-Sick Kids Program in individual CF therapy: A resource for the advancement of personalized medicine in CF.

PubMed

Eckford, Paul D W; McCormack, Jacqueline; Munsie, Lise; He, Gengming; Stanojevic, Sanja; Pereira, Sergio L; Ho, Karen; Avolio, Julie; Bartlett, Claire; Yang, Jin Ye; Wong, Amy P; Wellhauser, Leigh; Huan, Ling Jun; Jiang, Jia Xin; Ouyang, Hong; Du, Kai; Klingel, Michelle; Kyriakopoulou, Lianna; Gonska, Tanja; Moraes, Theo J; Strug, Lisa J; Rossant, Janet; Ratjen, Felix; Bear, Christine E

2018-04-20

Therapies targeting certain CFTR mutants have been approved, yet variations in clinical response highlight the need for in-vitro and genetic tools that predict patient-specific clinical outcomes. Toward this goal, the CF Canada-Sick Kids Program in Individual CF Therapy (CFIT) is generating a "first of its kind", comprehensive resource containing patient-specific cell cultures and data from 100 CF individuals that will enable modeling of therapeutic responses. The CFIT program is generating: 1) nasal cells from drug naïve patients suitable for culture and the study of drug responses in vitro, 2) matched gene expression data obtained by sequencing the RNA from the primary nasal tissue, 3) whole genome sequencing of blood derived DNA from each of the 100 participants, 4) induced pluripotent stem cells (iPSCs) generated from each participant's blood sample, 5) CRISPR-edited isogenic control iPSC lines and 6) prospective clinical data from patients treated with CF modulators. To date, we have recruited 57 of 100 individuals to CFIT, most of whom are homozygous for F508del (to assess in-vitro: in-vivo correlations with respect to ORKAMBI response) or heterozygous for F508del and a minimal function mutation. In addition, several donors are homozygous for rare nonsense and missense mutations. Nasal epithelial cell cultures and matched iPSC lines are available for many of these donors. This accessible resource will enable development of tools that predict individual outcomes to current and emerging modulators targeting F508del-CFTR and facilitate therapy discovery for rare CF causing mutations. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Prediction of scaling physics laws for proton acceleration with extended parameter space of the NIF ARC

NASA Astrophysics Data System (ADS)

Bhutwala, Krish; Beg, Farhat; Mariscal, Derek; Wilks, Scott; Ma, Tammy

2017-10-01

The Advanced Radiographic Capability (ARC) laser at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory is the world's most energetic short-pulse laser. It comprises four beamlets, each of substantial energy ( 1.5 kJ), extended short-pulse duration (10-30 ps), and large focal spot (>=50% of energy in 150 µm spot). This allows ARC to achieve proton and light ion acceleration via the Target Normal Sheath Acceleration (TNSA) mechanism, but it is yet unknown how proton beam characteristics scale with ARC-regime laser parameters. As theory has also not yet been validated for laser-generated protons at ARC-regime laser parameters, we attempt to formulate the scaling physics of proton beam characteristics as a function of laser energy, intensity, focal spot size, pulse length, target geometry, etc. through a review of relevant proton acceleration experiments from laser facilities across the world. These predicted scaling laws should then guide target design and future diagnostics for desired proton beam experiments on the NIF ARC. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and funded by the LLNL LDRD program under tracking code 17-ERD-039.

GITR Simulation of Helium Exposed Tungsten Erosion and Redistribution in PISCES-A

NASA Astrophysics Data System (ADS)

Younkin, T. R.; Green, D. L.; Doerner, R. P.; Nishijima, D.; Drobny, J.; Canik, J. M.; Wirth, B. D.

2017-10-01

The extreme heat, charged particle, and neutron flux / fluence to plasma facing materials in magnetically confined fusion devices has motivated research to understand, predict, and mitigate the associated detrimental effects. Of relevance to the ITER divertor is the helium interaction with the tungsten divertor, the resulting erosion and migration of impurities. The linear plasma device PISCES A has performed dedicated experiments for high (4x10-22 m-2s-1) and low (4x10-21 m-2s-1) flux, 250 eV He exposed tungsten targets to assess the net and gross erosion of tungsten and volumetric transport. The temperature of the target was held between 400 and 600 degrees C. We present results of the erosion / migration / re-deposition of W during the experiment from the GITR (Global Impurity Transport) code coupled to materials response models. In particular, the modeled and experimental W I emission spectroscopy data for the 429.4 nm wavelength and net erosion through target and collector mass difference measurements are compared. Overall, the predictions are in good agreement with experiments. This material is supported by the US DOE, Office of Science, Office of Fusion Energy Sciences and Office of Advanced Scientific Computing Research through the SciDAC program on Plasma-Surface Interactions.

Mass drug administration for trachoma: how long is not long enough?

PubMed

Jimenez, Violeta; Gelderblom, Huub C; Mann Flueckiger, Rebecca; Emerson, Paul M; Haddad, Danny

2015-03-01

Blinding trachoma is targeted for elimination by 2020 using the SAFE strategy (Surgery, Antibiotics, Facial cleanliness, and Environmental improvements). Annual mass drug administration (MDA) with azithromycin is a cornerstone of this strategy. If baseline prevalence of clinical signs of trachomatous inflammation - follicular among 1-9 year-olds (TF1-9) is ≥ 10% but <30%, the World Health Organization guidelines are for at least 3 annual MDAs; if ≥ 30%, 5. We assessed the likelihood of achieving the global elimination target of TF1-9 <5% at 3 and 5 year evaluations using program reports. We used the International Trachoma Initiative's prevalence and treatment database. Of 283 cross-sectional survey pairs with baseline and follow-up data, MDA was conducted in 170 districts. Linear and logistic regression modeling was applied to these to investigate the effect of MDA on baseline prevalence. Reduction to <5% was less likely, though not impossible, at higher baseline TF1-9 prevalences. Increased number of annual MDAs, as well as no skipped MDAs, were significant predictors of reduced TF1-9 at follow-up. The probability of achieving the <5% target was <50% for areas with ≥ 30% TF1-9 prevalence at baseline, even with 7 or more continuous annual MDAs. Number of annual MDAs alone appears insufficient to predict program progress; more information on the effects of baseline prevalence, coverage, and underlying environmental and hygienic conditions is needed. Programs should not skip MDAs, and at prevalences >30%, 7 or more annual MDAs may be required to achieve the target. There are five years left before the 2020 deadline to eliminate blinding trachoma. Low endemic settings are poised to succeed in their elimination goals. However, newly-identified high prevalence districts warrant immediate inclusion in the global program. Intensified application of the SAFE strategy is needed in order to guarantee blinding trachoma elimination by 2020.

Mass Drug Administration for Trachoma: How Long Is Not Long Enough?

PubMed Central

Jimenez, Violeta; Gelderblom, Huub C.; Mann Flueckiger, Rebecca; Emerson, Paul M.; Haddad, Danny

2015-01-01

Background Blinding trachoma is targeted for elimination by 2020 using the SAFE strategy (Surgery, Antibiotics, Facial cleanliness, and Environmental improvements). Annual mass drug administration (MDA) with azithromycin is a cornerstone of this strategy. If baseline prevalence of clinical signs of trachomatous inflammation – follicular among 1-9 year-olds (TF1-9) is ≥10% but <30%, the World Health Organization guidelines are for at least 3 annual MDAs; if ≥30%, 5. We assessed the likelihood of achieving the global elimination target of TF1-9 <5% at 3 and 5 year evaluations using program reports. Methodology/Principal Findings We used the International Trachoma Initiative’s prevalence and treatment database. Of 283 cross-sectional survey pairs with baseline and follow-up data, MDA was conducted in 170 districts. Linear and logistic regression modeling was applied to these to investigate the effect of MDA on baseline prevalence. Reduction to <5% was less likely, though not impossible, at higher baseline TF1-9 prevalences. Increased number of annual MDAs, as well as no skipped MDAs, were significant predictors of reduced TF1-9 at follow-up. The probability of achieving the <5% target was <50% for areas with ≥30% TF1-9 prevalence at baseline, even with 7 or more continuous annual MDAs. Conclusions Number of annual MDAs alone appears insufficient to predict program progress; more information on the effects of baseline prevalence, coverage, and underlying environmental and hygienic conditions is needed. Programs should not skip MDAs, and at prevalences >30%, 7 or more annual MDAs may be required to achieve the target. There are five years left before the 2020 deadline to eliminate blinding trachoma. Low endemic settings are poised to succeed in their elimination goals. However, newly-identified high prevalence districts warrant immediate inclusion in the global program. Intensified application of the SAFE strategy is needed in order to guarantee blinding trachoma elimination by 2020. PMID:25799168

TarPmiR: a new approach for microRNA target site prediction.

PubMed

Ding, Jun; Li, Xiaoman; Hu, Haiyan

2016-09-15

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

PubMed Central

Messinis, Dimitris E.; Melas, Ioannis N.; Hur, Junguk; Varshney, Navya; Alexopoulos, Leonidas G.

2018-01-01

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers. PMID:29341478

Perceptions of, and Assistance Provided to, a Hypothetical Rape Victim: Differences Between Rape Disclosure Recipients and Nonrecipients

PubMed Central

Paul, Lisa A.; Kehn, Andre; Gray, Matt J.; Salapska-Gelleri, Joanna

2014-01-01

Objective Undergraduate rape disclosure recipients and nonrecipients’ sociodemographic and life experience variables, attitudes towards rape and responses to a hypothetical rape disclosure were compared to determine differences between them. Participants One-hundred-ninety-two undergraduates at three universities participated in this online survey between November 2011 – April 2012. Methods Participants reported on their rape myth acceptance (RMA) and personal direct and indirect (i.e., disclosure receipt) experiences with sexual assault. Participants also responded to a hypothetical rape disclosure. Results Disclosure recipients were more likely to report a victimization history, and less confusion and perceived ineffectiveness in helping the hypothetical victim. RMA and nonrecipient status predicted perceived victim responsibility; these variables and childhood victimization predicted confusion about helping. RMA also predicted perceived ineffectiveness of one’s helping behaviors. Victimization history and female gender predicted victim empathy. Conclusions These findings can inform sexual assault-related programming for undergraduates through the provision of targeted assistance and corrective information. PMID:24779405

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

PubMed

Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

2010-06-15

We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

LDSD POST2 Simulation and SFDT-1 Pre-Flight Launch Operations Analyses

NASA Technical Reports Server (NTRS)

Bowes, Angela L.; Davis, Jody L.; Dutta, Soumyo; Striepe, Scott A.; Ivanov, Mark C.; Powell, Richard W.; White, Joseph

2015-01-01

The Low-Density Supersonic Decelerator (LDSD) Project's first Supersonic Flight Dynamics Test (SFDT-1) occurred June 28, 2014. Program to Optimize Simulated Trajectories II (POST2) was utilized to develop trajectory simulations characterizing all SFDT-1 flight phases from drop to splashdown. These POST2 simulations were used to validate the targeting parameters developed for SFDT- 1, predict performance and understand the sensitivity of the vehicle and nominal mission designs, and to support flight test operations with trajectory performance and splashdown location predictions for vehicle recovery. This paper provides an overview of the POST2 simulations developed for LDSD and presents the POST2 simulation flight dynamics support during the SFDT-1 launch, operations, and recovery.

ADRPM-VII applied to the long-range acoustic detection problem

NASA Technical Reports Server (NTRS)

Shalis, Edward; Koenig, Gerald

1990-01-01

An acoustic detection range prediction model (ADRPM-VII) has been written for IBM PC/AT machines running on the MS-DOS operating system. The software allows the user to predict detection distances of ground combat vehicles and their associated targets when they are involved in quasi-military settings. The program can also calculate individual attenuation losses due to spherical spreading, atmospheric absorption, ground reflection and atmospheric refraction due to temperature and wind gradients while varying parameters effecting the source-receiver problem. The purpose here is to examine the strengths and limitations of ADRPM-VII by modeling the losses due to atmospheric refraction and ground absorption, commonly known as excess attenuation, when applied to the long range detection problem for distances greater than 3 kilometers.

MOST: most-similar ligand based approach to target prediction.

PubMed

Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang

2017-03-11

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

Eye movement sequence generation in humans: Motor or goal updating?

PubMed Central

Quaia, Christian; Joiner, Wilsaan M.; FitzGibbon, Edmond J.; Optican, Lance M.; Smith, Maurice A.

2011-01-01

Saccadic eye movements are often grouped in pre-programmed sequences. The mechanism underlying the generation of each saccade in a sequence is currently poorly understood. Broadly speaking, two alternative schemes are possible: first, after each saccade the retinotopic location of the next target could be estimated, and an appropriate saccade could be generated. We call this the goal updating hypothesis. Alternatively, multiple motor plans could be pre-computed, and they could then be updated after each movement. We call this the motor updating hypothesis. We used McLaughlin’s intra-saccadic step paradigm to artificially create a condition under which these two hypotheses make discriminable predictions. We found that in human subjects, when sequences of two saccades are planned, the motor updating hypothesis predicts the landing position of the second saccade in two-saccade sequences much better than the goal updating hypothesis. This finding suggests that the human saccadic system is capable of executing sequences of saccades to multiple targets by planning multiple motor commands, which are then updated by serial subtraction of ongoing motor output. PMID:21191134

The PD-1 pathway as a therapeutic target to overcome immune escape mechanisms in cancer.

PubMed

Henick, Brian S; Herbst, Roy S; Goldberg, Sarah B

2014-12-01

Immunotherapy is emerging as a powerful approach in cancer treatment. Preclinical data predicted the antineoplastic effects seen in clinical trials of programmed death-1 (PD-1) pathway inhibitors, as well as their observed toxicities. The results of early clinical trials are extraordinarily promising in several cancer types and have shaped the direction of ongoing and future studies. This review describes the biological rationale for targeting the PD-1 pathway with monoclonal antibodies for the treatment of cancer as a context for examining the results of early clinical trials. It also surveys the landscape of ongoing clinical trials and discusses their anticipated strengths and limitations. PD-1 pathway inhibition represents a new frontier in cancer immunotherapy, which shows clear evidence of activity in various tumor types including NSCLC and melanoma. Ongoing and upcoming trials will examine optimal combinations of these agents, which should further define their role across tumor types. Current limitations include the absence of a reliable companion diagnostic to predict likely responders, as well as lack of data in early-stage cancer when treatment has the potential to increase cure rates.

Experiment to demonstrate separation of Cherenkov and scintillation signals

DOE PAGES

Caravaca, J.; Descamps, F. B.; Land, B. J.; ...

2017-05-05

The ability to separately identify the Cherenkov and scintillation light components produced in scintillating mediums holds the potential for a major breakthrough in neutrino detection technology, allowing development of a large, low-threshold, directional detector with a broad physics program. Furthermore, the CHESS (CHErenkov/Scintillation Separation) experiment employs an innovative detector design with an array of small, fast photomultiplier tubes and state-of-the-art electronics to demonstrate the reconstruction of a Cherenkov ring in a scintillating medium based on photon hit time and detected photoelectron density. Our paper describes the physical properties and calibration of CHESS along with first results. The ability to reconstructmore » Cherenkov rings are demonstrated in a water target, and a time precision of 338 ± 12 ps FWHM is achieved. Finally, Monte Carlo–based predictions for the ring imaging sensitivity with a liquid scintillator target predict an efficiency for identifying Cherenkov hits of 94 ± 1 % and 81 ± 1 % in pure linear alkyl benzene (LAB) and LAB loaded with 2 g/L of a fluor, PPO, respectively, with a scintillation contamination of 12 ± 1 % and 26 ± 1 % .« less

Experiment to demonstrate separation of Cherenkov and scintillation signals

NASA Astrophysics Data System (ADS)

Caravaca, J.; Descamps, F. B.; Land, B. J.; Wallig, J.; Yeh, M.; Orebi Gann, G. D.

2017-05-01

The ability to separately identify the Cherenkov and scintillation light components produced in scintillating mediums holds the potential for a major breakthrough in neutrino detection technology, allowing development of a large, low-threshold, directional detector with a broad physics program. The CHESS (CHErenkov/Scintillation Separation) experiment employs an innovative detector design with an array of small, fast photomultiplier tubes and state-of-the-art electronics to demonstrate the reconstruction of a Cherenkov ring in a scintillating medium based on photon hit time and detected photoelectron density. This paper describes the physical properties and calibration of CHESS along with first results. The ability to reconstruct Cherenkov rings is demonstrated in a water target, and a time precision of 338 ±12 ps FWHM is achieved. Monte Carlo-based predictions for the ring imaging sensitivity with a liquid scintillator target predict an efficiency for identifying Cherenkov hits of 94 ±1 % and 81 ±1 % in pure linear alkyl benzene (LAB) and LAB loaded with 2 g/L of a fluor, PPO, respectively, with a scintillation contamination of 12 ±1 % and 26 ±1 % .

Debiasing affective forecasting errors with targeted, but not representative, experience narratives.

PubMed

Shaffer, Victoria A; Focella, Elizabeth S; Scherer, Laura D; Zikmund-Fisher, Brian J

2016-10-01

To determine whether representative experience narratives (describing a range of possible experiences) or targeted experience narratives (targeting the direction of forecasting bias) can reduce affective forecasting errors, or errors in predictions of experiences. In Study 1, participants (N=366) were surveyed about their experiences with 10 common medical events. Those who had never experienced the event provided ratings of predicted discomfort and those who had experienced the event provided ratings of actual discomfort. Participants making predictions were randomly assigned to either the representative experience narrative condition or the control condition in which they made predictions without reading narratives. In Study 2, participants (N=196) were again surveyed about their experiences with these 10 medical events, but participants making predictions were randomly assigned to either the targeted experience narrative condition or the control condition. Affective forecasting errors were observed in both studies. These forecasting errors were reduced with the use of targeted experience narratives (Study 2) but not representative experience narratives (Study 1). Targeted, but not representative, narratives improved the accuracy of predicted discomfort. Public collections of patient experiences should favor stories that target affective forecasting biases over stories representing the range of possible experiences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Selection of optimal oligonucleotide probes for microarrays usingmultiple criteria, global alignment and parameter estimation.

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

Li, Xingyuan; He, Zhili; Zhou, Jizhong

2005-10-30

The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based onmore » gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.« less

Argonaute-based programmable RNase as a tool for cleavage of highly-structured RNA.

PubMed

Dayeh, Daniel M; Cantara, William A; Kitzrow, Jonathan P; Musier-Forsyth, Karin; Nakanishi, Kotaro

2018-06-12

The recent identification and development of RNA-guided enzymes for programmable cleavage of target nucleic acids offers exciting possibilities for both therapeutic and biotechnological applications. However, critical challenges such as expensive guide RNAs and inability to predict the efficiency of target recognition, especially for highly-structured RNAs, remain to be addressed. Here, we introduce a programmable RNA restriction enzyme, based on a budding yeast Argonaute (AGO), programmed with cost-effective 23-nucleotide (nt) single-stranded DNAs as guides. DNA guides offer the advantage that diverse sequences can be easily designed and purchased, enabling high-throughput screening to identify optimal recognition sites in the target RNA. Using this DNA-induced slicing complex (DISC) programmed with 11 different guide DNAs designed to span the sequence, sites of cleavage were identified in the 352-nt human immunodeficiency virus type 1 5'-untranslated region. This assay, coupled with primer extension and capillary electrophoresis, allows detection and relative quantification of all DISC-cleavage sites simultaneously in a single reaction. Comparison between DISC cleavage and RNase H cleavage reveals that DISC not only cleaves solvent-exposed sites, but also sites that become more accessible upon DISC binding. This study demonstrates the advantages of the DISC system for programmable cleavage of highly-structured, functional RNAs.

NOX2 As a Target for Drug Development: Indications, Possible Complications, and Progress

PubMed Central

Diebold, Becky A.; Smith, Susan M.E.; Li, Yang

2015-01-01

Abstract Significance: NOX2 is important for host defense, and yet is implicated in a large number of diseases in which inflammation plays a role in pathogenesis. These include acute and chronic lung inflammatory diseases, stroke, traumatic brain injury, and neurodegenerative diseases, including Alzheimer's and Parkinson's Diseases. Recent Advances: Recent drug development programs have targeted several NOX isoforms that are implicated in a variety of diseases. The focus has been primarily on NOX4 and NOX1 rather than on NOX2, due, in part, to concerns about possible immunosuppressive side effects. Nevertheless, NOX2 clearly contributes to the pathogenesis of many inflammatory diseases, and its inhibition is predicted to provide a novel therapeutic approach. Critical Issues: Possible side effects that might arise from targeting NOX2 are discussed, including the possibility that such inhibition will contribute to increased infections and/or autoimmune disorders. The state of the field with regard to existing NOX2 inhibitors and targeted development of novel inhibitors is also summarized. Future Directions: NOX2 inhibitors show particular promise for the treatment of inflammatory diseases, both acute and chronic. Theoretical side effects include pro-inflammatory and autoimmune complications and should be considered in any therapeutic program, but in our opinion, available data do not indicate that they are sufficiently likely to eliminate NOX2 as a drug target, particularly when weighed against the seriousness of many NOX2-related indications. Model studies demonstrating efficacy with minimal side effects are needed to encourage future development of NOX2 inhibitors as therapeutic agents. Antioxid. Redox Signal. 23, 375–405. PMID:24512192

Targeted Employment Subsidies: Issues of Structure and Design.

ERIC Educational Resources Information Center

Bishop, John; Haveman, Robert

Effects of variations in the structure of targeted employment subsidy programs on the attainment of program objectives are explored in this paper. First, the objectives that underlie targeted subsidy programs are outlined in relation to individual program characteristics and the economics of such programs are discussed. Then the wide range of…

Motor cortex guides selection of predictable movement targets

PubMed Central

Woodgate, Philip J.W.; Strauss, Soeren; Sami, Saber A.; Heinke, Dietmar

2016-01-01

The present paper asks whether the motor cortex contributes to prediction-based guidance of target selection. This question was inspired by recent evidence that suggests (i) recurrent connections from the motor system into the attentional system may extract movement-relevant perceptual information and (ii) that the motor cortex cannot only generate predictions of the sensory consequences of movements but may also operate as predictor of perceptual events in general. To test this idea we employed a choice reaching task requiring participants to rapidly reach and touch a predictable or unpredictable colour target. Motor cortex activity was modulated via transcranial direct current stimulation (tDCS). In Experiment 1 target colour repetitions were predictable. Under such conditions anodal tDCS facilitated selection versus sham and cathodal tDCS. This improvement was apparent for trajectory curvature but not movement initiation. Conversely, where no predictability of colour was embedded reach performance was unaffected by tDCS. Finally, the results of a key-press experiment suggested that motor cortex involvement is restricted to tasks where the predictable target colour is movement-relevant. The outcomes are interpreted as evidence that the motor system contributes to the top-down guidance of selective attention to movement targets. PMID:25835319

The influence of maternal health literacy and child’s age on participation in social welfare programs

PubMed Central

Pati, Susmita; Siewert, Elizabeth; Wong, Angie T.; Bhatt, Suraj K.; Calixte, Rose E.; Cnaan, Avital

2013-01-01

Objective To determine the influence of maternal health literacy and child’s age on participation in social welfare programs benefiting children. Methods In a longitudinal prospective cohort study of 560 Medicaid-eligible mother-infant dyads recruited in Philadelphia, maternal health literacy was assessed using the Test of Functional Health Literacy in Adults (short version). Participation in social welfare programs (Temporary Assistance to Needy Families [TANF], Supplemental Nutrition Assistance Program [SNAP], Special Supplemental Nutrition Program for Women, Infants, and Children [WIC], child care subsidy, and public housing) was self-reported at child’s birth, and at the 6, 12, 18, 24 month follow-up interviews. Generalized estimating equations quantified the strength of maternal health literacy as an estimator of program participation. Results The mothers were primarily African-Americans (83%), single (87%), with multiple children (62%). Nearly 24% of the mothers had inadequate or marginal health literacy. Children whose mothers had inadequate health literacy were less likely to receive child care subsidy (adjusted OR= 0.54, 95% CI: 0.34–0.85) than children whose mothers had adequate health literacy. Health literacy was not a significant predictor for TANF, SNAP, WIC or housing assistance. The predicted probability for participation in all programs decreased from birth to 24 months. Most notably, predicted WIC participation declined rapidly after age one. Conclusions During the first 24 months, mothers with inadequate health literacy could benefit from simplified or facilitated child care subsidy application processes. Targeted outreach and enrollment efforts conducted by social welfare programs need to take into account the changing needs of families as children age. PMID:23990157

Predicting Low Accrual in the National Cancer Institute’s Cooperative Group Clinical Trials

PubMed Central

Bennette, Caroline S.; Ramsey, Scott D.; McDermott, Cara L.; Carlson, Josh J.; Basu, Anirban; Veenstra, David L.

2016-01-01

Background: The extent to which trial-level factors differentially influence accrual to trials has not been comprehensively studied. Our objective was to evaluate the empirical relationship and predictive properties of putative risk factors for low accrual in the National Cancer Institute’s (NCI’s) Cooperative Group Program, now the National Clinical Trials Network (NCTN). Methods: Data from 787 phase II/III adult NCTN-sponsored trials launched between 2000 and 2011 were used to develop a logistic regression model to predict low accrual, defined as trials that closed with or were accruing at less than 50% of target; 46 trials opened between 2012 and 2013 were used for prospective validation. Candidate predictors were identified from a literature review and expert interviews; final predictors were selected using stepwise regression. Model performance was evaluated by calibration and discrimination via the area under the curve (AUC). All statistical tests were two-sided. Results: Eighteen percent (n = 145) of NCTN-sponsored trials closed with low accrual or were accruing at less than 50% of target three years or more after initiation. A multivariable model of twelve trial-level risk factors had good calibration and discrimination for predicting trials with low accrual (AUC in trials launched 2000–2011 = 0.739, 95% confidence interval [CI] = 0.696 to 0.783]; 2012–2013: AUC = 0.732, 95% CI = 0.547 to 0.917). Results were robust to different definitions of low accrual and predictor selection strategies. Conclusions: We identified multiple characteristics of NCTN-sponsored trials associated with low accrual, several of which have not been previously empirically described, and developed a prediction model that can provide a useful estimate of accrual risk based on these factors. Future work should assess the role of such prediction tools in trial design and prioritization decisions. PMID:26714555

Predictive modeling of mosquito abundance and dengue transmission in Kenya

NASA Astrophysics Data System (ADS)

Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.

2017-12-01

Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.

Application of the docking program SOL for CSAR benchmark.

PubMed

Sulimov, Alexey V; Kutov, Danil C; Oferkin, Igor V; Katkova, Ekaterina V; Sulimov, Vladimir B

2013-08-26

This paper is devoted to results obtained by the docking program SOL and the post-processing program DISCORE at the CSAR benchmark. SOL and DISCORE programs are described. SOL is the original docking program developed on the basis of the genetic algorithm, MMFF94 force field, rigid protein, precalculated energy grid including desolvation in the frame of simplified GB model, vdW, and electrostatic interactions and taking into account the ligand internal strain energy. An important SOL feature is the single- or multi-processor performance for up to hundreds of CPUs. DISCORE improves the binding energy scoring by the local energy optimization of the ligand docked pose and a simple linear regression on the base of available experimental data. The docking program SOL has demonstrated a good ability for correct ligand positioning in the active sites of the tested proteins in most cases of CSAR exercises. SOL and DISCORE have not demonstrated very exciting results on the protein-ligand binding free energy estimation. Nevertheless, for some target proteins, SOL and DISCORE were among the first in prediction of inhibition activity. Ways to improve SOL and DISCORE are discussed.

Improving Emotional Intelligence through Personality Development: The Effect of the Smart Phone Application based Dharma Life Program on Emotional Intelligence

PubMed Central

Poonamallee, Latha; Harrington, Alex M.; Nagpal, Manisha; Musial, Alec

2018-01-01

Emotional intelligence is established to predict success in leadership effectiveness in various contexts and has been linked to personality factors. This paper introduces Dharma Life Program, a novel approach to improving emotional intelligence by targeting maladaptive personality traits and triggering neuroplasticity through the use of a smart-phone application and mentoring. The program uses neuroplasticity to enable users to create a more adaptive application of their maladaptive traits, thus improving their emotional intelligence. In this study 26 participants underwent the Dharma Life Program in a leadership development setting. We assessed their emotional and social intelligence before and after the Dharma Life Program intervention using the Emotional and Social Competency Inventory (ESCI). The study found a significant improvement in the lowest three competencies and a significant improvement in almost all domains for the entire sample. Our findings suggest that the completion of the Dharma Life Program has a significant positive effect on Emotional and Social Competency scores and offers a new avenue for improving emotional intelligence competencies. PMID:29527182

Improving Emotional Intelligence through Personality Development: The Effect of the Smart Phone Application based Dharma Life Program on Emotional Intelligence.

PubMed

Poonamallee, Latha; Harrington, Alex M; Nagpal, Manisha; Musial, Alec

2018-01-01

Emotional intelligence is established to predict success in leadership effectiveness in various contexts and has been linked to personality factors. This paper introduces Dharma Life Program, a novel approach to improving emotional intelligence by targeting maladaptive personality traits and triggering neuroplasticity through the use of a smart-phone application and mentoring. The program uses neuroplasticity to enable users to create a more adaptive application of their maladaptive traits, thus improving their emotional intelligence. In this study 26 participants underwent the Dharma Life Program in a leadership development setting. We assessed their emotional and social intelligence before and after the Dharma Life Program intervention using the Emotional and Social Competency Inventory (ESCI). The study found a significant improvement in the lowest three competencies and a significant improvement in almost all domains for the entire sample. Our findings suggest that the completion of the Dharma Life Program has a significant positive effect on Emotional and Social Competency scores and offers a new avenue for improving emotional intelligence competencies.

Deploying Solid Targets in Dense Plasma Focus Devices for Improved Neutron Yields

NASA Astrophysics Data System (ADS)

Podpaly, Y. A.; Chapman, S.; Povilus, A.; Falabella, S.; Link, A.; Shaw, B. H.; Cooper, C. M.; Higginson, D.; Holod, I.; Sipe, N.; Gall, B.; Schmidt, A. E.

2017-10-01

We report on recent progress in using solid targets in dense plasma focus (DPF) devices. DPFs have been observed to generate energetic ion beams during the pinch phase; these beams interact with the dense plasma in the pinch region as well as the background gas and are believed to be the primary neutron generation mechanism for a D2 gas fill. Targets can be placed in the beam path to enhance neutron yield and to shorten the neutron pulse if desired. In this work, we measure yields from placing titanium deuteride foils, deuterated polyethylene, and non-deuterated control targets in deuterium filled DPFs at both megajoule and kilojoule scales. Furthermore, we have deployed beryllium targets in a helium gas-filled, kilojoule scale DPF for use as a potential AmBe radiological source replacement. Neutron yield, neutron time of flight, and optical images are used to diagnose the effectiveness of target deployments relative to particle-in-cell simulation predictions. A discussion of target holder engineering for material compatibility and damage control will be shown as well. Prepared by LLNL under Contract DE-AC52-07NA27344. Supported by the Office of Defense Nuclear Nonproliferation Research and Development within U.S. DOE's National Nuclear Security Administration and the LLNL Institutional Computing Grand Challenge program.

Contextual remapping in visual search after predictable target-location changes.

PubMed

Conci, Markus; Sun, Luning; Müller, Hermann J

2011-07-01

Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

On Why Targets Evoke P3 Components in Prediction Tasks: Drawing an Analogy between Prediction and Matching Tasks

PubMed Central

Verleger, Rolf; Cäsar, Stephanie; Siller, Bastian; Śmigasiewicz, Kamila

2017-01-01

P3 is the most conspicuous component in recordings of stimulus-evoked EEG potentials from the human scalp, occurring whenever some task has to be performed with the stimuli. The process underlying P3 has been assumed to be the updating of expectancies. More recently, P3 has been related to decision processing and to activation of established stimulus-response associations (S/R-link hypothesis). However, so far this latter approach has not provided a conception about how to explain the occurrence of P3 with predicted stimuli, although P3 was originally discovered in a prediction task. The present article proposes such a conception. We assume that the internal responses right or wrong both become associatively linked to each predicted target and that one of these two response alternatives gets activated as a function of match or mismatch of the target to the preceding prediction. This seems similar to comparison tasks where responses depend on the matching of the target stimulus with a preceding first stimulus (S1). Based on this idea, this study compared the effects of frequencies of first events (predictions or S1) on target-evoked P3s in prediction and comparison tasks. Indeed, frequencies not only of targets but also of first events had similar effects across tasks on target-evoked P3s. These results support the notion that P3 evoked by predicted stimuli reflects activation of appropriate internal “match” or “mismatch” responses, which is compatible with S/R-link hypothesis. PMID:29066965

Use of programmed cell death protein ligand 1 assay to predict the outcomes of non-small cell lung cancer patients treated with immune checkpoint inhibitors

PubMed Central

Tibaldi, Carmelo; Lunghi, Alice; Baldini, Editta

2017-01-01

The recent discovery of immune checkpoints inhibitors, especially anti-programmed cell death protein 1 (PD-1) and anti-programmed cell death protein ligand 1 (PD-L1) monoclonal antibodies, has opened new scenarios in the management of non-small cell lung cancer (NSCLC) and this new class of drugs has achieved a rapid development in the treatment of this disease. However, considering the costs of these drugs and the fact that only a subset of patients experience long-term disease control, the identification of predictive biomarkers for the selection of candidates suitable for treatment has become a priority. The research focused mainly on the expression of the PD-L1 receptor on both tumor cells and/or immune infiltrates determined by immunohistochemistry (IHC). However, different checkpoint inhibitors were tested, different IHC assays were used, different targets were considered (tumor cells, immune infiltrates or both) and different expression thresholds were employed in clinical trials. In some trials the assay was used prospectively to select the patients, while in other trials it was evaluated retrospectively. Some confusion emerges, which makes it difficult to easily compare the literature data and to translate them in practice management. This mini-review shows the possibilities and pitfalls of the PD-L1 expression to predict the activity and efficacy of anti PD1/PD-L1 monoclonal antibodies in the treatment of NSCLC. PMID:28848698

Hidden Efficiencies: Making Completion of the Pediatric Vaccine Schedule More Efficient for Physicians

PubMed Central

Ciarametaro, Mike; Bradshaw, Steven E.; Guiglotto, Jillian; Hahn, Beth; Meier, Genevieve

2015-01-01

Abstract The objective of this work is to demonstrate the potential time and labor savings that may result from increased use of combination vaccinations. The study (GSK study identifier: HO-12-4735) was a model developed to evaluate the efficiency of the pediatric vaccine schedule, using time and motion studies. The model considered vaccination time and the associated labor costs, but vaccination acquisition costs were not considered. We also did not consider any efficacy or safety differences between formulations. The model inputs were supported by a targeted literature review. The reference year for the model was 2012. The most efficient vaccination program using currently available vaccines was predicted to reduce costs through a combination of fewer injections (62%) and less time per vaccination (38%). The most versus the least efficient vaccine program was predicted to result in a 47% reduction in vaccination time and a 42% reduction in labor and supply costs. The estimated administration cost saving with the most versus the least efficient program was estimated to be nearly US $45 million. If hypothetical 6- or 7-valent vaccines are developed using the already most efficient schedule by adding additional antigens (pneumococcal conjugate vaccine and Haemophilus influenzae type b) to the most efficient 5-valent vaccine, the savings are predicted to be even greater. Combination vaccinations reduce the time burden of the childhood immunization schedule and could create the potential to improve vaccination uptake and compliance as a result of fewer required injections. PMID:25634165

Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities

PubMed Central

Cramer, Richard D.

2015-01-01

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response. PMID:26065424

Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-01-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

Measurements to predict the time of target replacement of a helical tomotherapy.

PubMed

Kampfer, Severin; Schell, Stefan; Duma, Marciana N; Wilkens, Jan J; Kneschaurek, Peter

2011-11-15

Intensity-modulated radiation therapy (IMRT) requires more beam-on time than normal open field treatment. Consequently, the machines wear out and need more spare parts. A helical tomotherapy treatment unit needs a periodical tungsten target replacement, which is a time consuming event. To be able to predict the next replacement would be quite valuable. We observed unexpected variations towards the end of the target lifetime in the performed pretreatment measurements for patient plan verification. Thus, we retrospectively analyze the measurements of our quality assurance program. The time dependence of the quotient of two simultaneous dose measurements at different depths within a phantom for a fixed open field irradiation is evaluated. We also assess the time-dependent changes of an IMRT plan measurement and of a relative depth dose curve measurement. Additionally, we performed a Monte Carlo simulation with Geant4 to understand the physical reasons for the measured values. Our measurements show that the dose at a specified depth compared to the dose in shallower regions of the phantom declines towards the end of the target lifetime. This reproducible effect can be due to the lowering of the mean energy of the X-ray spectrum. These results are supported by the measurements of the IMRT plan, as well as the study of the relative depth dose curve. Furthermore, the simulation is consistent with these findings since it provides a possible explanation for the reduction of the mean energy for thinner targets. It could be due to the lowering of low energy photon self-absorption in a worn out and therefore thinner target. We state a threshold value for our measurement at which a target replacement should be initiated. Measurements to observe a change in the energy are good predictors of the need for a target replacement. However, since all results support the softening of the spectrum hypothesis, all depth-dependent setups are viable for analyzing the deterioration of the tungsten target. The suggested measurements and criteria to replace the target can be very helpful for every user of a TomoTherapy machine.

Prediction in a visual language: real-time sentence processing in American Sign Language across development.

PubMed

Lieberman, Amy M; Borovsky, Arielle; Mayberry, Rachel I

2018-01-01

Prediction during sign language comprehension may enable signers to integrate linguistic and non-linguistic information within the visual modality. In two eyetracking experiments, we investigated American Sign language (ASL) semantic prediction in deaf adults and children (aged 4-8 years). Participants viewed ASL sentences in a visual world paradigm in which the sentence-initial verb was either neutral or constrained relative to the sentence-final target noun. Adults and children made anticipatory looks to the target picture before the onset of the target noun in the constrained condition only, showing evidence for semantic prediction. Crucially, signers alternated gaze between the stimulus sign and the target picture only when the sentential object could be predicted from the verb. Signers therefore engage in prediction by optimizing visual attention between divided linguistic and referential signals. These patterns suggest that prediction is a modality-independent process, and theoretical implications are discussed.

Eye movements and word skipping during reading: Effects of word length and predictability

PubMed Central

Rayner, Keith; Slattery, Timothy J.; Drieghe, Denis; Liversedge, Simon P.

2012-01-01

The extent to which target words were predictable from prior context was varied: half of the target words were predictable and the other half were unpredictable. In addition, the length of the target word varied: the target words were short (4–6 letters), medium (7–9 letters), or long (10–12 letters). Length and predictability both yielded strong effects on the probability of skipping the target words and on the amount of time readers fixated the target words (when they were not skipped). However, there was no interaction in any of the measures examined for either skipping or fixation time. The results demonstrate that word predictability (due to contextual constraint) and word length have strong and independent influences on word skipping and fixation durations. Furthermore, since the long words extended beyond the word identification span, the data indicate that skipping can occur on the basis of partial information in relation to word identity. PMID:21463086

Use of mutation spectra analysis software.

PubMed

Rogozin, I; Kondrashov, F; Glazko, G

2001-02-01

The study and comparison of mutation(al) spectra is an important problem in molecular biology, because these spectra often reflect on important features of mutations and their fixation. Such features include the interaction of DNA with various mutagens, the function of repair/replication enzymes, and properties of target proteins. It is known that mutability varies significantly along nucleotide sequences, such that mutations often concentrate at certain positions, called "hotspots," in a sequence. In this paper, we discuss in detail two approaches for mutation spectra analysis: the comparison of mutation spectra with a HG-PUBL program, (FTP: sunsite.unc.edu/pub/academic/biology/dna-mutations/hyperg) and hotspot prediction with the CLUSTERM program (www.itba.mi.cnr.it/webmutation; ftp.bionet.nsc.ru/pub/biology/dbms/clusterm.zip). Several other approaches for mutational spectra analysis, such as the analysis of a target protein structure, hotspot context revealing, multiple spectra comparisons, as well as a number of mutation databases are briefly described. Mutation spectra in the lacI gene of E. coli and the human p53 gene are used for illustration of various difficulties of such analysis. Copyright 2001 Wiley-Liss, Inc.

The design of a purpose-built exergame for fall prediction and prevention for older people.

PubMed

Marston, Hannah R; Woodbury, Ashley; Gschwind, Yves J; Kroll, Michael; Fink, Denis; Eichberg, Sabine; Kreiner, Karl; Ejupi, Andreas; Annegarn, Janneke; de Rosario, Helios; Wienholtz, Arno; Wieching, Rainer; Delbaere, Kim

2015-01-01

Falls in older people represent a major age-related health challenge facing our society. Novel methods for delivery of falls prevention programs are required to increase effectiveness and adherence to these programs while containing costs. The primary aim of the Information and Communications Technology-based System to Predict and Prevent Falls (iStoppFalls) project was to develop innovative home-based technologies for continuous monitoring and exercise-based prevention of falls in community-dwelling older people. The aim of this paper is to describe the components of the iStoppFalls system. The system comprised of 1) a TV, 2) a PC, 3) the Microsoft Kinect, 4) a wearable sensor and 5) an assessment and training software as the main components. The iStoppFalls system implements existing technologies to deliver a tailored home-based exercise and education program aimed at reducing fall risk in older people. A risk assessment tool was designed to identify fall risk factors. The content and progression rules of the iStoppFalls exergames were developed from evidence-based fall prevention interventions targeting muscle strength and balance in older people. The iStoppFalls fall prevention program, used in conjunction with the multifactorial fall risk assessment tool, aims to provide a comprehensive and individualised, yet novel fall risk assessment and prevention program that is feasible for widespread use to prevent falls and fall-related injuries. This work provides a new approach to engage older people in home-based exercise programs to complement or provide a potentially motivational alternative to traditional exercise to reduce the risk of falling.

Finding the Perfect Match: Factors That Influence Family Medicine Residency Selection.

PubMed

Wright, Katherine M; Ryan, Elizabeth R; Gatta, John L; Anderson, Lauren; Clements, Deborah S

2016-04-01

Residency program selection is a significant experience for emerging physicians, yet there is limited information about how applicants narrow their list of potential programs. This study examines factors that influence residency program selection among medical students interested in family medicine at the time of application. Medical students with an expressed interest in family medicine were invited to participate in a 37-item, online survey. Students were asked to rate factors that may impact residency selection on a 6-point Likert scale in addition to three open-ended qualitative questions. Mean values were calculated for each survey item and were used to determine a rank order for selection criteria. Logistic regression analysis was performed to identify factors that predict a strong interest in urban, suburban, and rural residency programs. Logistic regression was also used to identify factors that predict a strong interest in academic health center-based residencies, community-based residencies, and community-based residencies with an academic affiliation. A total of 705 medical students from 32 states across the country completed the survey. Location, work/life balance, and program structure (curriculum, schedule) were rated the most important factors for residency selection. Logistic regression analysis was used to refine our understanding of how each factor relates to specific types of residencies. These findings have implications for how to best advise students in selecting a residency, as well as marketing residencies to the right candidates. Refining the recruitment process will ensure a better fit between applicants and potential programs. Limited recruitment resources may be better utilized by focusing on targeted dissemination strategies.

Evidence-based intervention against bullying and cyberbullying: Evaluation of the NoTrap! program in two independent trials.

PubMed

Palladino, Benedetta E; Nocentini, Annalaura; Menesini, Ersilia

2016-01-01

The NoTrap! (Noncadiamointrappola!) program is a school-based intervention, which utilizes a peer-led approach to prevent and combat both traditional bullying and cyberbullying. The aim of the present study was to evaluate the efficacy of the third Edition of the program in accordance with the recent criteria for evidence-based interventions. Towards this aim, two quasi-experimental trials involving adolescents (age M = 14.91, SD = .98) attending their first year at different high schools were conducted. In Trial 1 (control group, n = 171; experimental group, n = 451), latent growth curve models for data from pre-, middle- and post-tests showed that intervention significantly predicted change over time in all the target variables (victimization, bullying, cybervictimization, and cyberbullying). Specifically, target variables were stable for the control group but decreased significantly over time for the experimental group. Long-term effects at the follow up 6 months later were also found. In Trial 2 (control group, n = 227; experimental group, n = 234), the moderating effect of gender was examined and there was a reported decrease in bullying and cyberbullying over time (pre- and post-test) in the experimental group but not the control group, and this decrease was similar for boys and girls. © 2016 Wiley Periodicals, Inc.

Predicting age groups of Twitter users based on language and metadata features

PubMed Central

Morgan-Lopez, Antonio A.; Chew, Robert F.; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research. PMID:28850620

Predicting age groups of Twitter users based on language and metadata features.

PubMed

Morgan-Lopez, Antonio A; Kim, Annice E; Chew, Robert F; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

Using a pattern-centered approach to assess sexual risk-taking in study abroad students.

PubMed

Marcantonio, Tiffany; Angelone, D J; Sledjeski, Eve

2016-01-01

The purpose of this study was to examine the impact of several potential factors related to sexually risky behaviors in study abroad students. The authors utilized a pattern-centered analysis to identify specific groups that can be targeted for intervention. The sample consisted of 173 students who studied abroad in a variety of international locations for an average of 4 months. Participants completed questionnaires informed by the Triandis Theory of Interpersonal Behavior that have been predictive of risky sex in traditional traveling environments. The analyses revealed 3 different pathways for risky sexual behavior: Environmental involvement, historical condom use, and intentions to engage in risky sex. These findings can be used for identification of specific high-risk groups of students who can be targeted for predeparture prevention programs.

The impact of indicated prevention and early intervention on co-morbid eating disorder and depressive symptoms: a systematic review.

PubMed

Rodgers, Rachel F; Paxton, Susan J

2014-01-01

Depressive and eating disorder symptoms are highly comorbid. To date, however, little is known regarding the efficacy of existing programs in decreasing concurrent eating disorder and depressive symptoms. We conducted a systematic review of selective and indicated controlled prevention and early intervention programs that assessed both eating disorder and depressive symptoms. We identified a total of 26 studies. The large majority of identified interventions (92%) were successful in decreasing eating disorder symptoms. However fewer than half (42%) were successful in decreasing both eating disorder and depressive symptoms. Intervention and participant characteristics did not predict success in decreasing depressive symptoms. Indicated prevention and early intervention programs targeting eating disorder symptoms are limited in their success in decreasing concurrent depressive symptoms. Further efforts to develop more efficient interventions that are successful in decreasing both eating disorder and depressive symptoms are warranted.

The influence of concrete support on child welfare program engagement, progress, and recurrence.

PubMed

Rostad, Whitney L; Rogers, Tia McGill; Chaffin, Mark J

2017-01-01

Families living in poverty are significantly more likely to become involved with child welfare services, and consequently, referred to interventions that target abusive and neglectful parenting practices. Program engagement and retention are difficult to achieve, possibly because of the concrete resource insufficiencies that may have contributed to a family's involvement with services in the first place. Various strategies have been used to enhance program completion, such as motivational interventions, monetary incentives, and financial assistance with concrete needs. This study examines the influence of adjunctive concrete support provided by home visitors on families' ( N = 1754) engagement, retention, and satisfaction with services as well as parenting outcomes. Using propensity stratification, mixed modeling procedures revealed that increasing concrete support predicted greater engagement, satisfaction, goal attainment, and lower short-term recidivism. Results suggest that adjunctive concrete support is a potentially beneficial strategy for promoting service engagement and satisfaction and increasing short-term child safety.

Sex differences in prenatal epigenetic programming of stress pathways.

PubMed

Bale, Tracy L

2011-07-01

Maternal stress experience is associated with neurodevelopmental disorders including schizophrenia and autism. Recent studies have examined mechanisms by which changes in the maternal milieu may be transmitted to the developing embryo and potentially translated into programming of the epigenome. Animal models of prenatal stress have identified important sex- and temporal-specific effects on offspring stress responsivity. As dysregulation of stress pathways is a common feature in most neuropsychiatric diseases, molecular and epigenetic analyses at the maternal-embryo interface, especially in the placenta, may provide unique insight into identifying much-needed predictive biomarkers. In addition, as most neurodevelopmental disorders present with a sex bias, examination of sex differences in the inheritance of phenotypic outcomes may pinpoint gene targets and specific windows of vulnerability in neurodevelopment, which have been disrupted. This review discusses the association and possible contributing mechanisms of prenatal stress in programming offspring stress pathway dysregulation and the importance of sex.

The influence of concrete support on child welfare program engagement, progress, and recurrence

PubMed Central

Rostad, Whitney L.; Rogers, Tia McGill; Chaffin, Mark J.

2016-01-01

Families living in poverty are significantly more likely to become involved with child welfare services, and consequently, referred to interventions that target abusive and neglectful parenting practices. Program engagement and retention are difficult to achieve, possibly because of the concrete resource insufficiencies that may have contributed to a family's involvement with services in the first place. Various strategies have been used to enhance program completion, such as motivational interventions, monetary incentives, and financial assistance with concrete needs. This study examines the influence of adjunctive concrete support provided by home visitors on families’ (N = 1754) engagement, retention, and satisfaction with services as well as parenting outcomes. Using propensity stratification, mixed modeling procedures revealed that increasing concrete support predicted greater engagement, satisfaction, goal attainment, and lower short-term recidivism. Results suggest that adjunctive concrete support is a potentially beneficial strategy for promoting service engagement and satisfaction and increasing short-term child safety. PMID:28533569

#2) EPA Perspective - Exposure and Effects Prediction and ...

EPA Pesticide Factsheets

Outline •Biomarkers as a risk assessment tool–exposure assessment & risk characterization•CDC’s NHANES as a source of biomarker data–history, goals & available data•Review of NHANES publications (1999-2013)–chemicals, uses, trends & challenges•NHANES biomarker case study–recommendations for future research The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

PubMed

Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

Enhanced guide-RNA design and targeting analysis for precise CRISPR genome editing of single and consortia of industrially relevant and non-model organisms.

PubMed

Mendoza, Brian J; Trinh, Cong T

2018-01-01

Genetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions. To accommodate the use of CRISPR/Cas systems for genome editing across organisms, we have developed a novel method, named CRISPR Associated Software for Pathway Engineering and Research (CASPER), for identifying on- and off-targets with enhanced predictability coupled with an analysis of non-unique (repeated) targets to assist in editing any organism with various endonucleases. Utilizing CASPER, we demonstrated a modest 2.4% and significant 30.2% improvement (F-test, P < 0.05) over the conventional methods for predicting on- and off-target activities, respectively. Further we used CASPER to develop novel applications in genome editing: multitargeting analysis (i.e. simultaneous multiple-site modification on a target genome with a sole guide-RNA requirement) and multispecies population analysis (i.e. guide-RNA design for genome editing across a consortium of organisms). Our analysis on a selection of industrially relevant organisms revealed a number of non-unique target sites associated with genes and transposable elements that can be used as potential sites for multitargeting. The analysis also identified shared and unshared targets that enable genome editing of single or multiple genomes in a consortium of interest. We envision CASPER as a useful platform to enhance the precise CRISPR genome editing for metabolic engineering and synthetic biology applications. https://github.com/TrinhLab/CASPER. ctrinh@utk.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences

PubMed Central

Šulc, Miroslav; Marín, Ray M.; Robins, Harlan S.; Vaníček, Jiří

2015-01-01

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3′ untranslated regions (3′ UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3′ UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA–mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA–mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats. PMID:25948580

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

Milostan, Catharina; Levin, Todd; Muehleisen, Ralph T.

Many electric utilities operate energy efficiency incentive programs that encourage increased dissemination and use of energy-efficient (EE) products in their service territories. The programs can be segmented into three broad categories—downstream incentive programs target product end users, midstream programs target product distributors, and upstream programs target product manufacturers. Traditional downstream programs have had difficulty engaging Small Business/Small Portfolio (SBSP) audiences, and an opportunity exists to expand Commercial Midstream Incentive Programs (CMIPs) to reach this market segment instead.

76 FR 34953 - Funding Opportunity Title: Risk Management Education in Targeted States (Targeted States Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-15

... Corporation Funding Opportunity Title: Risk Management Education in Targeted States (Targeted States Program... Corporation (FCIC), operating through the Risk Management Agency (RMA), announces its intent to award... same time as funding availability for similar but separate program, the Risk Management Education and...

Smooth Pursuit Eye Movement Deficits in Patients With Whiplash and Neck Pain are Modulated by Target Predictability.

PubMed

Janssen, Malou; Ischebeck, Britta K; de Vries, Jurryt; Kleinrensink, Gert-Jan; Frens, Maarten A; van der Geest, Jos N

2015-10-01

This is a cross-sectional study. The purpose of this study is to support and extend previous observations on oculomotor disturbances in patients with neck pain and whiplash-associated disorders (WADs) by systematically investigating the effect of static neck torsion on smooth pursuit in response to both predictably and unpredictably moving targets using video-oculography. Previous studies showed that in patients with neck complaints, for instance due to WAD, extreme static neck torsion deteriorates smooth pursuit eye movements in response to predictably moving targets compared with healthy controls. Eye movements in response to a smoothly moving target were recorded with video-oculography in a heterogeneous group of 55 patients with neck pain (including 11 patients with WAD) and 20 healthy controls. Smooth pursuit performance was determined while the trunk was fixed in 7 static rotations relative to the head (from 45° to the left to 45° to right), using both predictably and unpredictably moving stimuli. Patients had reduced smooth pursuit gains and smooth pursuit gain decreased due to neck torsion. Healthy controls showed higher gains for predictably moving targets compared with unpredictably moving targets, whereas patients with neck pain had similar gains in response to both types of target movements. In 11 patients with WAD, increased neck torsion decreased smooth pursuit performance, but only for predictably moving targets. Smooth pursuit of patients with neck pain is affected. The previously reported WAD-specific decline in smooth pursuit due to increased neck torsion seems to be modulated by the predictability of the movement of the target. The observed oculomotor disturbances in patients with WAD are therefore unlikely to be induced by impaired neck proprioception alone. 3.

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Designing Tyrosinase siRNAs by Multiple Prediction Algorithms and Evaluation of Their Anti-Melanogenic Effects.

PubMed

Kwon, Ok-Seon; Kwon, Soo-Jung; Kim, Jin Sang; Lee, Gunbong; Maeng, Han-Joo; Lee, Jeongmi; Hwang, Gwi Seo; Cha, Hyuk-Jin; Chun, Kwang-Hoon

2018-05-01

Melanin is a pigment produced from tyrosine in melanocytes. Although melanin has a protective role against UVB radiation-induced damage, it is also associated with the development of melanoma and darker skin tone. Tyrosinase is a key enzyme in melanin synthesis, which regulates the rate-limiting step during conversion of tyrosine into DOPA and dopaquinone. To develop effective RNA interference therapeutics, we designed a melanin siRNA pool by applying multiple prediction programs to reduce human tyrosinase levels. First, 272 siRNAs passed the target accessibility evaluation using the RNAxs program. Then we selected 34 siRNA sequences with ΔG ≥-34.6 kcal/mol, i-Score value ≥65, and siRNA scales score ≤30. siRNAs were designed as 19-bp RNA duplexes with an asymmetric 3' overhang at the 3' end of the antisense strand. We tested if these siRNAs effectively reduced tyrosinase gene expression using qRT-PCR and found that 17 siRNA sequences were more effective than commercially available siRNA. Three siRNAs further tested showed an effective visual color change in MNT-1 human cells without cytotoxic effects, indicating these sequences are anti-melanogenic. Our study revealed that human tyrosinase siRNAs could be efficiently designed using multiple prediction algorithms.

Designing Tyrosinase siRNAs by Multiple Prediction Algorithms and Evaluation of Their Anti-Melanogenic Effects

PubMed Central

Kwon, Ok-Seon; Kwon, Soo-Jung; Kim, Jin Sang; Lee, Gunbong; Maeng, Han-Joo; Lee, Jeongmi; Hwang, Gwi Seo; Cha, Hyuk-Jin; Chun, Kwang-Hoon

2018-01-01

Melanin is a pigment produced from tyrosine in melanocytes. Although melanin has a protective role against UVB radiation-induced damage, it is also associated with the development of melanoma and darker skin tone. Tyrosinase is a key enzyme in melanin synthesis, which regulates the rate-limiting step during conversion of tyrosine into DOPA and dopaquinone. To develop effective RNA interference therapeutics, we designed a melanin siRNA pool by applying multiple prediction programs to reduce human tyrosinase levels. First, 272 siRNAs passed the target accessibility evaluation using the RNAxs program. Then we selected 34 siRNA sequences with ΔG ≥−34.6 kcal/mol, i-Score value ≥65, and siRNA scales score ≤30. siRNAs were designed as 19-bp RNA duplexes with an asymmetric 3′ overhang at the 3′ end of the antisense strand. We tested if these siRNAs effectively reduced tyrosinase gene expression using qRT-PCR and found that 17 siRNA sequences were more effective than commercially available siRNA. Three siRNAs further tested showed an effective visual color change in MNT-1 human cells without cytotoxic effects, indicating these sequences are anti-melanogenic. Our study revealed that human tyrosinase siRNAs could be efficiently designed using multiple prediction algorithms. PMID:29223142

Radar cross sections of standard and complex shape targets

NASA Technical Reports Server (NTRS)

Sohel, M. S.

1974-01-01

The theoretical, analytical, and experimental results are described for radar cross sections (RCS) of different-shaped targets. Various techniques for predicting RCS are given, and RCS of finite standard targets are presented. Techniques used to predict the RCS of complex targets are made, and the RCS complex shapes are provided.

Targeted resequencing of candidate genes reveals novel variants associated with severe Behçet's uveitis.

PubMed

Kim, Sang Jin; Lee, Seungbok; Park, Changho; Seo, Jeong-Sun; Kim, Jong-Il; Yu, Hyeong Gon

2013-10-18

Behçet's disease (BD) is a chronic systemic inflammatory disorder characterized by four major manifestations: recurrent uveitis, oral and genital ulcers and skin lesions. To identify some pathogenic variants associated with severe Behçet's uveitis, we used targeted and massively parallel sequencing methods to explore the genetic diversity of target regions. A solution-based target enrichment kit was designed to capture whole-exonic regions of 132 candidate genes. Using a multiplexing strategy, 32 samples from patients with a severe type of Behçet's uveitis were sequenced with a Genome Analyzer IIx. We compared the frequency of each variant with that of 59 normal Korean controls, and selected five rare and eight common single-nucleotide variants as the candidates for a replication study. The selected variants were genotyped in 61 cases and 320 controls and, as a result, two rare and seven common variants showed significant associations with severe Behçet's uveitis (P<0.05). Some of these, including rs199955684 in KIR3DL3, rs1801133 in MTHFR, rs1051790 in MICA and rs1051456 in KIR2DL4, were predicted to be damaging by either the PolyPhen-2 or SIFT prediction program. Variants on FCGR3A (rs396991) and ICAM1 (rs5498) have been previously reported as susceptibility loci of this disease, and those on IFNAR1, MTFHR and MICA also replicated the previous reports at the gene level. The KIR3DL3 and KIR2DL4 genes are novel susceptibility genes that have not been reported in association with BD. In conclusion, this study showed that target enrichment and next-generation sequencing technologies can provide valuable information on the genetic predisposition for Behçet's uveitis.

A Component-Centered Meta-Analysis of Family-Based Prevention Programs for Adolescent Substance Use

PubMed Central

Roseth, Cary J.; Fosco, Gregory M.; Lee, You-kyung; Chen, I-Chien

2016-01-01

Although research has documented the positive effects of family-based prevention programs, the field lacks specific information regarding why these programs are effective. The current study summarized the effects of family-based programs on adolescent substance use using a component-based approach to meta-analysis in which we decomposed programs into a set of key topics or components that were specifically addressed by program curricula (e.g., parental monitoring/behavior management, problem solving, positive family relations, etc.). Components were coded according to the amount of time spent on program services that targeted youth, parents, and the whole family; we also coded effect sizes across studies for each substance-related outcome. Given the nested nature of the data, we used hierarchical linear modeling to link program components (Level 2) with effect sizes (Level 1). The overall effect size across programs was .31, which did not differ by type of substance. Youth-focused components designed to encourage more positive family relationships and a positive orientation toward the future emerged as key factors predicting larger than average effect sizes. Our results suggest that, within the universe of family-based prevention, where components such as parental monitoring/behavior management are almost universal, adding or expanding certain youth-focused components may be able to enhance program efficacy. PMID:27064553

Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10.

PubMed

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V; Craig, Timothy K; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C; van Raaij, Mark J; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-02-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, more than 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this article, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict transmembrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin (IL)-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fiber protein gene product 17 from bacteriophage T7; the bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally, an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. Copyright © 2013 The Authors. Wiley Periodicals, Inc.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Measurement of functional capacity requirements of police officers to aid in development of an occupation-specific cardiac rehabilitation training program.

PubMed

Adams, Jenny; Schneider, Jonna; Hubbard, Matthew; McCullough-Shock, Tiffany; Cheng, Dunlei; Simms, Kay; Hartman, Julie; Hinton, Paul; Strauss, Danielle

2010-01-01

This study was designed to measure the functional capacity of healthy subjects during strenuous simulated police tasks, with the goal of developing occupation-specific training for cardiac rehabilitation of police officers. A calibrated metabolic instrument and an oxygen consumption data collection mask were used to measure the oxygen consumption and heart rates of 30 Dallas Police Academy officers and cadets as they completed an 8-event obstacle course that simulated chasing, subduing, and handcuffing a suspect. Standard target heart rates (85% of age-predicted maximum heart rate, or 0.85 x [220 - age]) and metabolic equivalents (METs) were calculated; a matched-sample t test based on differences between target and achieved heart rate and MET level was used for statistical analysis. Peak heart rates during the obstacle course simulation were significantly higher than the standard target heart rates (those at which treadmill stress tests in physicians' offices are typically stopped) (t(29) = 12.81, P < 0.001) and significantly higher than the suggested maximum of 150 beats/min during cardiac rehabilitation training (t(29) = 17.84, P < 0.001). Peak MET levels during the obstacle course simulation were also significantly higher than the goal level (8 METs) that patients typically achieve in a cardiac rehabilitation program (t(29) = 14.73, P < 0.001). We conclude that police work requires a functional capacity greater than that typically attained in traditional cardiac rehabilitation programs. Rehabilitation professionals should consider performing maximal stress tests and increasing the intensity of cardiac rehabilitation workouts to effectively train police officers who have had a cardiac event.

Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators

USGS Publications Warehouse

Ginsberg, Howard; Bargar, Timothy A.; Hladik, Michelle L.; Lubelczyk, Charles

2017-01-01

Tick and mosquito management is important to public health protection. At the same time, growing concerns about declines of pollinator species raise the question of whether vector control practices might affect pollinator populations. We report the results of a task force of the North American Pollinator Protection Campaign (NAPPC) that examined potential effects of vector management practices on pollinators, and how these programs could be adjusted to minimize negative effects on pollinating species. The main types of vector control practices that might affect pollinators are landscape manipulation, biocontrol, and pesticide applications. Some current practices already minimize effects of vector control on pollinators (e.g., short-lived pesticides and application-targeting technologies). Nontarget effects can be further diminished by taking pollinator protection into account in the planning stages of vector management programs. Effects of vector control on pollinator species often depend on specific local conditions (e.g., proximity of locations with abundant vectors to concentrations of floral resources), so planning is most effective when it includes collaborations of local vector management professionals with local experts on pollinators. Interventions can then be designed to avoid pollinators (e.g., targeting applications to avoid blooming times and pollinator nesting habitats), while still optimizing public health protection. Research on efficient targeting of interventions, and on effects on pollinators of emerging technologies, will help mitigate potential deleterious effects on pollinators in future management programs. In particular, models that can predict effects of integrated pest management on vector-borne pathogen transmission, along with effects on pollinator populations, would be useful for collaborative decision-making.

The PEREGRINETM program: using physics and computer simulation to improve radiation therapy for cancer

NASA Astrophysics Data System (ADS)

Hartmann Siantar, Christine L.; Moses, Edward I.

1998-11-01

When using radiation to treat cancer, doctors rely on physics and computer technology to predict where the radiation dose will be deposited in the patient. The accuracy of computerized treatment planning plays a critical role in the ultimate success or failure of the radiation treatment. Inaccurate dose calculations can result in either insufficient radiation for cure, or excessive radiation to nearby healthy tissue, which can reduce the patient's quality of life. This paper describes how advanced physics, computer, and engineering techniques originally developed for nuclear weapons and high-energy physics research are being used to predict radiation dose in cancer patients. Results for radiation therapy planning, achieved in the Lawrence Livermore National Laboratory (LLNL) 0143-0807/19/6/005/img2 program show that these tools can give doctors new insights into their patients' treatments by providing substantially more accurate dose distributions than have been available in the past. It is believed that greater accuracy in radiation therapy treatment planning will save lives by improving doctors' ability to target radiation to the tumour and reduce suffering by reducing the incidence of radiation-induced complications.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

PubMed Central

Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Airola, Antti; Wennerberg, Krister

2017-01-01

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications. PMID:28787438

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

PubMed Central

Lemmens, Karen; De Bie, Tijl; Dhollander, Thomas; De Keersmaecker, Sigrid C; Thijs, Inge M; Schoofs, Geert; De Weerdt, Ami; De Moor, Bart; Vanderleyden, Jos; Collado-Vides, Julio; Engelen, Kristof; Marchal, Kathleen

2009-01-01

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity. PMID:19265557

Evaluating a Targeted Social Program When Placement Is Decentralized. Policy Research Working Papers No. 1945.

ERIC Educational Resources Information Center

Ravallion, Martin; Wodon, Quentin

Assessment of welfare gains from a targeted social program can be seriously biased unless the endogeneity of program participation is addressed. Bias comes from two sources of placement endogeneity: the purposive targeting of geographic areas, and the targeting of individual recipients within selected areas. Partial decentralization of program…

Dynamic visual acuity using "far" and "near" targets

NASA Technical Reports Server (NTRS)

Peters, Brian T.; Bloomberg, Jacob J.

2005-01-01

CONCLUSIONS: DVA may be useful for assessing the functional consequences of an impaired gaze stabilization mechanism or for testing the effectiveness of a rehabilitation paradigm. Because target distance influences the relative contributions of canal and otolith inputs, the ability to measure DVA at near and far viewing distances may also lead to tests that will independently assess canal and otolith function. OBJECTIVE: To present and test a methodology that uses dynamic visual acuity (DVA) to assess the efficacy of compensatory gaze mechanisms during a functionally relevant activity that differentially measures canal and otolith function. MATERIAL AND METHODS: The effect of treadmill walking at a velocity of 1.79 m/s on subjects' visual acuity was assessed at each of two viewing distances. A custom-written threshold determination program was used to display Landolt C optotypes on a laptop computer screen during a "far" (4 m) target condition and on a micro-display for a "near" (50 cm) target condition. The walking acuity scores for each target distance were normalized by subtracting a corresponding acuity measure obtained while standing still on the treadmill belt. RESULTS: As predicted by subjective reports of relative target motion, the decrease in visual acuity was significantly greater (p < 0.00001) for the near compared to the far condition.

Identification of proteins in Streptococcus pneumoniae by reverse vaccinology and genetic diversity of these proteins in clinical isolates.

PubMed

Argondizzo, Ana Paula Corrêa; da Mota, Fabio Faria; Pestana, Cristiane Pinheiro; Reis, Joice Neves; de Miranda, Antonio Basílio; Galler, Ricardo; Medeiros, Marco Alberto

2015-02-01

Streptococcus pneumoniae is a major cause of morbidity and mortality worldwide. Virulence-associated proteins common and conserved among all capsular types now represent the best strategy to combat pneumococcal infections. Our aim was to identify conserved targets in pneumococci that showed positive prediction for lipoprotein and extracellular subcellular location using bioinformatics programs and verify the distribution and the degree of conservation of these targets in pneumococci. These targets can be considered potential vaccine candidate to be evaluated in the future. A set of 13 targets were analyzed and confirmed the presence in all pneumococci tested. These 13 genes were highly conserved showing around >96 % of amino acid and nucleotide identity, but they were also present and show high identity in the closely related species Streptococcus mitis, Streptococcus oralis, and Streptococcus pseudopneumoniae. S. oralis clusters away from S. pneumoniae, while S. pseudopneumoniae and S. mitis cluster closer. The divergence between the selected targets was too small to be observed consistently in phylogenetic groups between the analyzed genomes of S. pneumoniae. The proteins analyzed fulfill two of the initial criteria of a vaccine candidate: targets are present in a variety of different pneumococci strains including different serotypes and are conserved among the samples evaluated.

Vision and Vestibular System Dysfunction Predicts Prolonged Concussion Recovery in Children.

PubMed

Master, Christina L; Master, Stephen R; Wiebe, Douglas J; Storey, Eileen P; Lockyer, Julia E; Podolak, Olivia E; Grady, Matthew F

2018-03-01

Up to one-third of children with concussion have prolonged symptoms lasting beyond 4 weeks. Vision and vestibular dysfunction is common after concussion. It is unknown whether such dysfunction predicts prolonged recovery. We sought to determine which vision or vestibular problems predict prolonged recovery in children. A retrospective cohort of pediatric patients with concussion. A subspecialty pediatric concussion program. Four hundred thirty-two patient records were abstracted. Presence of vision or vestibular dysfunction upon presentation to the subspecialty concussion program. The main outcome of interest was time to clinical recovery, defined by discharge from clinical follow-up, including resolution of acute symptoms, resumption of normal physical and cognitive activity, and normalization of physical examination findings to functional levels. Study subjects were 5 to 18 years (median = 14). A total of 378 of 432 subjects (88%) presented with vision or vestibular problems. A history of motion sickness was associated with vestibular dysfunction. Younger age, public insurance, and presence of headache were associated with later presentation for subspecialty concussion care. Vision and vestibular problems were associated within distinct clusters. Provocable symptoms with vestibulo-ocular reflex (VOR) and smooth pursuits and abnormal balance and accommodative amplitude (AA) predicted prolonged recovery time. Vision and vestibular problems predict prolonged concussion recovery in children. A history of motion sickness may be an important premorbid factor. Public insurance status may represent problems with disparities in access to concussion care. Vision assessments in concussion must include smooth pursuits, saccades, near point of convergence (NPC), and accommodative amplitude (AA). A comprehensive, multidomain assessment is essential to predict prolonged recovery time and enable active intervention with specific school accommodations and targeted rehabilitation.

microPIR2: a comprehensive database for human–mouse comparative study of microRNA–promoter interactions

PubMed Central

Piriyapongsa, Jittima; Bootchai, Chaiwat; Ngamphiw, Chumpol; Tongsima, Sissades

2014-01-01

microRNA (miRNA)–promoter interaction resource (microPIR) is a public database containing over 15 million predicted miRNA target sites located within human promoter sequences. These predicted targets are presented along with their related genomic and experimental data, making the microPIR database the most comprehensive repository of miRNA promoter target sites. Here, we describe major updates of the microPIR database including new target predictions in the mouse genome and revised human target predictions. The updated database (microPIR2) now provides ∼80 million human and 40 million mouse predicted target sites. In addition to being a reference database, microPIR2 is a tool for comparative analysis of target sites on the promoters of human–mouse orthologous genes. In particular, this new feature was designed to identify potential miRNA–promoter interactions conserved between species that could be stronger candidates for further experimental validation. We also incorporated additional supporting information to microPIR2 such as nuclear and cytoplasmic localization of miRNAs and miRNA–disease association. Extra search features were also implemented to enable various investigations of targets of interest. Database URL: http://www4a.biotec.or.th/micropir2 PMID:25425035

Can typical US home visits affect infant attachment? Preliminary findings from a randomized trial of Healthy Families Durham.

PubMed

Berlin, Lisa J; Martoccio, Tiffany L; Appleyard Carmody, Karen; Goodman, W Benjamin; O'Donnell, Karen; Williams, Janis; Murphy, Robert A; Dodge, Kenneth A

2017-12-01

US government-funded early home visiting services are expanding significantly. The most widely implemented home visiting models target at-risk new mothers and their infants. Such home visiting programs typically aim to support infant-parent relationships; yet, such programs' effects on infant attachment quality per se are as yet untested. Given these programs' aims, and the crucial role of early attachments in human development, it is important to understand attachment processes in home visited families. The current, preliminary study examined 94 high-risk mother-infant dyads participating in a randomized evaluation of the Healthy Families Durham (HFD) home visiting program. We tested (a) infant attachment security and disorganization as predictors of toddler behavior problems and (b) program effects on attachment security and disorganization. We found that (a) infant attachment disorganization (but not security) predicted toddler behavior problems and (b) participation in HFD did not significantly affect infant attachment security or disorganization. Findings are discussed in terms of the potential for attachment-specific interventions to enhance the typical array of home visiting services.

High-order graph matching based feature selection for Alzheimer's disease identification.

PubMed

Liu, Feng; Suk, Heung-Il; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

2013-01-01

One of the main limitations of l1-norm feature selection is that it focuses on estimating the target vector for each sample individually without considering relations with other samples. However, it's believed that the geometrical relation among target vectors in the training set may provide useful information, and it would be natural to expect that the predicted vectors have similar geometric relations as the target vectors. To overcome these limitations, we formulate this as a graph-matching feature selection problem between a predicted graph and a target graph. In the predicted graph a node is represented by predicted vector that may describe regional gray matter volume or cortical thickness features, and in the target graph a node is represented by target vector that include class label and clinical scores. In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. Using the ADNI dataset, we evaluate the effectiveness of the proposed method and obtain the accuracies of 92.17% and 81.57% in AD and MCI classification, respectively.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

Validating regulatory predictions from diverse bacteria with mutant fitness data

DOE PAGES

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.; ...

2017-05-24

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Validating regulatory predictions from diverse bacteria with mutant fitness data

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

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task.

PubMed

Diaz, Gabriel; Cooper, Joseph; Rothkopf, Constantin; Hayhoe, Mary

2013-01-16

Despite general agreement that prediction is a central aspect of perception, there is relatively little evidence concerning the basis on which visual predictions are made. Although both saccadic and pursuit eye-movements reveal knowledge of the future position of a moving visual target, in many of these studies targets move along simple trajectories through a fronto-parallel plane. Here, using a naturalistic and racquet-based interception task in a virtual environment, we demonstrate that subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions and the head and body are unrestrained. Furthermore, we found that, following a change in ball elasticity, subjects were able to accurately adjust their prebounce predictions of the ball's post-bounce trajectory. This suggests that prediction is guided by experience-based models of how information in the visual image will change over time.

Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task

PubMed Central

Diaz, Gabriel; Cooper, Joseph; Rothkopf, Constantin; Hayhoe, Mary

2013-01-01

Despite general agreement that prediction is a central aspect of perception, there is relatively little evidence concerning the basis on which visual predictions are made. Although both saccadic and pursuit eye-movements reveal knowledge of the future position of a moving visual target, in many of these studies targets move along simple trajectories through a fronto-parallel plane. Here, using a naturalistic and racquet-based interception task in a virtual environment, we demonstrate that subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions and the head and body are unrestrained. Furthermore, we found that, following a change in ball elasticity, subjects were able to accurately adjust their prebounce predictions of the ball's post-bounce trajectory. This suggests that prediction is guided by experience-based models of how information in the visual image will change over time. PMID:23325347

Perceived coercion in inpatients with Anorexia nervosa: Associations with illness severity and hospital course.

PubMed

Schreyer, Colleen C; Coughlin, Janelle W; Makhzoumi, Saniha H; Redgrave, Graham W; Hansen, Jennifer L; Guarda, Angela S

2016-04-01

The use of coercion in the treatment for anorexia nervosa (AN) is controversial and the limited studies to date have focused on involuntary treatment. However, coercive pressure for treatment that does not include legal measures is common in voluntarily admitted patients with AN. Empirical data examining the effect of non-legal forms of coerced care on hospital outcomes are needed. Participants (N = 202) with AN, Avoidant/Restrictive Food Intake Disorder (ARFID), or subthreshold AN admitted to a hospital-based behavioral specialty program completed questionnaires assessing illness severity and perceived coercion around the admissions process. Hospital course variables included inpatient length of stay, successful transition to a step-down partial hospitalization program, and achievement of target weight prior to program discharge. Higher perceived coercion at admission was associated with increased drive for thinness and body dissatisfaction, but not with admission BMI. Perceived coercion was not related to inpatient length of stay, rate of weight gain, or achievement of target weight although it was predictive of premature drop-out prior to transition to an integrated partial hospitalization program. These results, from an adequately powered sample, demonstrate that perceived coercion at admission to a hospital-based behavioral treatment program was not associated with rate of inpatient weight gain or achieving weight restoration, suggesting that coercive pressure to enter treatment does not necessarily undermine formation of a therapeutic alliance or clinical progress. Future studies should examine perceived coercion and long-term outcomes, patient views on coercive pressures, and the effect of different forms of leveraged treatment. © 2015 Wiley Periodicals, Inc.

An antiviral RISC isolated from Tobacco rattle virus-infected plants.

PubMed

Ciomperlik, Jessica J; Omarov, Rustem T; Scholthof, Herman B

2011-03-30

The RNAi model predicts that during antiviral defense a RNA-induced silencing complex (RISC) is programmed with viral short-interfering RNAs (siRNAs) to target the cognate viral RNA for degradation. We show that infection of Nicotiana benthamiana with Tobacco rattle virus (TRV) activates an antiviral nuclease that specifically cleaves TRV RNA in vitro. In agreement with known RISC properties, the nuclease activity was inhibited by NaCl and EDTA and stimulated by divalent metal cations; a novel property was its preferential targeting of elongated RNA molecules. Intriguingly, the specificity of the TRV RISC could be reprogrammed by exogenous addition of RNA (containing siRNAs) from plants infected with an unrelated virus, resulting in a newly acquired ability of RISC to target this heterologous genome in vitro. Evidently the virus-specific nuclease complex from N. benthamiana represents a genuine RISC that functions as a readily employable and reprogrammable antiviral defense unit. Copyright © 2011 Elsevier Inc. All rights reserved.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

PubMed Central

Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

2007-01-01

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

PubMed Central

Liu, Jie; Lee, Wen-hui; Zhang, Yun

2014-01-01

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis.

PubMed

Kleinstreuer, Nicole C; Dix, David J; Houck, Keith A; Kavlock, Robert J; Knudsen, Thomas B; Martin, Matthew T; Paul, Katie B; Reif, David M; Crofton, Kevin M; Hamilton, Kerry; Hunter, Ronald; Shah, Imran; Judson, Richard S

2013-01-01

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted testing and functional validation of cancer pathways.

DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

2017-03-24

Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides. Five different sets of feature viz., amino acid composition (AAC), di-peptide composition (DPC), pseudo amino acid composition (PAAC), composition-transition-distribution (CTD) and auto-correlation function (ACF) were used to map the protein sequences into numeric feature vectors. The encoded numeric vectors were then used as input in support vector machine (SVM) for classification of insecticide resistant and non-resistant proteins. Higher accuracies were obtained under RBF kernel than that of other kernels. Further, accuracies were observed to be higher for DPC feature set as compared to others. The proposed approach achieved an overall accuracy of >90% in discriminating resistant from non-resistant proteins. Further, the two classes of resistant proteins i.e., detoxification-based and target-based were discriminated from non-resistant proteins with >95% accuracy. Besides, >95% accuracy was also observed for discrimination of proteins involved in detoxification- and target-based resistance mechanisms. The proposed approach not only outperformed Blastp, PSI-Blast and Delta-Blast algorithms, but also achieved >92% accuracy while assessed using an independent dataset of 75 insecticide resistant proteins. This paper presents the first computational approach for discriminating the insecticide resistant proteins from non-resistant proteins. Based on the proposed approach, an online prediction server DIRProt has also been developed for computational prediction of insecticide resistant proteins, which is accessible at http://cabgrid.res.in:8080/dirprot/ . The proposed approach is believed to supplement the efforts needed to develop dynamic insecticides in wet-lab by targeting the insecticide resistant proteins.

Optimal Battery Utilization Over Lifetime for Parallel Hybrid Electric Vehicle to Maximize Fuel Economy

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

Patil, Chinmaya; Naghshtabrizi, Payam; Verma, Rajeev

This paper presents a control strategy to maximize fuel economy of a parallel hybrid electric vehicle over a target life of the battery. Many approaches to maximizing fuel economy of parallel hybrid electric vehicle do not consider the effect of control strategy on the life of the battery. This leads to an oversized and underutilized battery. There is a trade-off between how aggressively to use and 'consume' the battery versus to use the engine and consume fuel. The proposed approach addresses this trade-off by exploiting the differences in the fast dynamics of vehicle power management and slow dynamics of batterymore » aging. The control strategy is separated into two parts, (1) Predictive Battery Management (PBM), and (2) Predictive Power Management (PPM). PBM is the higher level control with slow update rate, e.g. once per month, responsible for generating optimal set points for PPM. The considered set points in this paper are the battery power limits and State Of Charge (SOC). The problem of finding the optimal set points over the target battery life that minimize engine fuel consumption is solved using dynamic programming. PPM is the lower level control with high update rate, e.g. a second, responsible for generating the optimal HEV energy management controls and is implemented using model predictive control approach. The PPM objective is to find the engine and battery power commands to achieve the best fuel economy given the battery power and SOC constraints imposed by PBM. Simulation results with a medium duty commercial hybrid electric vehicle and the proposed two-level hierarchical control strategy show that the HEV fuel economy is maximized while meeting a specified target battery life. On the other hand, the optimal unconstrained control strategy achieves marginally higher fuel economy, but fails to meet the target battery life.« less

Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

PubMed Central

Coronnello, Claudia; Hartmaier, Ryan; Arora, Arshi; Huleihel, Luai; Pandit, Kusum V.; Bais, Abha S.; Butterworth, Michael; Kaminski, Naftali; Stormo, Gary D.; Oesterreich, Steffi; Benos, Panayiotis V.

2012-01-01

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies. PMID:23284279

Detection of visual events along the apparent motion trace in patients with paranoid schizophrenia.

PubMed

Sanders, Lia Lira Olivier; Muckli, Lars; de Millas, Walter; Lautenschlager, Marion; Heinz, Andreas; Kathmann, Norbert; Sterzer, Philipp

2012-07-30

Dysfunctional prediction in sensory processing has been suggested as a possible causal mechanism in the development of delusions in patients with schizophrenia. Previous studies in healthy subjects have shown that while the perception of apparent motion can mask visual events along the illusory motion trace, such motion masking is reduced when events are spatio-temporally compatible with the illusion, and, therefore, predictable. Here we tested the hypothesis that this specific detection advantage for predictable target stimuli on the apparent motion trace is reduced in patients with paranoid schizophrenia. Our data show that, although target detection along the illusory motion trace is generally impaired, both patients and healthy control participants detect predictable targets more often than unpredictable targets. Patients had a stronger motion masking effect when compared to controls. However, patients showed the same advantage in the detection of predictable targets as healthy control subjects. Our findings reveal stronger motion masking but intact prediction of visual events along the apparent motion trace in patients with paranoid schizophrenia and suggest that the sensory prediction mechanism underlying apparent motion is not impaired in paranoid schizophrenia. Copyright © 2012. Published by Elsevier Ireland Ltd.

Individual Characteristics, Family Factors, and Classroom Experiences as Predictors of Low-Income Kindergarteners' Social Skills.

PubMed

Griffith, Shayl; Arnold, David; Voegler-Lee, Mary-Ellen; Kupersmidt, Janis

2016-01-01

There has been increasing awareness of the need for research and theory to take into account the intersection of individual characteristics and environmental contexts when examining predictors of child outcomes. The present longitudinal, multi-informant study examined the cumulative and interacting contributions of child characteristics (language skills, inattention/hyperactivity, and aggression) and preschool and family contextual factors in predicting kindergarten social skills in 389 low-income preschool children. Child characteristics and classroom factors, but not family factors, predicted teacher-rated kindergarten social skills, while child characteristics alone predicted change in teacher-rated social skills from preschool to kindergarten. Child characteristics and family factors, but not classroom factors, predicted parent-rated kindergarten social skills. Family factors alone predicted change in parent-rated social skills from preschool to kindergarten. Individual child characteristics did not interact with family or classroom factors in predicting parent- or teacher-rated social skills, and support was therefore found for an incremental, rather than an interactive, predictive model of social skills. The findings underscore the importance of assessing outcomes in more than one context, and of considering the impact of both individual and environmental contextual factors on children's developing social skills when designing targeted intervention programs to prepare children for kindergarten.

Individual Characteristics, Family Factors, and Classroom Experiences as Predictors of Low-Income Kindergarteners’ Social Skills

PubMed Central

Griffith, Shayl; Arnold, David; Voegler-Lee, Mary-Ellen; Kupersmidt, Janis

2017-01-01

There has been increasing awareness of the need for research and theory to take into account the intersection of individual characteristics and environmental contexts when examining predictors of child outcomes. The present longitudinal, multi-informant study examined the cumulative and interacting contributions of child characteristics (language skills, inattention/hyperactivity, and aggression) and preschool and family contextual factors in predicting kindergarten social skills in 389 low-income preschool children. Child characteristics and classroom factors, but not family factors, predicted teacher-rated kindergarten social skills, while child characteristics alone predicted change in teacher-rated social skills from preschool to kindergarten. Child characteristics and family factors, but not classroom factors, predicted parent-rated kindergarten social skills. Family factors alone predicted change in parent-rated social skills from preschool to kindergarten. Individual child characteristics did not interact with family or classroom factors in predicting parent- or teacher-rated social skills, and support was therefore found for an incremental, rather than an interactive, predictive model of social skills. The findings underscore the importance of assessing outcomes in more than one context, and of considering the impact of both individual and environmental contextual factors on children’s developing social skills when designing targeted intervention programs to prepare children for kindergarten. PMID:28804528

Investigating the role of the superior colliculus in active vision with the visual search paradigm.

PubMed

Shen, Kelly; Valero, Jerome; Day, Gregory S; Paré, Martin

2011-06-01

We review here both the evidence that the functional visuomotor organization of the optic tectum is conserved in the primate superior colliculus (SC) and the evidence for the linking proposition that SC discriminating activity instantiates saccade target selection. We also present new data in response to questions that arose from recent SC visual search studies. First, we observed that SC discriminating activity predicts saccade initiation when monkeys perform an unconstrained search for a target defined by either a single visual feature or a conjunction of two features. Quantitative differences between the results in these two search tasks suggest, however, that SC discriminating activity does not only reflect saccade programming. This finding concurs with visual search studies conducted in posterior parietal cortex and the idea that, during natural active vision, visual attention is shifted concomitantly with saccade programming. Second, the analysis of a large neuronal sample recorded during feature search revealed that visual neurons in the superficial layers do possess discriminating activity. In addition, the hypotheses that there are distinct types of SC neurons in the deeper layers and that they are differently involved in saccade target selection were not substantiated. Third, we found that the discriminating quality of single-neuron activity substantially surpasses the ability of the monkeys to discriminate the target from distracters, raising the possibility that saccade target selection is a noisy process. We discuss these new findings in light of the visual search literature and the view that the SC is a visual salience map for orienting eye movements. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3' UTRs and coding sequences.

PubMed

Šulc, Miroslav; Marín, Ray M; Robins, Harlan S; Vaníček, Jiří

2015-07-01

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3' untranslated regions (3' UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3' UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA-mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA-mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

PubMed Central

Schmitz, Ulf; Lai, Xin; Winter, Felix; Wolkenhauer, Olaf; Vera, Julio; Gupta, Shailendra K.

2014-01-01

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/. PMID:24875477

Expect the unexpected: a paradoxical effect of cue validity on the orienting of attention.

PubMed

Jollie, Ashley; Ivanoff, Jason; Webb, Nicole E; Jamieson, Andrew S

2016-10-01

Predictive central cues generate location-based expectancies, voluntary shifts of attention, and facilitate target processing. Often, location-based expectancies and voluntary attention are confounded in cueing tasks. Here we vary the predictability of central cues to determine whether they can evoke the inhibition of target processing in three go/no-go experiments. In the first experiment, the central cue was uninformative and did not predict the target's location. Importantly, these cues did not seem to affect target processing. In the second experiment, the central cue indicated the most or the least likely location of the target. Surprisingly, both types of cues facilitated target processing at the cued location. In the third experiment, the central cue predicted the most likely location of a no-go target, but it did not provide relevant information pertaining to the location of the go target. Again, the central cue facilitated processing of the go target. These results suggest that efforts to strategically allocate inhibition may be thwarted by the paradoxical monitoring of the cued location. The current findings highlight the need to further explore the relationship between location-based expectancies and spatial attention in cueing tasks.

Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma

PubMed Central

Levine, Douglas A.; Mankoo, Parminder; Schultz, Nikolaus; Du, Ying; Zhang, Yiqun; Larsson, Erik; Sheridan, Robert; Xiao, Weimin; Spellman, Paul T.; Getz, Gad; Wheeler, David A.; Perou, Charles M.; Gibbs, Richard A.; Sander, Chris; Hayes, D. Neil; Gunaratne, Preethi H.

2012-01-01

Background The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets. Methods and Results Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3′-untranslated region (with less frequency observed for coding sequence and 5′-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability. Conclusions This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers. PMID:22479643

Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment

PubMed Central

Velankar, Sameer; Kryshtafovych, Andriy; Huang, Shen‐You; Schneidman‐Duhovny, Dina; Sali, Andrej; Segura, Joan; Fernandez‐Fuentes, Narcis; Viswanath, Shruthi; Elber, Ron; Grudinin, Sergei; Popov, Petr; Neveu, Emilie; Lee, Hasup; Baek, Minkyung; Park, Sangwoo; Heo, Lim; Rie Lee, Gyu; Seok, Chaok; Qin, Sanbo; Zhou, Huan‐Xiang; Ritchie, David W.; Maigret, Bernard; Devignes, Marie‐Dominique; Ghoorah, Anisah; Torchala, Mieczyslaw; Chaleil, Raphaël A.G.; Bates, Paul A.; Ben‐Zeev, Efrat; Eisenstein, Miriam; Negi, Surendra S.; Weng, Zhiping; Vreven, Thom; Pierce, Brian G.; Borrman, Tyler M.; Yu, Jinchao; Ochsenbein, Françoise; Guerois, Raphaël; Vangone, Anna; Rodrigues, João P.G.L.M.; van Zundert, Gydo; Nellen, Mehdi; Xue, Li; Karaca, Ezgi; Melquiond, Adrien S.J.; Visscher, Koen; Kastritis, Panagiotis L.; Bonvin, Alexandre M.J.J.; Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Li, Jilong; Ma, Zhiwei; Cheng, Jianlin; Zou, Xiaoqin; Shen, Yang; Peterson, Lenna X.; Kim, Hyung‐Rae; Roy, Amit; Han, Xusi; Esquivel‐Rodriguez, Juan; Kihara, Daisuke; Yu, Xiaofeng; Bruce, Neil J.; Fuller, Jonathan C.; Wade, Rebecca C.; Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.; Imai, Kenichiro; Yamada, Kazunori; Oda, Toshiyuki; Nakamura, Tsukasa; Tomii, Kentaro; Pallara, Chiara; Romero‐Durana, Miguel; Jiménez‐García, Brian; Moal, Iain H.; Férnandez‐Recio, Juan; Joung, Jong Young; Kim, Jong Yun; Joo, Keehyoung; Lee, Jooyoung; Kozakov, Dima; Vajda, Sandor; Mottarella, Scott; Hall, David R.; Beglov, Dmitri; Mamonov, Artem; Xia, Bing; Bohnuud, Tanggis; Del Carpio, Carlos A.; Ichiishi, Eichiro; Marze, Nicholas; Kuroda, Daisuke; Roy Burman, Shourya S.; Gray, Jeffrey J.; Chermak, Edrisse; Cavallo, Luigi; Oliva, Romina; Tovchigrechko, Andrey

2016-01-01

ABSTRACT We present the results for CAPRI Round 30, the first joint CASP‐CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact‐sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology‐built subunit models and the smaller pair‐wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323–348. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:27122118

Whole-Genome Thermodynamic Analysis Reduces siRNA Off-Target Effects

PubMed Central

Chen, Xi; Liu, Peng; Chou, Hui-Hsien

2013-01-01

Small interfering RNAs (siRNAs) are important tools for knocking down targeted genes, and have been widely applied to biological and biomedical research. To design siRNAs, two important aspects must be considered: the potency in knocking down target genes and the off-target effect on any nontarget genes. Although many studies have produced useful tools to design potent siRNAs, off-target prevention has mostly been delegated to sequence-level alignment tools such as BLAST. We hypothesize that whole-genome thermodynamic analysis can identify potential off-targets with higher precision and help us avoid siRNAs that may have strong off-target effects. To validate this hypothesis, two siRNA sets were designed to target three human genes IDH1, ITPR2 and TRIM28. They were selected from the output of two popular siRNA design tools, siDirect and siDesign. Both siRNA design tools have incorporated sequence-level screening to avoid off-targets, thus their output is believed to be optimal. However, one of the sets we tested has off-target genes predicted by Picky, a whole-genome thermodynamic analysis tool. Picky can identify off-target genes that may hybridize to a siRNA within a user-specified melting temperature range. Our experiments validated that some off-target genes predicted by Picky can indeed be inhibited by siRNAs. Similar experiments were performed using commercially available siRNAs and a few off-target genes were also found to be inhibited as predicted by Picky. In summary, we demonstrate that whole-genome thermodynamic analysis can identify off-target genes that are missed in sequence-level screening. Because Picky prediction is deterministic according to thermodynamics, if a siRNA candidate has no Picky predicted off-targets, it is unlikely to cause off-target effects. Therefore, we recommend including Picky as an additional screening step in siRNA design. PMID:23484018

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

PubMed Central

Cao, Dong-Sheng; Liang, Yi-Zeng; Deng, Zhe; Hu, Qian-Nan; He, Min; Xu, Qing-Song; Zhou, Guang-Hua; Zhang, Liu-Xia; Deng, Zi-xin; Liu, Shao

2013-01-01

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki. PMID:23577055

The Predicted Growth of the Low Earth Orbit Space Debris Environment: An Assessment of Future Risk for Spacecraft

NASA Technical Reports Server (NTRS)

Krisko, Paula H.

2007-01-01

Space debris is a worldwide-recognized issue concerning the safety of commercial, military, and exploration spacecraft. The space debris environment includes both naturally occuring meteoroids and objects in Earth orbit that are generated by human activity, termed orbital debris. Space agencies around the world are addressing the dangers of debris collisions to both crewed and robotic spacecraft. In the United States, the Orbital Debris Program Office at the NASA Johnson Space Center leads the effort to categorize debris, predict its growth, and formulate mitigation policy for the environment from low Earth orbit (LEO) through geosynchronous orbit (GEO). This paper presents recent results derived from the NASA long-term debris environment model, LEGEND. It includes the revised NASA sodium potassium droplet model, newly corrected for a factor of two over-estimation of the droplet population. The study indicates a LEO environment that is already highly collisionally active among orbital debris larger than 1 cm in size. Most of the modeled collision events are non-catastrophic (i.e., They lead to a cratering of the target, but no large scale fragmentation.). But they are potentially mission-ending, and take place between impactors smaller than 10 cm and targets larger than 10 cm. Given the small size of the impactor these events would likely be undetectable by present-day measurement means. The activity continues into the future as would be expected. Impact rates of about four per year are predicted by the current study within the next 30 years, with the majority of targets being abandoned intacts (spent upper stages and spacecraft). Still, operational spacecraft do show a small collisional activity, one that increases over time as the small fragment population increases.

A program to measure new energetic particle nuclear interaction cross sections

NASA Astrophysics Data System (ADS)

Guzik, T. G.; Albergo, S.; Chen, C.-X.; Costa, S.; Crawford, H. J.; Engelage, J.; Ferrando, P.; Flores, I.; Greiner, L.; Jones, F. C.; Knott, C. N.; Ko, S.; Lindstrom, P. J.; Mazotta, J.; Mitchell, J. W.; Romanski, J.; Potenza, R.; Soutoul, A.; Testard, O.; Tull, C. E.; Tuve, C.; Waddington, C. J.; Webber, W. R.; Wefel, J. P.; Zhang, X.

1994-10-01

The Transport Collaboration, consisting of researchers from institutions in France, Germany, Italy, and the USA, has established a program to make new measurements of nuclear interaction cross sections for heavy projectiles (Z greater than or equal to 2) in targets of liquid H2, He and heavier materials. Such cross sections directly affect calculations of galactic and solar cosmic ray transport through matter and are needed for accurate radiation hazard assessment. To date, the collaboration has obtained data using the Lawrence Berkeley Laboratory Bevalac HISS facility with 20 projectiles from He-4 to Ni-58 in the energy range 393-910 MeV/nucleon. Preliminary results from the analysis of these data are presented here and compared to other measurements and to cross section prediction formulae.

A program to measure new energetic particle nuclear interaction cross sections

NASA Technical Reports Server (NTRS)

Guzik, T. G.; Albergo, S.; Chen, C. X.; Costa, S.; Crawford, H. J.; Engelage, J.; Ferrando, P.; Flores, I.; Greiner, L.; Jones, F. C.

1994-01-01

The Transport Collaboration, consisting of researchers from institutions in France, Germany, Italy, and the USA, has established a program to make new measurements of nuclear interaction cross sections for heavy projectiles (Z greater than or equal to 2) in targets of liquid H2, He and heavier materials. Such cross sections directly affect calculations of galactic and solar cosmic ray transport through matter and are needed for accurate radiation hazard assessment. To date, the collaboration has obtained data using the Lawrence Berkeley Laboratory Bevalac HISS facility with 20 projectiles from He-4 to Ni-58 in the energy range 393-910 MeV/nucleon. Preliminary results from the analysis of these data are presented here and compared to other measurements and to cross section prediction formulae.

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

NASA Technical Reports Server (NTRS)

Price, Kevin P.; Nellis, M. Duane

1996-01-01

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

AFIR: A Dimensionless Potency Metric for Characterizing the Activity of Monoclonal Antibodies

PubMed Central

Ramakrishna, R

2017-01-01

For monoclonal antibody (mAb) drugs, soluble targets may accumulate several thousand fold after binding to the drug. Time course data of mAb and total target is often collected and, although free target is more closely related to clinical effect, it is difficult to measure. Therefore, mathematical models of this data are used to predict target engagement. In this article, a “potency factor” is introduced as an approximation for the model‐predicted target inhibition. This potency factor is defined to be the time‐Averaged Free target concentration to Initial target concentration Ratio (AFIR), and it depends on three key quantities: the average drug concentration at steady state; the binding affinity; and the degree of target accumulation. AFIR provides the intuition for how changes in dosing regimen and binding affinity affect target capture and AFIR can be used to predict the druggability of new targets and the expected benefits of more potent, second‐generation mAbs. PMID:28375563

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

PubMed Central

Skinnider, Michael A.; Dejong, Chris A.; Rees, Philip N.; Johnston, Chad W.; Li, Haoxin; Webster, Andrew L. H.; Wyatt, Morgan A.; Magarvey, Nathan A.

2015-01-01

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/. PMID:26442528

Software for Dosage Individualization of Voriconazole for Immunocompromised Patients

PubMed Central

VanGuilder, Michael; Donnelly, J. Peter; Blijlevens, Nicole M. A.; Brüggemann, Roger J. M.; Jelliffe, Roger W.; Neely, Michael N.

2013-01-01

The efficacy of voriconazole is potentially compromised by considerable pharmacokinetic variability. There are increasing insights into voriconazole concentrations that are safe and effective for treatment of invasive fungal infections. Therapeutic drug monitoring is increasingly advocated. Software to aid in the individualization of dosing would be an extremely useful clinical tool. We developed software to enable the individualization of voriconazole dosing to attain predefined serum concentration targets. The process of individualized voriconazole therapy was based on concepts of Bayesian stochastic adaptive control. Multiple-model dosage design with feedback control was used to calculate dosages that achieved desired concentration targets with maximum precision. The performance of the software program was assessed using the data from 10 recipients of an allogeneic hematopoietic stem cell transplant (HSCT) receiving intravenous (i.v.) voriconazole. The program was able to model the plasma concentrations with a high level of precision, despite the wide range of concentration trajectories and interindividual pharmacokinetic variability. The voriconazole concentrations predicted after the last dosages were largely concordant with those actually measured. Simulations provided an illustration of the way in which the software can be used to adjust dosages of patients falling outside desired concentration targets. This software appears to be an extremely useful tool to further optimize voriconazole therapy and aid in therapeutic drug monitoring. Further prospective studies are now required to define the utility of the controller in daily clinical practice. PMID:23380734

Tailoring hospital marketing efforts to physicians' needs.

PubMed

Mackay, J M; Lamb, C W

1988-12-01

Marketing has become widely recognized as an important component of hospital management (Kotler and Clarke 1987; Ludke, Curry, and Saywell 1983). Physicians are becoming recognized as an important target market that warrants more marketing attention than it has received in the past (Super 1987; Wotruba, Haas, and Hartman 1982). Some experts predict that hospitals will begin focusing more marketing attention on physicians and less on consumers (Super 1986). Much of this attention is likely to take the form of practice management assistance, such as computer-based information system support or consulting services. The survey results reported here are illustrative only of how one hospital addressed the problem of physician need assessment. Other potential target markets include physicians who admit patients only to competitor hospitals and physicians who admit to multiple hospitals. The market might be segmented by individual versus group practice, area of specialization, or possibly even physician practice life cycle stage (Wotruba, Haas, and Hartman 1982). The questions included on the survey and the survey format are likely to be situation-specific. The key is the process, not the procedure. It is important for hospital marketers to recognize that practice management assistance needs will vary among markets (Jensen 1987). Therefore, hospitals must carefully identify their target physician market(s) and survey them about their specific needs before developing and implementing new physician marketing programs. Only then can they be reasonably confident that their marketing programs match their customers' needs.

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

46th Annual Targets, UAVs and Range Operations Symposium and Exhibition

DTIC Science & Technology

2008-10-10

introduction  mr. Ken Hislop , QF-16 Program manager, eglin aFB, FL 1:40 Pm - 2:00 Pm U.s. navy  CaPt Pat Buckley, Usn, Program manager...Bruce Ringstad Subscale & TCS Flight Lead Mr. Jim Cornwell Program Manager Mr. Ken Hislop Program Manager Ms. Lee Neugin Program Manager & Lead...Missiles Fired / 18 Kills AAC/PA 09-26-08-429 18 QF-16 Air Superiority Target Program Manager: Mr. Ken Hislop Description Full Scale Target for Threat

A review of the Fermilab fixed-target program

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

Rameika, R.

1994-12-01

All eyes are now on the Fermilab collider program as the intense search for the top quark continues. Nevertheless, Fermilab`s long tradition of operating a strong, diverse physics program depends not only on collider physics but also on effective use of the facilities the Laboratory was founded on, the fixed-target beamlines. In this talk the author presents highlights of the Fermilab fixed-target program from its (not too distant) past, (soon to be) present, and (hopefully, not too distant) future program. The author concentrates on those experiments which are unique to the fixed-target program, in particular hadron structure measurements which usemore » the varied beams and targets available in this mode and the physics results from kaon, hyperon and high statistics charm experiments which are not easily accessible in high p{sub T} hadron collider detectors.« less

Molecular cloning and cold shock induced overexpression of the DNA encoding phor sensor domain from Mycobacterium tuberculosis as a target molecule for novel anti-tubercular drugs

NASA Astrophysics Data System (ADS)

Langi, Gladys Emmanuella Putri; Moeis, Maelita R.; Ihsanawati, Giri-Rachman, Ernawati Arifin

2014-03-01

Mycobacterium tuberculosis (Mtb), the sole cause of Tuberculosis (TB), is still a major global problem. The discovery of new anti-tubercular drugs is needed to face the increasing TB cases, especially to prevent the increase of cases with resistant Mtb. A potential novel drug target is the Mtb PhoR sensor domain protein which is the histidine kinase extracellular domain for receiving environmental signals. This protein is the initial part of the two-component system PhoR-PhoP regulating 114 genes related to the virulence of Mtb. In this study, the gene encoding PhoR sensor domain (SensPhoR) was subcloned from pGEM-T SensPhoR from the previous study (Suwanto, 2012) to pColdII. The construct pColdII SensPhoR was confirmed through restriction analysis and sequencing. Using the construct, SensPhoR was overexpressed at 15°C using Escherichia coli BL21 (DE3). Low temperature was chosen because according to the solubility prediction program of recombinant proteins from The University of Oklahama, the PhoR sensor domain has a chance of 79.8% to be expressed as insoluble proteins in Escherichia coli's (E. coli) cytoplasm. This prediction is also supported by other similar programs: PROSO and PROSO II. The SDS PAGE result indicated that the PhoR sensor domain recombinant protein was overexpressed. For future studies, this protein will be purified and used for structure analysis which can be used to find potential drugs through rational drug design.

Examining the applicability of the IMB model in predicting condom use among sexually active secondary school students in Mbarara, Uganda

PubMed Central

Ybarra, Michele L.; Korchmaros, Josephine; Kiwanuka, Julius; Bangsberg, David R.; Bull, Sheana

2012-01-01

We tested the applicability of the IMB model in predicting condom use among sexually active secondary school students in Mbarara, Uganda. Three hundred and ninety adolescents across five secondary schools completed a self-report survey about their health and sexual experiences. Based upon results from structural equation modeling, the IMB model partially predicts condom use. Condom use was directly predicted by HIV prevention information and behavioral skills regarding having and using condoms. It was indirectly predicted (through behavioral skills regarding having and using condoms) by behavioral intentions regarding using condoms and talking to one‘s partner about safer sex. Aspects of one‘s first sexual experience (i.e., age at first sex, having discussed using condoms with first sex partner, willingness at first sex) are hugely influential of current condom use; this is especially true for discussing condoms with one‘s first partner. Findings highlight the importance of providing clear and comprehensive condom use training in HIV prevention programs aimed at Ugandan adolescents. They also underscore the importance of targeting abstinent youth before they become sexually active to positively affect their HIV preventive behavior at their first sexual experience. PMID:22350827

De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

PubMed

Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung

2011-08-01

Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Uncovering leaf rust responsive miRNAs in wheat (Triticum aestivum L.) using high-throughput sequencing and prediction of their targets through degradome analysis.

PubMed

Kumar, Dhananjay; Dutta, Summi; Singh, Dharmendra; Prabhu, Kumble Vinod; Kumar, Manish; Mukhopadhyay, Kunal

2017-01-01

Deep sequencing identified 497 conserved and 559 novel miRNAs in wheat, while degradome analysis revealed 701 targets genes. QRT-PCR demonstrated differential expression of miRNAs during stages of leaf rust progression. Bread wheat (Triticum aestivum L.) is an important cereal food crop feeding 30 % of the world population. Major threat to wheat production is the rust epidemics. This study was targeted towards identification and functional characterizations of micro(mi)RNAs and their target genes in wheat in response to leaf rust ingression. High-throughput sequencing was used for transcriptome-wide identification of miRNAs and their expression profiling in retort to leaf rust using mock and pathogen-inoculated resistant and susceptible near-isogenic wheat plants. A total of 1056 mature miRNAs were identified, of which 497 miRNAs were conserved and 559 miRNAs were novel. The pathogen-inoculated resistant plants manifested more miRNAs compared with the pathogen infected susceptible plants. The miRNA counts increased in susceptible isoline due to leaf rust, conversely, the counts decreased in the resistant isoline in response to pathogenesis illustrating precise spatial tuning of miRNAs during compatible and incompatible interaction. Stem-loop quantitative real-time PCR was used to profile 10 highly differentially expressed miRNAs obtained from high-throughput sequencing data. The spatio-temporal profiling validated the differential expression of miRNAs between the isolines as well as in retort to pathogen infection. Degradome analysis provided 701 predicted target genes associated with defense response, signal transduction, development, metabolism, and transcriptional regulation. The obtained results indicate that wheat isolines employ diverse arrays of miRNAs that modulate their target genes during compatible and incompatible interaction. Our findings contribute to increase knowledge on roles of microRNA in wheat-leaf rust interactions and could help in rust resistance breeding programs.

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-03-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

NASA Astrophysics Data System (ADS)

Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

2018-06-01

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2-D seismic reflection data processing flow focused on pre-stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching, to estimate the uncertainties of the depths of key horizons near the Deep Sea Drilling Project (DSDP) borehole 258 (DSDP-258) located in the Mentelle Basin, southwest of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ±2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent to the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program, leg 369.

Genome-wide computational identification of microRNAs and their targets in the deep-branching eukaryote Giardia lamblia.

PubMed

Zhang, Yan-Qiong; Chen, Dong-Liang; Tian, Hai-Feng; Zhang, Bao-Hong; Wen, Jian-Fan

2009-10-01

Using a combined computational program, we identified 50 potential microRNAs (miRNAs) in Giardia lamblia, one of the most primitive unicellular eukaryotes. These miRNAs are unique to G. lamblia and no homologues have been found in other organisms; miRNAs, currently known in other species, were not found in G. lamblia. This suggests that miRNA biogenesis and miRNA-mediated gene regulation pathway may evolve independently, especially in evolutionarily distant lineages. A majority (43) of the predicted miRNAs are located at one single locus; however, some miRNAs have two or more copies in the genome. Among the 58 miRNA genes, 28 are located in the intergenic regions whereas 30 are present in the anti-sense strands of the protein-coding sequences. Five predicted miRNAs are expressed in G. lamblia trophozoite cells evidenced by expressed sequence tags or RT-PCR. Thirty-seven identified miRNAs may target 50 protein-coding genes, including seven variant-specific surface proteins (VSPs). Our findings provide a clue that miRNA-mediated gene regulation may exist in the early stage of eukaryotic evolution, suggesting that it is an important regulation system ubiquitous in eukaryotes.

Short-term Efficacy of Click City®: Tobacco: Changing Etiological Mechanisms Related to the Onset of Tobacco Use

PubMed Central

Andrews, Judy A.; Gordon, Judith S.; Hampson, Sarah H.; Christiansen, Steven M.; Gunn, Barbara; Slovic, Paul; Severson, Herbert H.

2011-01-01

This paper described the short-term results from an ongoing randomized controlled efficacy study of Click City®: Tobacco, a tobacco prevention program designed for 5th graders, with a booster in sixth grade. Click City®: Tobacco is an innovative school-based prevention program delivered via an intranet, a series of linked computers with a single server. The components of the program target theoretically based and empirically supported etiological mechanisms predictive of future willingness and intentions to use tobacco and initiation of tobacco use. Each component was designed to change one or more etiological mechanisms and was empirically evaluated in the laboratory prior to inclusion in the program. Short-term results from 47 elementary schools (24 schools who used Click City®: Tobacco, and 23 who continued with their usual curriculum) showed change in intentions and willingness to use tobacco from baseline to one-week following the completion of the 5th grade sessions. The results demonstrate the short-term efficacy of this program and suggest that experimentally evaluating components prior to including them in the program contributed to the efficacy of the program. The program was most efficacious for students who were most at risk. PMID:21286810

Intermittently-visual Tracking Experiments Reveal the Roles of Error-correction and Predictive Mechanisms in the Human Visual-motor Control System

NASA Astrophysics Data System (ADS)

Hayashi, Yoshikatsu; Tamura, Yurie; Sase, Kazuya; Sugawara, Ken; Sawada, Yasuji

Prediction mechanism is necessary for human visual motion to compensate a delay of sensory-motor system. In a previous study, “proactive control” was discussed as one example of predictive function of human beings, in which motion of hands preceded the virtual moving target in visual tracking experiments. To study the roles of the positional-error correction mechanism and the prediction mechanism, we carried out an intermittently-visual tracking experiment where a circular orbit is segmented into the target-visible regions and the target-invisible regions. Main results found in this research were following. A rhythmic component appeared in the tracer velocity when the target velocity was relatively high. The period of the rhythm in the brain obtained from environmental stimuli is shortened more than 10%. The shortening of the period of rhythm in the brain accelerates the hand motion as soon as the visual information is cut-off, and causes the precedence of hand motion to the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand motion precedes the target in average when the predictive mechanism dominates the error-corrective mechanism.

Progression of chronic periodontitis can be predicted by the levels of Porphyromonas gingivalis and Treponema denticola in subgingival plaque.

PubMed

Byrne, S J; Dashper, S G; Darby, I B; Adams, G G; Hoffmann, B; Reynolds, E C

2009-12-01

Chronic periodontitis is an inflammatory disease of the supporting tissues of the teeth associated with bacteria. Diagnosis is achieved retrospectively by clinical observation of attachment loss. Predicting disease progression would allow for targeted preventive therapy. The aim of this study was to monitor disease progression in patients on a maintenance program and determine the levels of specific bacteria in subgingival plaque samples and then examine the ability of the clinical parameters of disease and levels of specific bacteria in the plaque samples to predict disease progression. During a 12-month longitudinal study of 41 subjects, 25 sites in 21 subjects experienced disease progression indicated by at least 2 mm of clinical attachment loss. Real-time polymerase chain reaction was used to determine the levels of Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Fusobacterium nucleatum, and Prevotella intermedia in subgingival plaque samples. No clinical parameters were able to predict periodontal disease progression. In sites undergoing imminent periodontal disease progression within the next 3 months, significant partial correlations were found between P. gingivalis and T. forsythia (r = 0.55, P < 0.001) and T. denticola and T. forsythia (r = 0.43, P = 0.04). The odds of a site undergoing imminent periodontal disease progression increased with increasing levels of P. gingivalis and T. denticola. Monitoring the proportions of P. gingivalis and T. denticola in subgingival plaque has the potential to help identify sites at significant risk for progression of periodontitis, which would assist in the targeted treatment of disease.

Predicting influenza vaccination uptake among health care workers: what are the key motivators?

PubMed

Corace, Kimberly; Prematunge, Chatura; McCarthy, Anne; Nair, Rama C; Roth, Virginia; Hayes, Thomas; Suh, Kathryn N; Balfour, Louise; Garber, Gary

2013-08-01

Health care worker (HCW) vaccination was critical to protecting HCW during the H1N1 pandemic. However, vaccine uptake rates fell below recommended targets. This study examined motivators and barriers influencing HCW pH1N1 vaccination to identify modifiable factors that can improve influenza vaccine uptake. A cross-sectional survey was conducted at a large Canadian tertiary care hospital. HCW (N = 3,275) completed measures of demographics, vaccination history, influenza risk factors, and attitudes toward pH1N1 vaccination. Self-reported vaccination was verified with staff vaccination records. Of the total sample, 2,862 (87.4%) HCW received the pH1N1 vaccine. Multiple logistic regression analyses were used to predict HCW vaccination. HCW attitudes toward vaccination significantly predicted vaccination, even after adjusting for demographics, vaccine history, and influenza risk factors. This model correctly predicted 95% (confidence interval [CI]: 0.93-0.96) of HCW vaccination. Key modifiable factors driving HCW vaccination include (1) desire to protect family members and patients, (2) belief that vaccination is important even if one is healthy, (3) confidence in vaccine safety, and (4) supervisor and physician encouragement. This research identified fundamental reasons why HCW get vaccinated and provides direction for future influenza vaccination programs. To enhance vaccine uptake, it is important to target HCW attitudes in influenza vaccine campaigns and create a culture of vaccine promotion in the workplace, including strong messaging from supervisors and physicians. Copyright © 2013 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

NICMOS Focus and HST Breathing

NASA Astrophysics Data System (ADS)

Suchkov, A.; Hershey, J.

1998-09-01

The program 7608 monitored on a biweekly basis NICMOS camera foci from June 9, 1997, through February 18, 1998. Each of the biweekly observations included 17 measurements of focus position (focus sweeps), individually for each of the three cameras. The measurements for camera 1 and camera 3 foci covered one or two HST orbital periods. Comparison of these measurements with the predictions of the three OTA focus breathing models has shown the following. (1). Focus variations seen in NICMOS focus sweeps correlate well with the OTA focus thermal breathing as predicted by breathing models (“4- temperature”, “full-temperature”, and “attitude” models). Thus they can be attributed mostly to the HST orbital temperature variation. (2). The amount of breathing (breathing amplitude) has been found to be on average larger in the first orbit after a telescope slew to a new target. This is explained as being due to additional thermal perturbations caused by the change in the HST attitude as the telescope repoints to a new target. (3). In the first orbit, the amount of focus change predicted by the 4-temperature model is about the same as that seen in the focus sweeps data (breathing scale factor ~1). However the full-temperature model predicts a two times smaller breathing amplitude (breathing scale factor ~1.7). This suggests that the light shield temperatures are more responsive to the attitude change than temperatures from the other temperature sensors. The results of this study may help to better understand the HST thermal cycles and to improve the models describing the impact of those on both the OTA and NICMOS focus.

An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data.

PubMed

Jin, Guangxu; Zhao, Hong; Zhou, Xiaobo; Wong, Stephen T C

2011-07-01

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data. We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens. We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism. The software implemented in Python 2.7 programming language is available from request. stwong@tmhs.org.

An Optimized Transient Dual Luciferase Assay for Quantifying MicroRNA Directed Repression of Targeted Sequences

PubMed Central

Moyle, Richard L.; Carvalhais, Lilia C.; Pretorius, Lara-Simone; Nowak, Ekaterina; Subramaniam, Gayathery; Dalton-Morgan, Jessica; Schenk, Peer M.

2017-01-01

Studies investigating the action of small RNAs on computationally predicted target genes require some form of experimental validation. Classical molecular methods of validating microRNA action on target genes are laborious, while approaches that tag predicted target sequences to qualitative reporter genes encounter technical limitations. The aim of this study was to address the challenge of experimentally validating large numbers of computationally predicted microRNA-target transcript interactions using an optimized, quantitative, cost-effective, and scalable approach. The presented method combines transient expression via agroinfiltration of Nicotiana benthamiana leaves with a quantitative dual luciferase reporter system, where firefly luciferase is used to report the microRNA-target sequence interaction and Renilla luciferase is used as an internal standard to normalize expression between replicates. We report the appropriate concentration of N. benthamiana leaf extracts and dilution factor to apply in order to avoid inhibition of firefly LUC activity. Furthermore, the optimal ratio of microRNA precursor expression construct to reporter construct and duration of the incubation period post-agroinfiltration were determined. The optimized dual luciferase assay provides an efficient, repeatable and scalable method to validate and quantify microRNA action on predicted target sequences. The optimized assay was used to validate five predicted targets of rice microRNA miR529b, with as few as six technical replicates. The assay can be extended to assess other small RNA-target sequence interactions, including assessing the functionality of an artificial miRNA or an RNAi construct on a targeted sequence. PMID:28979287

How accurately does the Brief Job Stress Questionnaire identify workers with or without potential psychological distress?

PubMed

Tsutsumi, Akizumi; Inoue, Akiomi; Eguchi, Hisashi

2017-07-27

The manual for the Japanese Stress Check Program recommends use of the Brief Job Stress Questionnaire (BJSQ) from among the program's instruments and proposes criteria for defining "high-stress" workers. This study aimed to examine how accurately the BJSQ identifies workers with or without potential psychological distress. We used an online survey to administer the BJSQ with a psychological distress scale (K6) to randomly selected workers (n=1,650). We conducted receiver operating characteristics curve analyses to estimate the screening performance of the cutoff points that the Stress Check Program manual recommends for the BJSQ. Prevalence of workers with potential psychological distress defined as K6 score ≥13 was 13%. Prevalence of "high-risk" workers defined using criteria recommended by the program manual was 16.7% for the original version of the BJSQ. The estimated values were as follows: sensitivity, 60.5%; specificity, 88.9%; Youden index, 0.504; positive predictive value, 47.3%; negative predictive value, 93.8%; positive likelihood ratio, 6.0; and negative likelihood ratio, 0.4. Analyses based on the simplified BJSQ indicated lower sensitivity compared with the original version, although we expected roughly the same screening performance for the best scenario using the original version. Our analyses in which psychological distress measured by K6 was set as the target condition indicate less than half of the identified "high-stress" workers warrant consideration for secondary screening for psychological distress.

OCEAN: Optimized Cross rEActivity estimatioN.

PubMed

Czodrowski, Paul; Bolick, Wolf-Guido

2016-10-24

The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN.

Host-Parasite Interactions from the Inside: Plant Reproductive Ontogeny Drives Specialization in Parasitic Insects

PubMed Central

Boivin, Thomas; Gidoin, Cindy; von Aderkas, Patrick; Safrana, Jonathan; Candau, Jean-Noël; Chalon, Alain; Sondo, Marion; El Maâtaoui, Mohamed

2015-01-01

Host plant interactions are likely key drivers of evolutionary processes involved in the diversification of phytophagous insects. Granivory has received substantial attention for its crucial role in shaping the interaction between plants and their seed parasites, but fine-scale mechanisms explaining the role of host plant reproductive biology on specialization of seed parasites remain poorly described. In a comparative approach using plant histological techniques, we tested the hypotheses that different seed parasite species synchronize their life cycles to specific stages in seed development, and that the stage they target depends on major differences in seed development programs. In a pinaceous system, seed storage products are initiated before ovule fertilization and the wasps target the ovule’s nucellus during megagametogenesis, a stage at which larvae may benefit from the by-products derived from both secreting cells and dying nucellar cells. In a cupressaceous system, oviposition activity peaks later, during embryogenesis, and the wasps target the ovule’s megagametophyte where larvae may benefit from cell disintegration during embryogenesis. Our cytohistological approach shows for the first time how, despite divergent oviposition targets, different parasite species share a common strategy that consists of first competing for nutrients with developing plant structures, and then consuming these developed structures to complete their development. Our results support the prediction that seed developmental program is an axis for specialization in seed parasites, and that it could be an important parameter in models of their ecological and taxonomic divergence. This study provides the basis for further investigating the possibility of the link between plant ontogeny and pre-dispersal seed parasitism. PMID:26441311

Host-Parasite Interactions from the Inside: Plant Reproductive Ontogeny Drives Specialization in Parasitic Insects.

PubMed

Boivin, Thomas; Gidoin, Cindy; von Aderkas, Patrick; Safrana, Jonathan; Candau, Jean-Noël; Chalon, Alain; Sondo, Marion; El Maâtaoui, Mohamed

2015-01-01

Host plant interactions are likely key drivers of evolutionary processes involved in the diversification of phytophagous insects. Granivory has received substantial attention for its crucial role in shaping the interaction between plants and their seed parasites, but fine-scale mechanisms explaining the role of host plant reproductive biology on specialization of seed parasites remain poorly described. In a comparative approach using plant histological techniques, we tested the hypotheses that different seed parasite species synchronize their life cycles to specific stages in seed development, and that the stage they target depends on major differences in seed development programs. In a pinaceous system, seed storage products are initiated before ovule fertilization and the wasps target the ovule's nucellus during megagametogenesis, a stage at which larvae may benefit from the by-products derived from both secreting cells and dying nucellar cells. In a cupressaceous system, oviposition activity peaks later, during embryogenesis, and the wasps target the ovule's megagametophyte where larvae may benefit from cell disintegration during embryogenesis. Our cytohistological approach shows for the first time how, despite divergent oviposition targets, different parasite species share a common strategy that consists of first competing for nutrients with developing plant structures, and then consuming these developed structures to complete their development. Our results support the prediction that seed developmental program is an axis for specialization in seed parasites, and that it could be an important parameter in models of their ecological and taxonomic divergence. This study provides the basis for further investigating the possibility of the link between plant ontogeny and pre-dispersal seed parasitism.

Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

NASA Astrophysics Data System (ADS)

Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Montes, Matthieu

2018-01-01

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

Why Do White Americans Oppose Race-Targeted Policies? Clarifying the Impact of Symbolic Racism

PubMed Central

Rabinowitz, Joshua L.; Sears, David O.; Sidanius, Jim; Krosnick, Jon A.

2009-01-01

Measures of symbolic racism (SR) have often been used to tap racial prejudice toward Blacks. However, given the wording of questions used for this purpose, some of the apparent effects on attitudes toward policies to help Blacks may instead be due to political conservatism, attitudes toward government, and/or attitudes toward redistributive government policies in general. Using data from national probability sample surveys and an experiment, we explored whether SR has effects even when controlling for these potential confounds and whether its effects are specific to policies involving Blacks. Holding constant conservatism and attitudes toward limited government, SR predicted Whites' opposition to policies designed to help Blacks and more weakly predicted attitudes toward social programs whose beneficiaries were racially ambiguous. An experimental manipulation of policy beneficiaries revealed that SR predicted policy attitudes when Blacks were the beneficiary but not when women were. These findings are consistent with the claim that SR's association with racial policy preferences is not due to these confounds. PMID:20161542

Temperamental Differences in Children’s Reactions to Peer Victimization

PubMed Central

Sugimura, Niwako; Rudolph, Karen D.

2015-01-01

Objective This research examined the hypothesis that temperament and sex moderate the contribution of peer victimization to children’s subsequent adjustment (aggression and depressive symptoms). Method Children (125 boys, 158 girls; M age = 7.95 years, SD = 0.32; 77.7% White, 22.3% minority) and teachers reported on overt and relational victimization. Parents rated children’s temperament (inhibitory control and negative emotionality) and depressive symptoms, and teachers reported on children’s overt and relational aggression. Results Across a one-year time period, (a) overt victimization predicted overt aggression in girls with poor inhibitory control; (b) overt and relational victimization predicted depressive symptoms in girls with high negative emotionality; and (c) relational victimization predicted depressive symptoms in boys with low negative emotionality. Conclusions This research helps to explain individual variation in children’s reactions to peer victimization, and has implications for person-by-environment models of development. Moreover, this research informs the development of targeted intervention programs for victimized youth that bolster specific resources depending on their temperament. PMID:22420650

On the internal target model in a tracking task

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Baron, S.

1981-01-01

An optimal control model for predicting operator's dynamic responses and errors in target tracking ability is summarized. The model, which predicts asymmetry in the tracking data, is dependent on target maneuvers and trajectories. Gunners perception, decision making, control, and estimate of target positions and velocity related to crossover intervals are discussed. The model provides estimates for means, standard deviations, and variances for variables investigated and for operator estimates of future target positions and velocities.

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

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

psRNATarget: a plant small RNA target analysis server

PubMed Central

Dai, Xinbin; Zhao, Patrick Xuechun

2011-01-01

Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/. PMID:21622958

Global analysis of bacterial transcription factors to predict cellular target processes.

PubMed

Doerks, Tobias; Andrade, Miguel A; Lathe, Warren; von Mering, Christian; Bork, Peer

2004-03-01

Whole-genome sequences are now available for >100 bacterial species, giving unprecedented power to comparative genomics approaches. We have applied genome-context methods to predict target processes that are regulated by transcription factors (TFs). Of 128 orthologous groups of proteins annotated as TFs, to date, 36 are functionally uncharacterized; in our analysis we predict a probable cellular target process or biochemical pathway for half of these functionally uncharacterized TFs.

Renaissance of the ~1 TeV Fixed-Target Program

NASA Astrophysics Data System (ADS)

Adams, T.; Appel, J. A.; Arms, K. E.; Balantekin, A. B.; Conrad, J. M.; Cooper, P. S.; Djurcic, Z.; Dunwoodie, W.; Engelfried, J.; Fisher, P. H.; Gottschalk, E.; de Gouvea, A.; Heller, K.; Ignarra, C. M.; Karagiorgi, G.; Kwan, S.; Loinaz, W. A.; Meadows, B.; Moore, R.; Morfín, J. G.; Naples, D.; Nienaber, P.; Pate, S. F.; Papavassiliou, V.; Petrov, A. A.; Purohit, M. V.; Ray, H.; Russ, J.; Schwartz, A. J.; Seligman, W. G.; Shaevitz, M. H.; Schellman, H.; Spitz, J.; Syphers, M. J.; Tait, T. M. P.; Vannucci, F.

This document describes the physics potential of a new fixed-target program based on a ~1 TeV proton source. Two proton sources are potentially available in the future: the existing Tevatron at Fermilab, which can provide 800 GeV protons for fixed-target physics, and a possible upgrade to the SPS at CERN, called SPS+, which would produce 1 TeV protons on target. In this paper we use an example Tevatron fixed-target program to illustrate the high discovery potential possible in the charm and neutrino sectors. We highlight examples which are either unique to the program or difficult to accomplish at other venues.

Renaissance of the ~ 1-TeV Fixed-Target Program

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

Adams, T.; /Florida State U.; Appel, J.A.

2011-12-02

This document describes the physics potential of a new fixed-target program based on a {approx}1 TeV proton source. Two proton sources are potentially available in the future: the existing Tevatron at Fermilab, which can provide 800 GeV protons for fixed-target physics, and a possible upgrade to the SPS at CERN, called SPS+, which would produce 1 TeV protons on target. In this paper we use an example Tevatron fixed-target program to illustrate the high discovery potential possible in the charm and neutrino sectors. We highlight examples which are either unique to the program or difficult to accomplish at other venues.

Sub-Model Partial Least Squares for Improved Accuracy in Quantitative Laser Induced Breakdown Spectroscopy

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Clegg, S. M.; Frydenvang, J.

2015-12-01

One of the primary challenges faced by the ChemCam instrument on the Curiosity Mars rover is developing a regression model that can accurately predict the composition of the wide range of target types encountered (basalts, calcium sulfate, feldspar, oxides, etc.). The original calibration used 69 rock standards to train a partial least squares (PLS) model for each major element. By expanding the suite of calibration samples to >400 targets spanning a wider range of compositions, the accuracy of the model was improved, but some targets with "extreme" compositions (e.g. pure minerals) were still poorly predicted. We have therefore developed a simple method, referred to as "submodel PLS", to improve the performance of PLS across a wide range of target compositions. In addition to generating a "full" (0-100 wt.%) PLS model for the element of interest, we also generate several overlapping submodels (e.g. for SiO2, we generate "low" (0-50 wt.%), "mid" (30-70 wt.%), and "high" (60-100 wt.%) models). The submodels are generally more accurate than the "full" model for samples within their range because they are able to adjust for matrix effects that are specific to that range. To predict the composition of an unknown target, we first predict the composition with the submodels and the "full" model. Then, based on the predicted composition from the "full" model, the appropriate submodel prediction can be used (e.g. if the full model predicts a low composition, use the "low" model result, which is likely to be more accurate). For samples with "full" predictions that occur in a region of overlap between submodels, the submodel predictions are "blended" using a simple linear weighted sum. The submodel PLS method shows improvements in most of the major elements predicted by ChemCam and reduces the occurrence of negative predictions for low wt.% targets. Submodel PLS is currently being used in conjunction with ICA regression for the major element compositions of ChemCam data.

The acquisition of contextual cueing effects by persons with and without intellectual disability.

PubMed

Merrill, Edward C; Conners, Frances A; Yang, Yingying; Weathington, Dana

2014-10-01

Two experiments were conducted to compare the acquisition of contextual cueing effects of adolescents and young adults with intellectual disabilities (ID) relative to typically developing children and young adults. Contextual cueing reflects an implicit, memory based attention guidance mechanism that results in faster search for target locations that have been previously experienced in a predictable context. In the study, participants located a target stimulus embedded in a context of numerous distracter stimuli. During a learning phase, the location of the target was predictable from the location of the distracters in the search displays. We then compared response times to locating predictable relative to unpredictable targets presented in a test phase. In Experiment 1, all of the distracters predicted the location of the target. In Experiment 2, half of the distracters predicted the location of the target while the other half varied randomly. The participants with ID exhibited significant contextual facilitation in both experiments, with the magnitude of facilitation being similar to that of the typically developing (TD) children and adults. We concluded that deficiencies in contextual cueing are not necessarily associated with low measured intelligence that results in a classification of ID. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modified linear predictive coding approach for moving target tracking by Doppler radar

NASA Astrophysics Data System (ADS)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Towards crystal structure prediction of complex organic compounds – a report on the fifth blind test

PubMed Central

Bardwell, David A.; Adjiman, Claire S.; Arnautova, Yelena A.; Bartashevich, Ekaterina; Boerrigter, Stephan X. M.; Braun, Doris E.; Cruz-Cabeza, Aurora J.; Day, Graeme M.; Della Valle, Raffaele G.; Desiraju, Gautam R.; van Eijck, Bouke P.; Facelli, Julio C.; Ferraro, Marta B.; Grillo, Damian; Habgood, Matthew; Hofmann, Detlef W. M.; Hofmann, Fridolin; Jose, K. V. Jovan; Karamertzanis, Panagiotis G.; Kazantsev, Andrei V.; Kendrick, John; Kuleshova, Liudmila N.; Leusen, Frank J. J.; Maleev, Andrey V.; Misquitta, Alston J.; Mohamed, Sharmarke; Needs, Richard J.; Neumann, Marcus A.; Nikylov, Denis; Orendt, Anita M.; Pal, Rumpa; Pantelides, Constantinos C.; Pickard, Chris J.; Price, Louise S.; Price, Sarah L.; Scheraga, Harold A.; van de Streek, Jacco; Thakur, Tejender S.; Tiwari, Siddharth; Venuti, Elisabetta; Zhitkov, Ilia K.

2011-01-01

Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories – a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome. PMID:22101543

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

PubMed

Romero-Durán, Francisco J; Alonso, Nerea; Yañez, Matilde; Caamaño, Olga; García-Mera, Xerardo; González-Díaz, Humberto

2016-04-01

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eos

Some of the most interesting CASP11 targets through the eyes of their authors

PubMed Central

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K.; Edwards, Robert A.; Fass, Deborah; Hartmann, Marcus D.; Korycinski, Mateusz; Lewis, Richard J.; Lorimer, Donald; Lupas, Andrei N.; Newman, Janet; Peat, Thomas S.; Piepenbrink, Kurt H.; Prahlad, Janani; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca M.; Seguritan, Victor; Sundberg, Eric J.; Singh, Abhimanyu K.; Wilson, Mark A.

2015-01-01

ABSTRACT The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34–50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:26473983

Predictive saccade in the absence of smooth pursuit: interception of moving targets in the archer fish.

PubMed

Ben-Simon, Avi; Ben-Shahar, Ohad; Vasserman, Genadiy; Segev, Ronen

2012-12-15

Interception of fast-moving targets is a demanding task many animals solve. To handle it successfully, mammals employ both saccadic and smooth pursuit eye movements in order to confine the target to their area centralis. But how can non-mammalian vertebrates, which lack smooth pursuit, intercept moving targets? We studied this question by exploring eye movement strategies employed by archer fish, an animal that possesses an area centralis, lacks smooth pursuit eye movements, but can intercept moving targets by shooting jets of water at them. We tracked the gaze direction of fish during interception of moving targets and found that they employ saccadic eye movements based on prediction of target position when it is hit. The fish fixates on the target's initial position for ∼0.2 s from the onset of its motion, a time period used to predict whether a shot can be made before the projection of the target exits the area centralis. If the prediction indicates otherwise, the fish performs a saccade that overshoots the center of gaze beyond the present target projection on the retina, such that after the saccade the moving target remains inside the area centralis long enough to prepare and perform a shot. These results add to the growing body of knowledge on biological target tracking and may shed light on the mechanism underlying this behavior in other animals with no neural system for the generation of smooth pursuit eye movements.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.

PubMed

Richard, Ann M; Judson, Richard S; Houck, Keith A; Grulke, Christopher M; Volarath, Patra; Thillainadarajah, Inthirany; Yang, Chihae; Rathman, James; Martin, Matthew T; Wambaugh, John F; Knudsen, Thomas B; Kancherla, Jayaram; Mansouri, Kamel; Patlewicz, Grace; Williams, Antony J; Little, Stephen B; Crofton, Kevin M; Thomas, Russell S

2016-08-15

The U.S. Environmental Protection Agency's (EPA) ToxCast program is testing a large library of Agency-relevant chemicals using in vitro high-throughput screening (HTS) approaches to support the development of improved toxicity prediction models. Launched in 2007, Phase I of the program screened 310 chemicals, mostly pesticides, across hundreds of ToxCast assay end points. In Phase II, the ToxCast library was expanded to 1878 chemicals, culminating in the public release of screening data at the end of 2013. Subsequent expansion in Phase III has resulted in more than 3800 chemicals actively undergoing ToxCast screening, 96% of which are also being screened in the multi-Agency Tox21 project. The chemical library unpinning these efforts plays a central role in defining the scope and potential application of ToxCast HTS results. The history of the phased construction of EPA's ToxCast library is reviewed, followed by a survey of the library contents from several different vantage points. CAS Registry Numbers are used to assess ToxCast library coverage of important toxicity, regulatory, and exposure inventories. Structure-based representations of ToxCast chemicals are then used to compute physicochemical properties, substructural features, and structural alerts for toxicity and biotransformation. Cheminformatics approaches using these varied representations are applied to defining the boundaries of HTS testability, evaluating chemical diversity, and comparing the ToxCast library to potential target application inventories, such as used in EPA's Endocrine Disruption Screening Program (EDSP). Through several examples, the ToxCast chemical library is demonstrated to provide comprehensive coverage of the knowledge domains and target inventories of potential interest to EPA. Furthermore, the varied representations and approaches presented here define local chemistry domains potentially worthy of further investigation (e.g., not currently covered in the testing library or defined by toxicity "alerts") to strategically support data mining and predictive toxicology modeling moving forward.

Research Opportunities from Emerging Atmospheric Observing and Modeling Capabilities.

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Schlatter, Thomas W.

1996-02-01

The Second Prospectus Development Team (PDT-2) of the U.S. Weather Research Program was charged with identifying research opportunities that are best matched to emerging operational and experimental measurement and modeling methods. The overarching recommendation of PDT-2 is that inputs for weather forecast models can best be obtained through the use of composite observing systems together with adaptive (or targeted) observing strategies employing both in situ and remote sensing. Optimal observing systems and strategies are best determined through a three-part process: observing system simulation experiments, pilot field measurement programs, and model-assisted data sensitivity experiments. Furthermore, the mesoscale research community needs easy and timely access to the new operational and research datasets in a form that can readily be reformatted into existing software packages for analysis and display. The value of these data is diminished to the extent that they remain inaccessible.The composite observing system of the future must combine synoptic observations, routine mobile observations, and targeted observations, as the current or forecast situation dictates. High costs demand fuller exploitation of commercial aircraft, meteorological and navigation [Global Positioning System (GPS)] satellites, and Doppler radar. Single observing systems must be assessed in the context of a composite system that provides complementary information. Maintenance of the current North American rawinsonde network is critical for progress in both research-oriented and operational weather forecasting.Adaptive sampling strategies are designed to improve large-scale and regional weather prediction but they will also improve diagnosis and prediction of flash flooding, air pollution, forest fire management, and other environmental emergencies. Adaptive measurements can be made by piloted or unpiloted aircraft. Rawinsondes can be launched and satellites can be programmed to make adaptive observations at special times or in specific regions. PDT-2 specifically recommends the following forms of data gathering: a pilot field and modeling study should be designed and executed to assess the benefit of adaptive observations over the eastern Pacific for mesoscale forecasts over the contiguous United

Family Functioning and Adolescent Alcohol Use: A Moderated Mediation Analysis

PubMed Central

Ohannessian, Christine McCauley; Flannery, Kaitlin M.; Simpson, Emily; Russell, Beth S.

2016-01-01

The primary goals of this longitudinal study were to examine the relationship between family functioning and adolescent alcohol use and to examine whether depressed mood mediates this relationship. An additional goal was to explore whether these relations were moderated by gender. The sample included 1,031 high school students from the Mid-Atlantic United States. Participants completed surveys in school during the spring of 2007, 2008, and 2009. Path analysis results indicated that family functioning predicted alcohol use for girls. Moreover, depressed mood mediated this relationship. None of the direct paths between family functioning and adolescent alcohol use were significant for boys. However, similar to girls, depressed mood negatively predicted alcohol use for boys. Taken together, the findings highlight the need for prevention programs targeting adolescent substance use to consider gender-specific trajectories. PMID:26994346

PD-L1 (CD274) promoter methylation predicts survival in colorectal cancer patients.

PubMed

Goltz, Diane; Gevensleben, Heidrun; Dietrich, Jörn; Dietrich, Dimo

2017-01-01

This study evaluates promoter methylation of the programmed cell death ligand 1 (PD-L1) as a biomarker in a cohort of 383 colorectal cancer patients. PD-L1 methylation (m PD-L1 ) was inversely correlated with PD-L1 mRNA expression ( p = 0.001) and was associated with significantly shorter overall survival (OS, p = 0.003) and recurrence-free survival (RFS, p < 0.001). In age-stratified multivariate Cox proportional hazards analyses including sex, tumor, nodal, distant metastasis categories, microsatellite instability (MSI)-status, and PD-L1 mRNA, m PD-L1 is classified as an independent prognostic factor (OS: p = 0.030; RFS: p < 0.001). Further studies are needed to evaluate PD-L1 methylation as a biomarker for response prediction of immunotherapies targeting the PD-1/PD-L1 axis.

A component-centered meta-analysis of family-based prevention programs for adolescent substance use.

PubMed

Van Ryzin, Mark J; Roseth, Cary J; Fosco, Gregory M; Lee, You-Kyung; Chen, I-Chien

2016-04-01

Although research has documented the positive effects of family-based prevention programs, the field lacks specific information regarding why these programs are effective. The current study summarized the effects of family-based programs on adolescent substance use using a component-based approach to meta-analysis in which we decomposed programs into a set of key topics or components that were specifically addressed by program curricula (e.g., parental monitoring/behavior management,problem solving, positive family relations, etc.). Components were coded according to the amount of time spent on program services that targeted youth, parents, and the whole family; we also coded effect sizes across studies for each substance-related outcome. Given the nested nature of the data, we used hierarchical linear modeling to link program components (Level 2) with effect sizes (Level 1). The overall effect size across programs was .31, which did not differ by type of substance. Youth-focused components designed to encourage more positive family relationships and a positive orientation toward the future emerged as key factors predicting larger than average effect sizes. Our results suggest that, within the universe of family-based prevention, where components such as parental monitoring/behavior management are almost universal, adding or expanding certain youth-focused components may be able to enhance program efficacy. Copyright © 2016 Elsevier Ltd. All rights reserved.

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method

PubMed Central

Dakshanamurthy, Sivanesan; Issa, Naiem T; Assefnia, Shahin; Seshasayee, Ashwini; Peters, Oakland J; Madhavan, Subha; Uren, Aykut; Brown, Milton L; Byers, Stephen W

2012-01-01

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27,000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets. PMID:22780961

SU-G-JeP3-09: Tumor Location Prediction Using Natural Respiratory Volume for Respiratory Gated Radiation Therapy (RGRT): System Verification Study

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

Kim, M; Jung, J; Yoon, D

Purpose: Respiratory gated radiation therapy (RGRT) gives accurate results when a patient’s breathing is stable and regular. Thus, the patient should be fully aware during respiratory pattern training before undergoing the RGRT treatment. In order to bypass the process of respiratory pattern training, we propose a target location prediction system for RGRT that uses only natural respiratory volume, and confirm its application. Methods: In order to verify the proposed target location prediction system, an in-house phantom set was used. This set involves a chest phantom including target, external markers, and motion generator. Natural respiratory volume signals were generated using themore » random function in MATLAB code. In the chest phantom, the target takes a linear motion based on the respiratory signal. After a four-dimensional computed tomography (4DCT) scan of the in-house phantom, the motion trajectory was derived as a linear equation. The accuracy of the linear equation was compared with that of the motion algorithm used by the operating motion generator. In addition, we attempted target location prediction using random respiratory volume values. Results: The correspondence rate of the linear equation derived from the 4DCT images with the motion algorithm of the motion generator was 99.41%. In addition, the average error rate of target location prediction was 1.23% for 26 cases. Conclusion: We confirmed the applicability of our proposed target location prediction system for RGRT using natural respiratory volume. If additional clinical studies can be conducted, a more accurate prediction system can be realized without requiring respiratory pattern training.« less

Scale-up of a physiologically-based pharmacokinetic model to predict the disposition of monoclonal antibodies in monkeys.

PubMed

Glassman, Patrick M; Chen, Yang; Balthasar, Joseph P

2015-10-01

Preclinical assessment of monoclonal antibody (mAb) disposition during drug development often includes investigations in non-human primate models. In many cases, mAb exhibit non-linear disposition that relates to mAb-target binding [i.e., target-mediated disposition (TMD)]. The goal of this work was to develop a physiologically-based pharmacokinetic (PBPK) model to predict non-linear mAb disposition in plasma and in tissues in monkeys. Physiological parameters for monkeys were collected from several sources, and plasma data for several mAbs associated with linear pharmacokinetics were digitized from prior literature reports. The digitized data displayed great variability; therefore, parameters describing inter-antibody variability in the rates of pinocytosis and convection were estimated. For prediction of the disposition of individual antibodies, we incorporated tissue concentrations of target proteins, where concentrations were estimated based on categorical immunohistochemistry scores, and with assumed localization of target within the interstitial space of each organ. Kinetics of target-mAb binding and target turnover, in the presence or absence of mAb, were implemented. The model was then employed to predict concentration versus time data, via Monte Carlo simulation, for two mAb that have been shown to exhibit TMD (2F8 and tocilizumab). Model predictions, performed a priori with no parameter fitting, were found to provide good prediction of dose-dependencies in plasma clearance, the areas under plasma concentration versu time curves, and the time-course of plasma concentration data. This PBPK model may find utility in predicting plasma and tissue concentration versus time data and, potentially, the time-course of receptor occupancy (i.e., mAb-target binding) to support the design and interpretation of preclinical pharmacokinetic-pharmacodynamic investigations in non-human primates.

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application

PubMed Central

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-01-01

Background microRNAs (miRNAs) are single-stranded RNA molecules of about 20–23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. Results GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. Conclusion GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA. PMID:19534746

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

PubMed

Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-06-16

microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

PubMed

Lim, Hansaim; Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; Xie, Lei

2016-10-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

PubMed Central

Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; Meng, Patrick; Xie, Lei

2016-01-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP. PMID:27716836

Non-Solenoidal Startup Research Directions on the Pegasus Toroidal Experiment

NASA Astrophysics Data System (ADS)

Fonck, R. J.; Bongard, M. W.; Lewicki, B. T.; Reusch, J. A.; Winz, G. R.

2017-10-01

The Pegasus research program has been focused on developing a physical understanding and predictive models for non-solenoidal tokamak plasma startup using Local Helicity Injection (LHI). LHI employs strong localized electron currents injected along magnetic field lines in the plasma edge that relax through magnetic turbulence to form a tokamak-like plasma. Pending approval, the Pegasus program will address a broader, more comprehensive examination of non-solenoidal tokamak startup techniques. New capabilities may include: increasing the toroidal field to 0.6 T to support critical scaling tests to near-NSTX-U field levels; deploying internal plasma diagnostics; installing a coaxial helicity injection (CHI) capability in the upper divertor region; and deploying a modest (200-400 kW) electron cyclotron RF capability. These efforts will address scaling of relevant physics to higher BT, separate and comparative studies of helicity injection techniques, efficiency of handoff to consequent current sustainment techniques, and the use of ECH to synergistically improve the target plasma for consequent bootstrap and neutral beam current drive sustainment. This has an ultimate goal of validating techniques to produce a 1 MA target plasma in NSTX-U and beyond. Work supported by US DOE Grant DE-FG02-96ER54375.

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

PubMed

Tichy, Diana; Pickl, Julia Maria Anna; Benner, Axel; Sültmann, Holger

2017-03-31

The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Dynamic Management of Releases for the Delaware River Basin using NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

Weiss, W.; Wang, L.; Murphy, T.; Muralidhar, D.; Tarrier, B.

2011-12-01

The New York City Department of Environmental Protection (DEP) has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. Using an interim version of OST, DEP and the New York State Department of Environmental Conservation (DEC) have developed a provisional, one-year Delaware River Basin reservoir release program to succeed the existing Flexible Flow Management Program (FFMP) which expired on May 31, 2011. The FFMP grew out of the Good Faith Agreement of 1983 among the four Basin states (New York, New Jersey, Pennsylvania, and Delaware) that established modified diversions and flow targets during drought conditions. It provided a set of release schedules as a framework for managing diversions and releases from New York City's Delaware Basin reservoirs in order to support multiple objectives, including water supply, drought mitigation, flood mitigation, tailwaters fisheries, main stem habitat, recreation, and salinity repulsion. The provisional program (OST-FFMP) defines available water based on current Upper Delaware reservoir conditions and probabilistic forecasts of reservoir inflow. Releases are then set based on a set of release schedules keyed to the water availability. Additionally, OST-FFMP attempts to provide enhanced downstream flood protection by making spill mitigation releases to keep the Delaware System reservoirs at a seasonally varying conditional storage objective. The OST-FFMP approach represents a more robust way of managing downstream releases, accounting for predicted future hydrologic conditions by making more water available for release when conditions are forecasted to be wet and protecting water supply reliability when conditions are forecasted to be dry. Further, the dynamic nature of the program allows the release decision to be adjusted as hydrologic conditions change. OST simulations predict that this program can provide substantial benefits for downstream stakeholders while protecting DEP's ability to ensure a reliable water supply for 9 million customers in NYC and the surrounding communities. The one-year nature of the program will allow for DEP and the Decree Parties to evaluate and improve the program in the future. This paper will describe the OST-FFMP program and discuss preliminary observations on its performance based on key NYC and downstream stakeholder performance metrics.

Docking-based classification models for exploratory toxicology ...

EPA Pesticide Factsheets

Background: Exploratory toxicology is a new emerging research area whose ultimate mission is that of protecting human health and environment from risks posed by chemicals. In this regard, the ethical and practical limitation of animal testing has encouraged the promotion of computational methods for the fast screening of huge collections of chemicals available on the market. Results: We derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals having high quality experimental data, kindly provided by the U.S. Environmental Protection Agency (EPA). The predictive power of our docking-based models was supported by values of AUC, EF1% (EFmax = 7.1), -LR (at SE = 0.75) and +LR (at SE = 0.25) ranging from 0.63 to 0.72, from 2.5 to 6.2, from 0.35 to 0.67 and from 2.05 to 9.84, respectively. In addition, external predictions were successfully made on some representative known estrogenic chemicals. Conclusion: We show how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Importantly, these methods enable one to employ the physicochemical information contained in the X-ray solved biological target and to screen structurally-unrelated chemicals. Shows how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Evaluation of 24 reliable dockin

Computational methods for a three-dimensional model of the petroleum-discovery process

USGS Publications Warehouse

Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

1980-01-01

A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

In silico identification of genetic variants in glucocerebrosidase (GBA) gene involved in Gaucher's disease using multiple software tools.

PubMed

Manickam, Madhumathi; Ravanan, Palaniyandi; Singh, Pratibha; Talwar, Priti

2014-01-01

Gaucher's disease (GD) is an autosomal recessive disorder caused by the deficiency of glucocerebrosidase, a lysosomal enzyme that catalyses the hydrolysis of the glycolipid glucocerebroside to ceramide and glucose. Polymorphisms in GBA gene have been associated with the development of Gaucher disease. We hypothesize that prediction of SNPs using multiple state of the art software tools will help in increasing the confidence in identification of SNPs involved in GD. Enzyme replacement therapy is the only option for GD. Our goal is to use several state of art SNP algorithms to predict/address harmful SNPs using comparative studies. In this study seven different algorithms (SIFT, MutPred, nsSNP Analyzer, PANTHER, PMUT, PROVEAN, and SNPs&GO) were used to predict the harmful polymorphisms. Among the seven programs, SIFT found 47 nsSNPs as deleterious, MutPred found 46 nsSNPs as harmful. nsSNP Analyzer program found 43 out of 47 nsSNPs are disease causing SNPs whereas PANTHER found 32 out of 47 as highly deleterious, 22 out of 47 are classified as pathological mutations by PMUT, 44 out of 47 were predicted to be deleterious by PROVEAN server, all 47 shows the disease related mutations by SNPs&GO. Twenty two nsSNPs were commonly predicted by all the seven different algorithms. The common 22 targeted mutations are F251L, C342G, W312C, P415R, R463C, D127V, A309V, G46E, G202E, P391L, Y363C, Y205C, W378C, I402T, S366R, F397S, Y418C, P401L, G195E, W184R, R48W, and T43R.

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.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions.

PubMed

Kaufman, Leyla V; Wright, Mark G

2017-07-07

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions

PubMed Central

Kaufman, Leyla V.; Wright, Mark G.

2017-01-01

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments. PMID:28686180

Molecular classification of gastric cancer.

PubMed

Röcken, Christoph

2017-03-01

Gastric cancer is among the most common cancers worldwide. Despite declining incidences, the prognosis remains dismal in Western countries and is better in Asian countries with national cancer screening programs. Complete endoscopic or surgical resection of the primary tumor with or without lymphadenectomy offers the only chance of cure in the early stage of the disease. Survival of more locally advanced gastric cancers was improved by the introduction of perioperative, adjuvant and palliative chemotherapy. However, the identification and usage of novel predictive and diagnostic targets is urgently needed. Areas covered: Recent comprehensive molecular profiling of gastric cancer proposed four molecular subtypes, i.e. Epstein-Barr virus-associated, microsatellite instable, chromosomal instable and genomically stable carcinomas. The new molecular classification will spur clinical trials exploring novel targeted therapeutics. This review summarizes recent advancements of the molecular classification, and based on that, putative pitfalls for the development of tissue-based companion diagnostics, i.e. prevalence of actionable targets and therapeutic efficacy, tumor heterogeneity and tumor evolution, impact of ethnicity on gastric cancer biology, and standards of care in the East and West. Expert commentary: The overall low prevalence of actionable targets and tumor heterogeneity are the two main obstacles of precision medicine for gastric cancer.

Local contextual processing of abstract and meaningful real-life images in professional athletes.

PubMed

Fogelson, Noa; Fernandez-Del-Olmo, Miguel; Acero, Rafael Martín

2012-05-01

We investigated the effect of abstract versus real-life meaningful images from sports on local contextual processing in two groups of professional athletes. Local context was defined as the occurrence of a short predictive series of stimuli occurring before delivery of a target event. EEG was recorded in 10 professional basketball players and 9 professional athletes of individual sports during three sessions. In each session, a different set of visual stimuli were presented: triangles facing left, up, right, or down; four images of a basketball player throwing a ball; four images of a baseball player pitching a baseball. Stimuli consisted of 15 % targets and 85 % of equal numbers of three types of standards. Recording blocks consisted of targets preceded by randomized sequences of standards and by sequences including a predictive sequence signaling the occurrence of a subsequent target event. Subjects pressed a button in response to targets. In all three sessions, reaction times and peak P3b latencies were shorter for predicted targets compared with random targets, the last most informative stimulus of the predictive sequence induced a robust P3b, and N2 amplitude was larger for random targets compared with predicted targets. P3b and N2 peak amplitudes were larger in the professional basketball group in comparison with professional athletes of individual sports, across the three sessions. The findings of this study suggest that local contextual information is processed similarly for abstract and for meaningful images and that professional basketball players seem to allocate more attentional resources in the processing of these visual stimuli.

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

A Modeling and Experimental Investigation of the Effects of Antigen Density, Binding Affinity, and Antigen Expression Ratio on Bispecific Antibody Binding to Cell Surface Targets*

PubMed Central

Rhoden, John J.; Dyas, Gregory L.

2016-01-01

Despite the increasing number of multivalent antibodies, bispecific antibodies, fusion proteins, and targeted nanoparticles that have been generated and studied, the mechanism of multivalent binding to cell surface targets is not well understood. Here, we describe a conceptual and mathematical model of multivalent antibody binding to cell surface antigens. Our model predicts that properties beyond 1:1 antibody:antigen affinity to target antigens have a strong influence on multivalent binding. Predicted crucial properties include the structure and flexibility of the antibody construct, the target antigen(s) and binding epitope(s), and the density of antigens on the cell surface. For bispecific antibodies, the ratio of the expression levels of the two target antigens is predicted to be critical to target binding, particularly for the lower expressed of the antigens. Using bispecific antibodies of different valencies to cell surface antigens including MET and EGF receptor, we have experimentally validated our modeling approach and its predictions and observed several nonintuitive effects of avidity related to antigen density, target ratio, and antibody affinity. In some biological circumstances, the effect we have predicted and measured varied from the monovalent binding interaction by several orders of magnitude. Moreover, our mathematical framework affords us a mechanistic interpretation of our observations and suggests strategies to achieve the desired antibody-antigen binding goals. These mechanistic insights have implications in antibody engineering and structure/activity relationship determination in a variety of biological contexts. PMID:27022022

Earlier Violent Television Exposure and Later Drug Dependence

PubMed Central

Brook, David W.; Katten, Naomi S.; Ning, Yuming; Brook, Judith S.

2013-01-01

This research examined the longitudinal pathways from earlier violent television exposure to later drug dependence. African American and Puerto Rican adolescents were interviewed during three points in time (N = 463). Violent television exposure in late adolescence predicted violent television exposure in young adulthood, which in turn was related to tobacco/marijuana use, nicotine dependence, and later drug dependence. Some policy and clinical implications suggest: a) regulating the times when violent television is broadcast; b) creating developmentally targeted prevention/treatment programs; and c) recognizing that watching violent television may serve as a cue regarding increased susceptibility to nicotine and drug dependence. PMID:18612881

Application of the AMPLE cluster-and-truncate approach to NMR structures for molecular replacement

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

Bibby, Jaclyn; Keegan, Ronan M.; Mayans, Olga

2013-11-01

Processing of NMR structures for molecular replacement by AMPLE works well. AMPLE is a program developed for clustering and truncating ab initio protein structure predictions into search models for molecular replacement. Here, it is shown that its core cluster-and-truncate methods also work well for processing NMR ensembles into search models. Rosetta remodelling helps to extend success to NMR structures bearing low sequence identity or high structural divergence from the target protein. Potential future routes to improved performance are considered and practical, general guidelines on using AMPLE are provided.

Neural correlates of the spatial and expectancy components of endogenous and stimulus-driven orienting of attention in the Posner task.

PubMed

Doricchi, Fabrizio; Macci, Enrica; Silvetti, Massimo; Macaluso, Emiliano

2010-07-01

Voluntary orienting of visual attention is conventionally measured in tasks with predictive central cues followed by frequent valid targets at the cued location and by infrequent invalid targets at the uncued location. This implies that invalid targets entail both spatial reorienting of attention and breaching of the expected spatial congruency between cues and targets. Here, we used event-related functional magnetic resonance imaging (fMRI) to separate the neural correlates of the spatial and expectancy components of both endogenous orienting and stimulus-driven reorienting of attention. We found that during endogenous orienting with predictive cues, there was a significant deactivation of the right Temporal-Parietal Junction (TPJ). We also discovered that the lack of an equivalent deactivation with nonpredictive cues was matched to drop in attentional costs and preservation of attentional benefits. The right TPJ showed equivalent responses to invalid targets following predictive and nonpredictive cues. On the contrary, infrequent-unexpected invalid targets following predictive cues specifically activated the right Middle and Inferior Frontal Gyrus (MFG-IFG). Additional comparisons with spatially neutral trials demonstrated that, independently of cue predictiveness, valid targets activate the left TPJ, whereas invalid targets activate both the left and right TPJs. These findings show that the selective right TPJ activation that is found in the comparison between invalid and valid trials results from the reciprocal cancelling of the different activations that in the left TPJ are related to the processing of valid and invalid targets. We propose that left and right TPJs provide "matching and mismatching to attentional template" signals. These signals enable reorienting of attention and play a crucial role in the updating of the statistical contingency between cues and targets.

Computational identification of potential multi-drug combinations for reduction of microglial inflammation in Alzheimer disease

PubMed Central

Anastasio, Thomas J.

2015-01-01

Like other neurodegenerative diseases, Alzheimer Disease (AD) has a prominent inflammatory component mediated by brain microglia. Reducing microglial inflammation could potentially halt or at least slow the neurodegenerative process. A major challenge in the development of treatments targeting brain inflammation is the sheer complexity of the molecular mechanisms that determine whether microglia become inflammatory or take on a more neuroprotective phenotype. The process is highly multifactorial, raising the possibility that a multi-target/multi-drug strategy could be more effective than conventional monotherapy. This study takes a computational approach in finding combinations of approved drugs that are potentially more effective than single drugs in reducing microglial inflammation in AD. This novel approach exploits the distinct advantages of two different computer programming languages, one imperative and the other declarative. Existing programs written in both languages implement the same model of microglial behavior, and the input/output relationships of both programs agree with each other and with data on microglia over an extensive test battery. Here the imperative program is used efficiently to screen the model for the most efficacious combinations of 10 drugs, while the declarative program is used to analyze in detail the mechanisms of action of the most efficacious combinations. Of the 1024 possible drug combinations, the simulated screen identifies only 7 that are able to move simulated microglia at least 50% of the way from a neurotoxic to a neuroprotective phenotype. Subsequent analysis shows that of the 7 most efficacious combinations, 2 stand out as superior both in strength and reliability. The model offers many experimentally testable and therapeutically relevant predictions concerning effective drug combinations and their mechanisms of action. PMID:26097457

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.

In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy

PubMed Central

Echigoya, Yusuke; Mouly, Vincent; Garcia, Luis; Yokota, Toshifumi; Duddy, William

2015-01-01

The use of antisense ‘splice-switching’ oligonucleotides to induce exon skipping represents a potential therapeutic approach to various human genetic diseases. It has achieved greatest maturity in exon skipping of the dystrophin transcript in Duchenne muscular dystrophy (DMD), for which several clinical trials are completed or ongoing, and a large body of data exists describing tested oligonucleotides and their efficacy. The rational design of an exon skipping oligonucleotide involves the choice of an antisense sequence, usually between 15 and 32 nucleotides, targeting the exon that is to be skipped. Although parameters describing the target site can be computationally estimated and several have been identified to correlate with efficacy, methods to predict efficacy are limited. Here, an in silico pre-screening approach is proposed, based on predictive statistical modelling. Previous DMD data were compiled together and, for each oligonucleotide, some 60 descriptors were considered. Statistical modelling approaches were applied to derive algorithms that predict exon skipping for a given target site. We confirmed (1) the binding energetics of the oligonucleotide to the RNA, and (2) the distance in bases of the target site from the splice acceptor site, as the two most predictive parameters, and we included these and several other parameters (while discounting many) into an in silico screening process, based on their capacity to predict high or low efficacy in either phosphorodiamidate morpholino oligomers (89% correctly predicted) and/or 2’O Methyl RNA oligonucleotides (76% correctly predicted). Predictions correlated strongly with in vitro testing for sixteen de novo PMO sequences targeting various positions on DMD exons 44 (R2 0.89) and 53 (R2 0.89), one of which represents a potential novel candidate for clinical trials. We provide these algorithms together with a computational tool that facilitates screening to predict exon skipping efficacy at each position of a target exon. PMID:25816009

Ku-Band rendezvous radar performance computer simulation model

NASA Technical Reports Server (NTRS)

Magnusson, H. G.; Goff, M. F.

1984-01-01

All work performed on the Ku-band rendezvous radar performance computer simulation model program since the release of the preliminary final report is summarized. Developments on the program fall into three distinct categories: (1) modifications to the existing Ku-band radar tracking performance computer model; (2) the addition of a highly accurate, nonrealtime search and acquisition performance computer model to the total software package developed on this program; and (3) development of radar cross section (RCS) computation models for three additional satellites. All changes in the tracking model involved improvements in the automatic gain control (AGC) and the radar signal strength (RSS) computer models. Although the search and acquisition computer models were developed under the auspices of the Hughes Aircraft Company Ku-Band Integrated Radar and Communications Subsystem program office, they have been supplied to NASA as part of the Ku-band radar performance comuter model package. Their purpose is to predict Ku-band acquisition performance for specific satellite targets on specific missions. The RCS models were developed for three satellites: the Long Duration Exposure Facility (LDEF) spacecraft, the Solar Maximum Mission (SMM) spacecraft, and the Space Telescopes.

Ku-Band rendezvous radar performance computer simulation model

NASA Astrophysics Data System (ADS)

Magnusson, H. G.; Goff, M. F.

1984-06-01

All work performed on the Ku-band rendezvous radar performance computer simulation model program since the release of the preliminary final report is summarized. Developments on the program fall into three distinct categories: (1) modifications to the existing Ku-band radar tracking performance computer model; (2) the addition of a highly accurate, nonrealtime search and acquisition performance computer model to the total software package developed on this program; and (3) development of radar cross section (RCS) computation models for three additional satellites. All changes in the tracking model involved improvements in the automatic gain control (AGC) and the radar signal strength (RSS) computer models. Although the search and acquisition computer models were developed under the auspices of the Hughes Aircraft Company Ku-Band Integrated Radar and Communications Subsystem program office, they have been supplied to NASA as part of the Ku-band radar performance comuter model package. Their purpose is to predict Ku-band acquisition performance for specific satellite targets on specific missions. The RCS models were developed for three satellites: the Long Duration Exposure Facility (LDEF) spacecraft, the Solar Maximum Mission (SMM) spacecraft, and the Space Telescopes.

Dramatic Enhancement of Genome Editing by CRISPR/Cas9 Through Improved Guide RNA Design

PubMed Central

Farboud, Behnom; Meyer, Barbara J.

2015-01-01

Success with genome editing by the RNA-programmed nuclease Cas9 has been limited by the inability to predict effective guide RNAs and DNA target sites. Not all guide RNAs have been successful, and even those that were, varied widely in their efficacy. Here we describe and validate a strategy for Caenorhabditis elegans that reliably achieved a high frequency of genome editing for all targets tested in vivo. The key innovation was to design guide RNAs with a GG motif at the 3′ end of their target-specific sequences. All guides designed using this simple principle induced a high frequency of targeted mutagenesis via nonhomologous end joining (NHEJ) and a high frequency of precise DNA integration from exogenous DNA templates via homology-directed repair (HDR). Related guide RNAs having the GG motif shifted by only three nucleotides showed severely reduced or no genome editing. We also combined the 3′ GG guide improvement with a co-CRISPR/co-conversion approach. For this co-conversion scheme, animals were only screened for genome editing at designated targets if they exhibited a dominant phenotype caused by Cas9-dependent editing of an unrelated target. Combining the two strategies further enhanced the ease of mutant recovery, thereby providing a powerful means to obtain desired genetic changes in an otherwise unaltered genome. PMID:25695951

Designing Ligands for Leishmania, Plasmodium, and Aspergillus N-Myristoyl Transferase with Specificity and Anti-Target-Safe Virtual Libraries.

PubMed

Garcia-Sosa, Alfonso T

2018-01-01

Leishmaniasis, malaria, and fungal diseases are burdens on individuals and populations and can present severe complications. Easily accessible chemical treatments for these diseases are increasingly sought-after. Targeting the parasite N-myristoyl transferase while avoiding the human enzyme and other anti-targets may allow the prospect of compounds with pan-activity against these diseases, which would simplify treatments and costs. Developing chemical libraries, both virtual and physical, that have been filtered and flagged early on in the drug discovery process (before virtual screening) could reduce attrition rates of compounds being developed and failing late in development stages due to problems of side-effects or toxicity. Chemical libraries have been screened against the anti-targets pregnane-X-receptor, sulfotransferase, cytochrome P450 2a6, 2c9, and 3a4 with three different docking programs. Statistically significant differences are observed in their interactions with these enzymes as compared to small molecule drugs and bioactive non-drug datasets. A series of compounds are proposed with the best predicted profiles for inhibition of all parasite targets while sparing the human form and anti-targets. Some of the topranked compounds have confirmed experimental activity against Leishmania, and highlighted are those compounds with best properties for further development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A computer program to determine the possible daily release window for sky target experiments

NASA Technical Reports Server (NTRS)

Michaud, N. H.

1973-01-01

A computer program is presented which is designed to determine the daily release window for sky target experiments. Factors considered in the program include: (1) target illumination by the sun at release time and during the tracking period; (2) look angle elevation above local horizon from each tracking station to the target; (3) solar depression angle from the local horizon of each tracking station during the experimental period after target release; (4) lunar depression angle from the local horizon of each tracking station during the experimental period after target release; and (5) total sky background brightness as seen from each tracking station while viewing the target. Program output is produced in both graphic and data form. Output data can be plotted for a single calendar month or year. The numerical values used to generate the plots are furnished to permit a more detailed review of the computed daily release windows.

Involuntary orienting of attention to a sound desynchronizes the occipital alpha rhythm and improves visual perception.

PubMed

Feng, Wenfeng; Störmer, Viola S; Martinez, Antigona; McDonald, John J; Hillyard, Steven A

2017-04-15

Directing attention voluntarily to the location of a visual target results in an amplitude reduction (desynchronization) of the occipital alpha rhythm (8-14Hz), which is predictive of improved perceptual processing of the target. Here we investigated whether modulations of the occipital alpha rhythm triggered by the involuntary orienting of attention to a salient but spatially non-predictive sound would similarly influence perception of a subsequent visual target. Target discrimination was more accurate when a sound preceded the target at the same location (validly cued trials) than when the sound was on the side opposite to the target (invalidly cued trials). This behavioral effect was accompanied by a sound-induced desynchronization of the alpha rhythm over the lateral occipital scalp. The magnitude of alpha desynchronization over the hemisphere contralateral to the sound predicted correct discriminations of validly cued targets but not of invalidly cued targets. These results support the conclusion that cue-induced alpha desynchronization over the occipital cortex is a manifestation of a general priming mechanism that improves visual processing and that this mechanism can be activated either by the voluntary or involuntary orienting of attention. Further, the observed pattern of alpha modulations preceding correct and incorrect discriminations of valid and invalid targets suggests that involuntary orienting to the non-predictive sound has a rapid and purely facilitatory influence on processing targets on the cued side, with no inhibitory influence on targets on the opposite side. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictors of Anastomotic Leak in Elderly Patients After Colectomy: Nomogram-Based Assessment From the American College of Surgeons National Surgical Quality Program Procedure-Targeted Cohort.

PubMed

Rencuzogullari, Ahmet; Benlice, Cigdem; Valente, Michael; Abbas, Maher A; Remzi, Feza H; Gorgun, Emre

2017-05-01

Elderly patients undergoing colorectal surgery have increasingly become under scrutiny by accounting for the largest fraction of geriatric postoperative deaths and a significant proportion of all postoperative complications, including anastomotic leak. This study aimed to determine predictors of anastomotic leak in elderly patients undergoing colectomy by creating a novel nomogram for simplistic prediction of anastomotic leak risk in a given patient. This study was a retrospective review. The database review of the American College of Surgeons National Surgical Quality Improvement Program was conducted at a single institution. Patients aged ≥65 years who underwent elective segmental colectomy with an anastomosis at different levels (abdominal or low pelvic) in 2012-2013 were identified from the multi-institutional procedure-targeted database. We constructed a stepwise multiple logistic regression model for anastomotic leak as an outcome; predictors were selected in a stepwise fashion using the Akaike information criterion. The validity of the nomogram was externally tested on elderly patients (≥65 years of age) from the 2014 American College of Surgeons National Surgical Quality Improvement Program colectomy-targeted database. A total of 10,392 patients were analyzed, and anastomotic leak occurred in 332 (3.2%). Of the patients who developed anastomotic leak, 192 (57.8%) were men (p < 0.001). Based on unadjusted analysis, factors associated with an increased risk of anastomotic leak were ASA score III and IV (p < 0.001), chronic obstructive pulmonary disease (p = 0.004), diabetes mellitus (p = 0.003), smoking history (p = 0.014), weight loss (p = 0.013), previously infected wound (p = 0.005), omitting mechanical bowel preparation (p = 0.005) and/or preoperative oral antibiotic use (p < 0.001), and wounds classified as contaminated or dirty/infected (p = 0.008). Patients who developed anastomotic leak had a longer length of hospital stay (17 vs 7 d; p < 0.001) and operative time (191 vs 162 min; p < 0.001). A multivariate model and nomogram were created. This study was limited by its retrospective nature and short-term follow-up (30 d). An accurate prediction of anastomotic leak affecting morbidity and mortality after colorectal surgery using the proposed nomogram may facilitate decision making in elderly patients for healthcare providers.

The motivation to express prejudice.

PubMed

Forscher, Patrick S; Cox, William T L; Graetz, Nicholas; Devine, Patricia G

2015-11-01

Contemporary prejudice research focuses primarily on people who are motivated to respond without prejudice and the ways in which unintentional bias can cause these people to act in a manner inconsistent with this motivation. However, some real-world phenomena (e.g., hate speech, hate crimes) and experimental findings (e.g., Plant & Devine, 2001, 2009) suggest that some prejudice is intentional. These phenomena and findings are difficult to explain solely from the motivations to respond without prejudice. We argue that some people are motivated to express prejudice, and we develop the Motivation to Express Prejudice Scale (MP) to measure this motivation. In 7 studies involving more than 6,000 participants, we demonstrate that, across scale versions targeted at Black people and gay men, the MP has good reliability and convergent, discriminant, and predictive validity. In normative climates that prohibit prejudice, the internal and external motivations to express prejudice are functionally nonindependent, but they become more independent when normative climates permit more prejudice toward a target group. People high in the motivation to express prejudice are relatively likely to resist pressure to support programs promoting intergroup contact and to vote for political candidates who support oppressive policies. The motivation to express prejudice predicted these outcomes even when controlling for attitudes and the motivations to respond without prejudice. This work encourages contemporary prejudice researchers to give greater consideration to the intentional aspects of negative intergroup behavior and to broaden the range of phenomena, target groups, and samples that they study. (c) 2015 APA, all rights reserved).

The motivation to express prejudice

PubMed Central

Forscher, Patrick S.; Cox, William T. L.; Graetz, Nicholas; Devine, Patricia G.

2015-01-01

Contemporary prejudice research focuses primarily on people who are motivated to respond without prejudice and the ways in which unintentional bias can cause these people to act inconsistent with this motivation. However, some real-world phenomena (e.g., hate speech, hate crimes) and experimental findings (e.g., Plant & Devine, 2001; 2009) suggest that some expressions of prejudice are intentional. These phenomena and findings are difficult to explain solely from the motivations to respond without prejudice. We argue that some people are motivated to express prejudice, and we develop the motivation to express prejudice (MP) scale to measure this motivation. In seven studies involving more than 6,000 participants, we demonstrate that, across scale versions targeted at Black people and gay men, the MP scale has good reliability and convergent, discriminant, and predictive validity. In normative climates that prohibit prejudice, the internal and external motivations to express prejudice are functionally non-independent, but they become more independent when normative climates permit more prejudice toward a target group. People high in the motivation to express prejudice are relatively likely to resist pressure to support programs promoting intergroup contact and vote for political candidates who support oppressive policies. The motivation to express prejudice predicted these outcomes even when controlling for attitudes and the motivations to respond without prejudice. This work encourages contemporary prejudice researchers to broaden the range of samples, target groups, and phenomena that they study, and more generally to consider the intentional aspects of negative intergroup behavior. PMID:26479365

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

PubMed

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

2014-01-01

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

A Universal Intervention Program Increases Ethnic-Racial Identity Exploration and Resolution to Predict Adolescent Psychosocial Functioning One Year Later.

PubMed

Umaña-Taylor, Adriana J; Kornienko, Olga; Douglass Bayless, Sara; Updegraff, Kimberly A

2018-01-01

Ethnic-racial identity formation represents a key developmental task that is especially salient during adolescence and has been associated with many indices of positive adjustment. The Identity Project intervention, which targeted ethnic-racial identity exploration and resolution, was designed based on the theory that program-induced changes in ethnic-racial identity would lead to better psychosocial adjustment (e.g., global identity cohesion, self-esteem, mental health, academic achievement). Adolescents (N =215; Mage =15.02, SD =.68; 50% female) participated in a small-scale randomized control trial with an attention control group. A cascading mediation model was tested using pre-test and three follow-up assessments (12, 18, and 67 weeks after baseline). The program led to increases in exploration, subsequent increases in resolution and, in turn, higher global identity cohesion, higher self-esteem, lower depressive symptoms, and better grades. Results support the notion that increasing adolescents' ethnic-racial identity can promote positive psychosocial functioning among youth.

Polarized electron beams elastically scattered by atoms as a tool for testing fundamental predictions of quantum mechanics.

PubMed

Dapor, Maurizio

2018-03-29

Quantum information theory deals with quantum noise in order to protect physical quantum bits (qubits) from its effects. A single electron is an emblematic example of a qubit, and today it is possible to experimentally produce polarized ensembles of electrons. In this paper, the theory of the polarization of electron beams elastically scattered by atoms is briefly summarized. Then the POLARe program suite, a set of computer programs aimed at the calculation of the spin-polarization parameters of electron beams elastically interacting with atomic targets, is described. Selected results of the program concerning Ar, Kr, and Xe atoms are presented together with the comparison with experimental data about the Sherman function for low kinetic energy of the incident electrons (1.5eV-350eV). It is demonstrated that the quantum-relativistic theory of the polarization of electron beams elastically scattered by atoms is in good agreement with experimental data down to energies smaller than a few eV.

Comparing biomarker measurements to a normal range: when ...

EPA Pesticide Factsheets

This commentary is the second of a series outlining one specific concept in interpreting biomarkers data. In the first, an observational method was presented for assessing the distribution of measurements before making parametric calculations. Here, the discussion revolves around the next step, the choice of using standard error of the mean or the calculated standard deviation to compare or predict measurement results. The National Exposure Research Laboratory’s (NERL’s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA’s mission to protect human health and the environment. HEASD’s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA’s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.

Arctic Research NASA's Cryospheric Sciences Program

NASA Technical Reports Server (NTRS)

Waleed, Abdalati; Zukor, Dorothy J. (Technical Monitor)

2001-01-01

Much of NASA's Arctic Research is run through its Cryospheric Sciences Program. Arctic research efforts to date have focused primarily on investigations of the mass balance of the largest Arctic land-ice masses and the mechanisms that control it, interactions among sea ice, polar oceans, and the polar atmosphere, atmospheric processes in the polar regions, energy exchanges in the Arctic. All of these efforts have been focused on characterizing, understanding, and predicting, changes in the Arctic. NASA's unique vantage from space provides an important perspective for the study of these large scale processes, while detailed process information is obtained through targeted in situ field and airborne campaigns and models. An overview of NASA investigations in the Arctic will be presented demonstrating how the synthesis of space-based technology, and these complementary components have advanced our understanding of physical processes in the Arctic.

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

Automatic priming of attentional control by relevant colors.

PubMed

Ansorge, Ulrich; Becker, Stefanie I

2012-01-01

We tested whether color word cues automatically primed attentional control settings during visual search, or whether color words were used in a strategic manner for the control of attention. In Experiment 1, we used color words as cues that were informative or uninformative with respect to the target color. Regardless of the cue's informativeness, distractors similar to the color cue captured more attention. In Experiment 2, the participants either indicated their expectation about the target color or recalled the last target color, which was uncorrelated with the present target color. We observed more attentional capture by distractors that were similar to the participants' predictions and recollections, but no difference between effects of the recollected and predicted colors. In Experiment 3, we used 100%-informative word cues that were congruent with the predicted target color (e.g., the word "red" informed that the target would be red) or incongruent with the predicted target color (e.g., the word "green" informed that the target would be red) and found that informative incongruent word cues primed attention capture by a word-similar distractor. Together, the results suggest that word cues (Exps. 1 and 3) and color representations (Exp. 2) primed attention capture in an automatic manner. This indicates that color cues automatically primed temporary adjustments in attention control settings.

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.

A quick reality check for microRNA target prediction.

PubMed

Kast, Juergen

2011-04-01

The regulation of protein abundance by microRNA (miRNA)-mediated repression of mRNA translation is a rapidly growing area of interest in biochemical research. In animal cells, the miRNA seed sequence does not perfectly match that of the mRNA it targets, resulting in a large number of possible miRNA targets and varied extents of repression. Several software tools are available for the prediction of miRNA targets, yet the overlap between them is limited. Jovanovic et al. have developed and applied a targeted, quantitative approach to validate predicted miRNA target proteins. Using a proteome database, they have set up and tested selected reaction monitoring assays for approximately 20% of more than 800 predicted let-7 targets, as well as control genes in Caenorhabditis elegans. Their results demonstrate that such assays can be developed quickly and with relative ease, and applied in a high-throughput setup to verify known and identify novel miRNA targets. They also show, however, that the choice of the biological system and material has a noticeable influence on the frequency, extent and direction of the observed changes. Nonetheless, selected reaction monitoring assays, such as those developed by Jovanovic et al., represent an attractive new tool in the study of miRNA function at the organism level.

Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

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

Down-Regulation of MicroRNA-210 Confers Sensitivity towards 1’S-1’-Acetoxychavicol Acetate (ACA) in Cervical Cancer Cells by Targeting SMAD4

PubMed Central

Phuah, Neoh Hun; Azmi, Mohamad Nurul; Awang, Khalijah; Nagoor, Noor Hasima

2017-01-01

MicroRNAs (miRNAs) are short non-coding RNAs that regulate genes posttranscriptionally. Past studies have reported that miR-210 is up-regulated in many cancers including cervical cancer, and plays a pleiotropic role in carcinogenesis. However, its role in regulating response towards anti-cancer agents has not been fully elucidated. We have previously reported that the natural compound 1’S-1’-acetoxychavicol acetate (ACA) is able to induce cytotoxicity in various cancer cells including cervical cancer cells. Hence, this study aims to investigate the mechanistic role of miR-210 in regulating response towards ACA in cervical cancer cells. In the present study, we found that ACA down-regulated miR-210 expression in cervical cancer cells, and suppression of miR-210 expression enhanced sensitivity towards ACA by inhibiting cell proliferation and promoting apoptosis. Western blot analysis showed increased expression of mothers against decapentaplegic homolog 4 (SMAD4), which was predicted as a target of miR-210 by target prediction programs, following treatment with ACA. Luciferase reporter assay confirmed that miR-210 binds to sequences in 3′UTR of SMAD4. Furthermore, decreased in SMAD4 protein expression was observed when miR-210 was overexpressed. Conversely, SMAD4 protein expression increased when miR-210 expression was suppressed. Lastly, we demonstrated that overexpression of SMAD4 augmented the anti-proliferative and apoptosis-inducing effects of ACA. Taken together, our results demonstrated that down-regulation of miR-210 conferred sensitivity towards ACA in cervical cancer cells by targeting SMAD4. These findings suggest that combination of miRNAs and natural compounds could provide new strategies in treating cervical cancer. PMID:28401751

Down-Regulation of MicroRNA-210 Confers Sensitivity towards 1'S-1'-Acetoxychavicol Acetate (ACA) in Cervical Cancer Cells by Targeting SMAD4.

PubMed

Phuah, Neoh Hun; Azmi, Mohamad Nurul; Awang, Khalijah; Nagoor, Noor Hasima

2017-04-01

MicroRNAs (miRNAs) are short non-coding RNAs that regulate genes posttranscriptionally. Past studies have reported that miR-210 is up-regulated in many cancers including cervical cancer, and plays a pleiotropic role in carcinogenesis. However, its role in regulating response towards anti-cancer agents has not been fully elucidated. We have previously reported that the natural compound 1'S-1'-acetoxychavicol acetate (ACA) is able to induce cytotoxicity in various cancer cells including cervical cancer cells. Hence, this study aims to investigate the mechanistic role of miR-210 in regulating response towards ACA in cervical cancer cells. In the present study, we found that ACA down-regulated miR-210 expression in cervical cancer cells, and suppression of miR-210 expression enhanced sensitivity towards ACA by inhibiting cell proliferation and promoting apoptosis. Western blot analysis showed increased expression of mothers against decapentaplegic homolog 4 (SMAD4), which was predicted as a target of miR-210 by target prediction programs, following treatment with ACA. Luciferase reporter assay confirmed that miR-210 binds to sequences in 3'UTR of SMAD4. Furthermore, decreased in SMAD4 protein expression was observed when miR-210 was overexpressed. Conversely, SMAD4 protein expression increased when miR-210 expression was suppressed. Lastly, we demonstrated that overexpression of SMAD4 augmented the anti-proliferative and apoptosis-inducing effects of ACA. Taken together, our results demonstrated that down-regulation of miR-210 conferred sensitivity towards ACA in cervical cancer cells by targeting SMAD4. These findings suggest that combination of miRNAs and natural compounds could provide new strategies in treating cervical cancer.

Evidence That Up-Regulation of MicroRNA-29 Contributes to Postnatal Body Growth Deceleration

PubMed Central

Kamran, Fariha; Andrade, Anenisia C.; Nella, Aikaterini A.; Clokie, Samuel J.; Rezvani, Geoffrey; Nilsson, Ola; Baron, Jeffrey

2015-01-01

Body growth is rapid in infancy but subsequently slows and eventually ceases due to a progressive decline in cell proliferation that occurs simultaneously in multiple organs. We previously showed that this decline in proliferation is driven in part by postnatal down-regulation of a large set of growth-promoting genes in multiple organs. We hypothesized that this growth-limiting genetic program is orchestrated by microRNAs (miRNAs). Bioinformatic analysis identified target sequences of the miR-29 family of miRNAs to be overrepresented in age–down-regulated genes. Concomitantly, expression microarray analysis in mouse kidney and lung showed that all members of the miR-29 family, miR-29a, -b, and -c, were strongly up-regulated from 1 to 6 weeks of age. Real-time PCR confirmed that miR-29a, -b, and -c were up-regulated with age in liver, kidney, lung, and heart, and their expression levels were higher in hepatocytes isolated from 5-week-old mice than in hepatocytes from embryonic mouse liver at embryonic day 16.5. We next focused on 3 predicted miR-29 target genes (Igf1, Imp1, and Mest), all of which are growth-promoting. A 3′-untranslated region containing the predicted target sequences from each gene was placed individually in a luciferase reporter construct. Transfection of miR-29 mimics suppressed luciferase gene activity for all 3 genes, and this suppression was diminished by mutating the target sequences, suggesting that these genes are indeed regulated by miR-29. Taken together, the findings suggest that up-regulation of miR-29 during juvenile life drives the down-regulation of multiple growth-promoting genes, thus contributing to physiological slowing and eventual cessation of body growth. PMID:25866874

Evidence That Up-Regulation of MicroRNA-29 Contributes to Postnatal Body Growth Deceleration.

PubMed

Kamran, Fariha; Andrade, Anenisia C; Nella, Aikaterini A; Clokie, Samuel J; Rezvani, Geoffrey; Nilsson, Ola; Baron, Jeffrey; Lui, Julian C

2015-06-01

Body growth is rapid in infancy but subsequently slows and eventually ceases due to a progressive decline in cell proliferation that occurs simultaneously in multiple organs. We previously showed that this decline in proliferation is driven in part by postnatal down-regulation of a large set of growth-promoting genes in multiple organs. We hypothesized that this growth-limiting genetic program is orchestrated by microRNAs (miRNAs). Bioinformatic analysis identified target sequences of the miR-29 family of miRNAs to be overrepresented in age-down-regulated genes. Concomitantly, expression microarray analysis in mouse kidney and lung showed that all members of the miR-29 family, miR-29a, -b, and -c, were strongly up-regulated from 1 to 6 weeks of age. Real-time PCR confirmed that miR-29a, -b, and -c were up-regulated with age in liver, kidney, lung, and heart, and their expression levels were higher in hepatocytes isolated from 5-week-old mice than in hepatocytes from embryonic mouse liver at embryonic day 16.5. We next focused on 3 predicted miR-29 target genes (Igf1, Imp1, and Mest), all of which are growth-promoting. A 3'-untranslated region containing the predicted target sequences from each gene was placed individually in a luciferase reporter construct. Transfection of miR-29 mimics suppressed luciferase gene activity for all 3 genes, and this suppression was diminished by mutating the target sequences, suggesting that these genes are indeed regulated by miR-29. Taken together, the findings suggest that up-regulation of miR-29 during juvenile life drives the down-regulation of multiple growth-promoting genes, thus contributing to physiological slowing and eventual cessation of body growth.

Target-motion prediction for robotic search and rescue in wilderness environments.

PubMed

Macwan, Ashish; Nejat, Goldie; Benhabib, Beno

2011-10-01

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as terrain topology, target physiology and psychology, clues found, etc. The isoprobability curves are propagated over time and space. The significant tangible benefit of the proposed target-motion prediction methodology is demonstrated through a comparison to a nonprobabilistic approach, as well as through a simulated realistic wilderness search scenario.

Some of the most interesting CASP11 targets through the eyes of their authors.

PubMed

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K; Edwards, Robert A; Fass, Deborah; Hartmann, Marcus D; Korycinski, Mateusz; Lewis, Richard J; Lorimer, Donald; Lupas, Andrei N; Newman, Janet; Peat, Thomas S; Piepenbrink, Kurt H; Prahlad, Janani; van Raaij, Mark J; Rohwer, Forest; Segall, Anca M; Seguritan, Victor; Sundberg, Eric J; Singh, Abhimanyu K; Wilson, Mark A; Schwede, Torsten

2016-09-01

The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34-50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

The Neural Correlates of Inhibiting Pursuit to Smoothly Moving Targets

ERIC Educational Resources Information Center

Burke, Melanie Rose; Barnes, Graham R.

2011-01-01

A previous study has shown that actively pursuing a moving target provides a predictive motor advantage when compared with passive observation of the moving target while keeping the eyes still [Burke, M. R., & Barnes, G. R. Anticipatory eye movements evoked after active following versus passive observation of a predictable motion stimulus. "Brain…

ERP evidence for selective drop in attentional costs in uncertain environments: challenging a purely premotor account of covert orienting of attention.

PubMed

Lasaponara, Stefano; Chica, Ana B; Lecce, Francesca; Lupianez, Juan; Doricchi, Fabrizio

2011-07-01

Several studies have proved that the reliability of endogenous spatial cues linearly modulates the reaction time advantage in the processing of targets at validly cued vs. invalidly cued locations, i.e. the "validity effect". This would imply that with non-predictive cues, no "validity effect" should be observed. However, contrary to this prediction, one could hypothesize that attentional benefits by valid cuing (i.e. the RT advantage for validly vs. neutrally cued targets) can still be maintained with non-predictive cues, if the brain were endowed with mechanisms allowing the selective reduction in costs of reorienting from invalidly cued locations (i.e. the reduction of the RT disadvantage for invalidly vs. neutrally cued targets). This separated modulation of attentional benefits and costs would be adaptive in uncertain contexts where cues predict at chance level the location of targets. Through the joint recording of manual reaction times and event-related cerebral potentials (ERPs), we have found that this is the case and that relying on non-predictive endogenous cues results in abatement of attentional costs and the difference in the amplitude of the P1 brain responses evoked by invalidly vs. neutrally cued targets. In contrast, the use of non-predictive cues leaves unaffected attentional benefits and the difference in the amplitude of the N1 responses evoked by validly vs. neutrally cued targets. At the individual level, the drop in costs with non-predictive cues was matched with equivalent lateral biases in RTs to neutrally and invalidly cued targets presented in the left and right visual field. During the cue period, the drop in costs with non-predictive cues was preceded by reduction of the Early Directing Attention Negativity (EDAN) on posterior occipital sites and by enhancement of the frontal Anterior Directing Attention Negativity (ADAN) correlated to preparatory voluntary orienting. These findings demonstrate, for the first time, that the segregation of mechanisms regulating attentional benefits and costs helps efficiency of orienting in "uncertain" visual spatial contexts characterized by poor probabilistic association between cues and targets. Copyright © 2011 Elsevier Ltd. All rights reserved.

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug-centric repositioning scenarios. BRDTI Software and supplementary materials are available online at www.ksi.mff.cuni.cz/∼peska/BRDTI. Copyright © 2017 Elsevier B.V. All rights reserved.

[The vaccination coverage rate: why is it so low?].

PubMed

Wembonyama, O

1994-01-01

The problems hampering vaccination programs in Zaire include the inaccessibility of vaccination posts, the deplorable condition of vaccines and supplies, transport difficulties, and community disinterest. Most vaccination posts in Zaire are physically inaccessible and poorly stocked. They lack skilled staff and are unable to provide quality care. They do not have the means of providing themselves with vaccine; shortages are so common that vaccination schedules are difficult to follow. Refrigerators are usually not available in vaccination centers and are often diverted to other uses if they are available. The instructions for storing vaccines are often incorrectly followed. Single-use needles and syringes continue to be reused. Vehicles assigned to vaccination programs are often used for the private benefit of program officials or their families. Misuse of vehicles contributes to their short life expectancy. Local communities are disinterested in vaccination programs because they do not contribute to immediate survival. Moreover, the population regularly experiences the death of correctly vaccinated children. Some persons distrust vaccination as a trick to render women sterile or cause fever and convulsions in children. Mass vaccination programs are so poorly organized that their failure is predictable. The officials in charge spend most of their time in their offices rather than getting to know the target populations, and are often more interested in publicity for themselves than in the program. Press coverage is indispensable, but it should be devoted to furthering the program and not the careers of the officials in charge. Training of vaccinators, stocking of vaccination posts, and other essential tasks are often left until the last minute and improvised rather than carefully planned and implemented. The vaccinators are often unemployed persons who have little knowledge of correct techniques. Vaccination coverage could be improved if planners and health officials would acquaint themselves with the target communities, their health problems, and their perceptions of the vaccination program. Vaccination posts, hours of operation, and date of vaccination programs should be carefully planned to ensure that they are accessible to the population. The community should be informed about the program and motivated to participate. The logistics should be carefully worked out, and the vaccinators should be trained well in advance of the campaign.

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

Engaging Mexican Origin Families in a School-Based Preventive Intervention

PubMed Central

Mauricio, Anne M.; Gonzales, Nancy A.; Millsap, Roger E.; Meza, Connie M.; Dumka, Larry E.; Germán, Miguelina; Genalo, M. Toni

2009-01-01

This study describes a culturally sensitive approach to engage Mexican origin families in a school-based, family-focused preventive intervention trial. The approach was evaluated via assessing study enrollment and intervention program participation, as well as examining predictors of engagement at each stage. Incorporating traditional cultural values into all aspects of engagement resulted in participation rates higher than reported rates of minority-focused trials not emphasizing cultural sensitivity. Family preferred language (English or Spanish) or acculturation status predicted engagement at all levels, with less acculturated families participating at higher rates. Spanish-language families with less acculturated adolescents participated at higher rates than Spanish-language families with more acculturated adolescents. Other findings included two-way interactions between family language and the target child’s familism values, family single- vs. dual-parent status, and number of hours the primary parent worked in predicting intervention participation. Editors’ Strategic Implications: The authors present a promising approach—which requires replication—to engaging and retaining Mexican American families in a school-based prevention program. The research also highlights the importance of considering acculturation status when implementing and studying culturally tailored aspects of prevention models. PMID:18004659

Hip fracture incidence 2003-2013 and projected cases until 2050 in Austria: a population-based study.

PubMed

Concin, Hans; Brozek, Wolfgang; Benedetto, Karl-Peter; Häfele, Hartmut; Kopf, Joachim; Bärenzung, Thomas; Schnetzer, Richard; Schenk, Christian; Stimpfl, Elmar; Waheed-Hutter, Ursula; Ulmer, Hanno; Rapp, Kilian; Zwettler, Elisabeth; Nagel, Gabriele

2016-12-01

Elevated hip fracture incidence is a major public health problem looming to aggravate in industrialized countries due to demographic developments. We report hip fracture incidence and expected future cases from Vorarlberg, the westernmost province of Austria, results potentially representative of Central European populations. Crude and standardized hip fracture incidence rates in Vorarlberg 2003-2013 are reported. Based on the age-specific incidence in 2013 or trends 2003-2013, we predict hip fractures till 2050. Female age-standardized hip fracture incidence decreased 2005-2013, whereas for men, the trend was rather unclear. Uncorrected forecasts indicate that by 2050, female and male cases will each have more than doubled from 2015 in all demographic core scenarios. Corrected by incidence trends before 2013, cases are expected to drop among women but rise among men. We anticipate rising hip fracture numbers in Vorarlberg within the next decades, unless prevention programs that presumably account for decreasing incidence rates, particularly among women since 2005, take further effect to counteract the predicted steady increase due to demographic changes. Concomitantly, augmented endeavors to target the male population by these programs are needed.

[PD-L1 expression: An emerging biomarker in non-small cell lung cancer].

PubMed

Adam, Julien; Planchard, David; Marabelle, Aurélien; Soria, Jean-Charles; Scoazec, Jean-Yves; Lantuéjoul, Sylvie

2016-01-01

Therapies targeting immune checkpoints, in particular programmed death 1 (PD-1) and its ligand programmed death ligand 1 (PD-L1), are major new strategies for the treatment of several malignancies including mestatatic non-small cell lung cancer (NSCLC). The identification of predictive biomarkers of response is required, considering efficacy, cost and potential adverse events. Expression of PD-L1 by immunohistochemistry has been associated with higher response rate and overall survival in several clinical trials evaluating anti-PD-1 and anti-PD-L1 monoclonal antibodies. Thus, PD-L1 immunohistochemical companion assays could be required for treatment with some of these therapies in NSCLC. However, heterogeneity in methodologies of PD-L1 assays in terms of primary antibodies and scoring algorithms, and tumor heterogenity for PD-L1 expression are important issues to be considered. More studies are required to compare the different assays, ensure their harmonization and standardization and identify the optimal conditions for testing. PD-L1 expression is likely an imperfect predictive biomarker for patient selection and association with other markers of the tumor immune microenvironment will be probably necessary in the future. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

DXRaySMCS: a user-friendly interface developed for prediction of diagnostic radiology X-ray spectra produced by Monte Carlo (MCNP-4C) simulation.

PubMed

Bahreyni Toossi, M T; Moradi, H; Zare, H

2008-01-01

In this work, the general purpose Monte Carlo N-particle radiation transport computer code (MCNP-4C) was used for the simulation of X-ray spectra in diagnostic radiology. The electron's path in the target was followed until its energy was reduced to 10 keV. A user-friendly interface named 'diagnostic X-ray spectra by Monte Carlo simulation (DXRaySMCS)' was developed to facilitate the application of MCNP-4C code for diagnostic radiology spectrum prediction. The program provides a user-friendly interface for: (i) modifying the MCNP input file, (ii) launching the MCNP program to simulate electron and photon transport and (iii) processing the MCNP output file to yield a summary of the results (relative photon number per energy bin). In this article, the development and characteristics of DXRaySMCS are outlined. As part of the validation process, output spectra for 46 diagnostic radiology system settings produced by DXRaySMCS were compared with the corresponding IPEM78. Generally, there is a good agreement between the two sets of spectra. No statistically significant differences have been observed between IPEM78 reported spectra and the simulated spectra generated in this study.

Connecting students to institutions: the relationship between program resources and student retention in respiratory care education programs.

PubMed

Ari, Arzu

2009-09-01

Respiratory care education programs are being held accountable for student retention. Increasing student retention is necessary for the respiratory therapy profession, which suffers from a shortage of qualified therapists needed to meet the increased demand. The present study investigated the relationship between student retention rate and program resources, in order to understand which and to what extent the different components of program resources predict student retention rate. The target population of this study was baccalaureate of science degree respiratory care education programs. After utilizing a survey research method, Pearson correlations and multiple regression analysis were used for data analysis. With a 63% response rate (n = 36), this study found a statistically significant relationship between program resources and student retention rate. Financial and personnel resources had a statistically significant positive relationship with student retention. The mean financial resources per student was responsible for 33% of the variance in student retention, while the mean personnel resources per student accounted for 12% of the variance in student retention. Program financial resources available to students was the single best predictor of program performance on student retention. Respiratory care education programs spending more money per student and utilizing more personnel in the program have higher mean performance in student retention. Therefore, respiratory care education programs must devote sufficient resources to retaining students so that they can produce more respiratory therapists and thereby make the respiratory therapy profession stronger.

Image-based numerical modeling of HIFU-induced lesions

NASA Astrophysics Data System (ADS)

Almekkaway, Mohamed K.; Shehata, Islam A.; Haritonova, Alyona; Ballard, John; Casper, Andrew; Ebbini, Emad

2017-03-01

Atherosclerosis is a chronic vascular disease affecting large and medium sized arteries. Several treatment options are already available for treatment of this disease. Targeting atherosclerotic plaques by high intensity focused ultrasound (HIFU) using dual mode ultrasound arrays (DMUA) was recently introduced in literature. We present a finite difference time domain (FDTD) simulation modeling of the wave propagation in heterogeneous medium from the surface of a 3.5 MHz array prototype with 32-elements. After segmentation of the ultrasound image obtained for the treatment region in-vivo, we integrated this anatomical information into our simulation to account for different parameters that may be caused by these multi-region anatomical complexities. The simulation program showed that HIFU was able to induce damage in the prefocal region instead of the target area. The HIFU lesions, as predicted by our simulation, were well correlated with the actual damage detected in histology.

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

Application of the Athlete's Performance Passport for Doping Control: A Case Report.

PubMed

Iljukov, Sergei; Bermon, Stephane; Schumacher, Yorck O

2018-01-01

The efficient use of testing resources is a key issue in the fight against doping. The longitudinal tracking of sporting performances to identify unusual improvements possibly caused by doping, so-called "athlete's performance passport" (APP) is a new concept to improve targeted anti-doping testing. In fact, unusual performances by an athlete would trigger a more thorough testing program. In the present case report, performance data is modeled using the critical power concept for a group of athletes based on their past performances. By these means, an athlete with unusual deviations from his predicted performances was identified. Subsequent target testing using blood testing and the athlete biological passport resulted in an anti-doping rule violation procedure and suspension of the athlete. This case demonstrates the feasibility of the APP approach where athlete's performance is monitored and might serve as an example for the practical implementation of the method.

Application of the Athlete's Performance Passport for Doping Control: A Case Report

PubMed Central

Iljukov, Sergei; Bermon, Stephane; Schumacher, Yorck O.

2018-01-01

The efficient use of testing resources is a key issue in the fight against doping. The longitudinal tracking of sporting performances to identify unusual improvements possibly caused by doping, so-called “athlete's performance passport” (APP) is a new concept to improve targeted anti-doping testing. In fact, unusual performances by an athlete would trigger a more thorough testing program. In the present case report, performance data is modeled using the critical power concept for a group of athletes based on their past performances. By these means, an athlete with unusual deviations from his predicted performances was identified. Subsequent target testing using blood testing and the athlete biological passport resulted in an anti-doping rule violation procedure and suspension of the athlete. This case demonstrates the feasibility of the APP approach where athlete's performance is monitored and might serve as an example for the practical implementation of the method. PMID:29651247

The 150 most important questions in cancer research and clinical oncology series: Questions 25-30 : Edited by Chinese Journal of Cancer.

PubMed

2017-05-04

To accelerate our endeavors to overcome cancer, Chinese Journal of Cancer has launched a program of publishing 150 most important questions in cancer research and clinical oncology. In this article, 6 more questions are presented as followed. Question 25: Does imprinting of immune responses to infections early in life predict future risk of childhood and adult cancers? Question 26: How to induce homogeneous tumor antigen expression in a heterogeneous tumor mass to enhance the efficacy of cancer immunotherapy? Question 27: Could we enhance the therapeutic effects of immunotherapy by targeting multiple tumor antigens simultaneously or sequentially? Question 28: Can immuno-targeting to cytokines halt cancer metastasis? Question 29: How can we dynamically and less-invasively monitor the activity of CD8 + T killer cells at tumor sites and draining lymph nodes? Question 30: How can the immune system destroy the niches for cancer initiation?

Predicting the Noise of High Power Fluid Targets Using Computational Fluid Dynamics

NASA Astrophysics Data System (ADS)

Moore, Michael; Covrig Dusa, Silviu

The 2.5 kW liquid hydrogen (LH2) target used in the Qweak parity violation experiment is the highest power LH2 target in the world and the first to be designed with Computational Fluid Dynamics (CFD) at Jefferson Lab. The Qweak experiment determined the weak charge of the proton by measuring the parity-violating elastic scattering asymmetry of longitudinally polarized electrons from unpolarized liquid hydrogen at small momentum transfer (Q2 = 0 . 025 GeV2). This target satisfied the design goals of < 1 % luminosity reduction and < 5 % contribution to the total asymmetry width (the Qweak target achieved 2 % or 55ppm). State of the art time dependent CFD simulations are being developed to improve the predictions of target noise on the time scale of the electron beam helicity period. These predictions will be bench-marked with the Qweak target data. This work is an essential component in future designs of very high power low noise targets like MOLLER (5 kW, target noise asymmetry contribution < 25 ppm) and MESA (4.5 kW).

Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.

PubMed

Cheng, Feixiong; Li, Weihua; Wu, Zengrui; Wang, Xichuan; Zhang, Chen; Li, Jie; Liu, Guixia; Tang, Yun

2013-04-22

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

Bioinformatics prediction of siRNAs as potential antiviral agents against dengue viruses

PubMed Central

Villegas-Rosales, Paula M; Méndez-Tenorio, Alfonso; Ortega-Soto, Elizabeth; Barrón, Blanca L

2012-01-01

Dengue virus (DENV 1-4) represents the major emerging arthropod-borne viral infection in the world. Currently, there is neither an available vaccine nor a specific treatment. Hence, there is a need of antiviral drugs for these viral infections; we describe the prediction of short interfering RNA (siRNA) as potential therapeutic agents against the four DENV serotypes. Our strategy was to carry out a series of multiple alignments using ClustalX program to find conserved sequences among the four DENV serotype genomes to obtain a consensus sequence for siRNAs design. A highly conserved sequence among the four DENV serotypes, located in the encoding sequence for NS4B and NS5 proteins was found. A total of 2,893 complete DENV genomes were downloaded from the NCBI, and after a depuration procedure to identify identical sequences, 220 complete DENV genomes were left. They were edited to select the NS4B and NS5 sequences, which were aligned to obtain a consensus sequence. Three different servers were used for siRNA design, and the resulting siRNAs were aligned to identify the most prevalent sequences. Three siRNAs were chosen, one targeted the genome region that codifies for NS4B protein and the other two; the region for NS5 protein. Predicted secondary structure for DENV genomes was used to demonstrate that the siRNAs were able to target the viral genome forming double stranded structures, necessary to activate the RNA silencing machinery. PMID:22829722

A chemogenomic analysis of the human proteome: application to enzyme families.

PubMed

Bernasconi, Paul; Chen, Min; Galasinski, Scott; Popa-Burke, Ioana; Bobasheva, Anna; Coudurier, Louis; Birkos, Steve; Hallam, Rhonda; Janzen, William P

2007-10-01

Sequence-based phylogenies (SBP) are well-established tools for describing relationships between proteins. They have been used extensively to predict the behavior and sensitivity toward inhibitors of enzymes within a family. The utility of this approach diminishes when comparing proteins with little sequence homology. Even within an enzyme family, SBPs must be complemented by an orthogonal method that is independent of sequence to better predict enzymatic behavior. A chemogenomic approach is demonstrated here that uses the inhibition profile of a 130,000 diverse molecule library to uncover relationships within a set of enzymes. The profile is used to construct a semimetric additive distance matrix. This matrix, in turn, defines a sequence-independent phylogeny (SIP). The method was applied to 97 enzymes (kinases, proteases, and phosphatases). SIP does not use structural information from the molecules used for establishing the profile, thus providing a more heuristic method than the current approaches, which require knowledge of the specific inhibitor's structure. Within enzyme families, SIP shows a good overall correlation with SBP. More interestingly, SIP uncovers distances within families that are not recognizable by sequence-based methods. In addition, SIP allows the determination of distance between enzymes with no sequence homology, thus uncovering novel relationships not predicted by SBP. This chemogenomic approach, used in conjunction with SBP, should prove to be a powerful tool for choosing target combinations for drug discovery programs as well as for guiding the selection of profiling and liability targets.

PL-PatchSurfer: a novel molecular local surface-based method for exploring protein-ligand interactions.

PubMed

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G; Kihara, Daisuke

2014-08-27

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets.

PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions

PubMed Central

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G.; Kihara, Daisuke

2014-01-01

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. PMID:25167137

Scaling up towards international targets for AIDS, tuberculosis, and malaria: contribution of global fund-supported programs in 2011-2015.

PubMed

Katz, Itamar; Komatsu, Ryuichi; Low-Beer, Daniel; Atun, Rifat

2011-02-23

The paper projects the contribution to 2011-2015 international targets of three major pandemics by programs in 140 countries funded by the Global Fund to Fight AIDS, Tuberculosis and Malaria, the largest external financier of tuberculosis and malaria programs and a major external funder of HIV programs in low and middle income countries. Estimates, using past trends, for the period 2011-2015 of the number of persons receiving antiretroviral (ARV) treatment, tuberculosis case detection using the internationally approved DOTS strategy, and insecticide-treated nets (ITNs) to be delivered by programs in low and middle income countries supported by the Global Fund compared to international targets established by UNAIDS, Stop TB Partnership, Roll Back Malaria Partnership and the World Health Organisation. Global Fund-supported programs are projected to provide ARV treatment to 5.5-5.8 million people, providing 30%-31% of the 2015 international target. Investments in tuberculosis and malaria control will enable reaching in 2015 60%-63% of the international target for tuberculosis case detection and 30%-35% of the ITN distribution target in sub-Saharan Africa. Global Fund investments will substantially contribute to the achievement by 2015 of international targets for HIV, TB and malaria. However, additional large scale international and domestic financing is needed if these targets are to be reached by 2015.

Communicating with nonindustrial private forest-land owners: Getting programs on target

Treesearch

Thomas W. Birch; Nancy A. Pywell; Nancy A. Pywell

1986-01-01

Nonindustrial private forest-land owners can be motivated 1, by programs directed to their needs and objectives. Seven target audiences in Pennsylvania were defined and outlets for information programs identified for each target I audience. The primary objectives of each group and the benefits they expect from owning forest land were considered in the preparation of...

Metrics to assess injury prevention programs for young workers in high-risk occupations: a scoping review of the literature.

PubMed

Jennifer, Smith; Purewal, Birinder Praneet; Macpherson, Alison; Pike, Ian

2018-05-01

Despite legal protections for young workers in Canada, youth aged 15-24 are at high risk of traumatic occupational injury. While many injury prevention initiatives targeting young workers exist, the challenge faced by youth advocates and employers is deciding what aspect(s) of prevention will be the most effective focus for their efforts. A review of the academic and grey literatures was undertaken to compile the metrics-both the indicators being evaluated and the methods of measurement-commonly used to assess injury prevention programs for young workers. Metrics are standards of measurement through which efficiency, performance, progress, or quality of a plan, process, or product can be assessed. A PICO framework was used to develop search terms. Medline, PubMed, OVID, EMBASE, CCOHS, PsychINFO, CINAHL, NIOSHTIC, Google Scholar and the grey literature were searched for articles in English, published between 1975-2015. Two independent reviewers screened the resulting list and categorized the metrics in three domains of injury prevention: Education, Environment and Enforcement. Of 174 acquired articles meeting the inclusion criteria, 21 both described and assessed an intervention. Half were educational in nature (N=11). Commonly assessed metrics included: knowledge, perceptions, self-reported behaviours or intentions, hazardous exposures, injury claims, and injury counts. One study outlined a method for developing metrics to predict injury rates. Metrics specific to the evaluation of young worker injury prevention programs are needed, as current metrics are insufficient to predict reduced injuries following program implementation. One study, which the review brought to light, could be an appropriate model for future research to develop valid leading metrics specific to young workers, and then apply these metrics to injury prevention programs for youth.

A research program in empirical computer science

NASA Technical Reports Server (NTRS)

Knight, J. C.

1991-01-01

During the grant reporting period our primary activities have been to begin preparation for the establishment of a research program in experimental computer science. The focus of research in this program will be safety-critical systems. Many questions that arise in the effort to improve software dependability can only be addressed empirically. For example, there is no way to predict the performance of the various proposed approaches to building fault-tolerant software. Performance models, though valuable, are parameterized and cannot be used to make quantitative predictions without experimental determination of underlying distributions. In the past, experimentation has been able to shed some light on the practical benefits and limitations of software fault tolerance. It is common, also, for experimentation to reveal new questions or new aspects of problems that were previously unknown. A good example is the Consistent Comparison Problem that was revealed by experimentation and subsequently studied in depth. The result was a clear understanding of a previously unknown problem with software fault tolerance. The purpose of a research program in empirical computer science is to perform controlled experiments in the area of real-time, embedded control systems. The goal of the various experiments will be to determine better approaches to the construction of the software for computing systems that have to be relied upon. As such it will validate research concepts from other sources, provide new research results, and facilitate the transition of research results from concepts to practical procedures that can be applied with low risk to NASA flight projects. The target of experimentation will be the production software development activities undertaken by any organization prepared to contribute to the research program. Experimental goals, procedures, data analysis and result reporting will be performed for the most part by the University of Virginia.

Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program

PubMed Central

Kiernan, Michaela; Moore, Susan D.; Schoffman, Danielle E.; Lee, Katherine; King, Abby C.; Taylor, C. Barr; Kiernan, Nancy Ellen; Perri, Michael G.

2015-01-01

Social support could be a powerful weight-loss treatment moderator or mediator but is rarely assessed. We assessed the psychometric properties, initial levels, and predictive validity of a measure of perceived social support and sabotage from friends and family for healthy eating and physical activity (eight subscales). Overweight/obese women randomized to one of two 6-month, group-based behavioral weight-loss programs (N=267; mean BMI 32.1±3.5; 66.3% White) completed subscales at baseline, and weight loss was assessed at 6 months. Internal consistency, discriminant validity, and content validity were excellent for support subscales and adequate for sabotage subscales; qualitative responses revealed novel deliberate instances not reflected in current sabotage items. Most women (>75%) “never” or “rarely” experienced support from friends or family. Using non-parametric classification methods, we identified two subscales—support from friends for healthy eating and support from family for physical activity—that predicted three clinically meaningful subgroups who ranged in likelihood of losing ≥5% of initial weight at 6 months. Women who “never” experienced family support were least likely to lose weight (45.7% lost weight) whereas women who experienced both frequent friend and family support were more likely to lose weight (71.6% lost weight). Paradoxically, women who “never” experienced friend support were most likely to lose weight (80.0% lost weight), perhaps because the group-based programs provided support lacking from friendships. Psychometrics for support subscales were excellent; initial support was rare; and the differential roles of friend versus family support could inform future targeted weight-loss interventions to subgroups at risk. PMID:21996661

Can We Predict Those With Osteoarthritis Who Will Worsen Following a Chronic Disease Management Program?

PubMed

Eyles, Jillian P; Mills, Kathryn; Lucas, Barbara R; Williams, Matthew J; Makovey, Joanna; Teoh, Laurence; Hunter, David J

2016-09-01

To identify predictors of worsening symptoms and overall health of the treated hip or knee joint following 26 weeks of a nonsurgical chronic disease management program for hip and knee osteoarthritis (OA) and to examine the consistency of these predictors across 3 definitions of worsening. This prospective cohort study followed 539 participants of the program for 26 weeks. The 3 definitions of worsening included symptomatic worsening based on change in the Western Ontario and McMaster Universities Osteoarthritis Index Global score (WOMAC-G) measuring pain, stiffness, and function; a transition scale that asked about overall health of the treated hip or knee joint; and a composite outcome including both. Multivariate logistic regression models were constructed for the 3 definitions of worsening. Complete data were available for 386 participants: mean age was 66.3 years, 69% were female, 85% reported knee joint pain as primary symptom (signal joint), 46% were waitlisted for total joint arthroplasty (TJA). TJA waitlist status, signal joint, 6-Minute Walk Test (6MWT), depressive symptoms, pain, and age were independently associated with at least 1 definition of worsening. TJA waitlist status and 6MWT remained in the multivariate models for the transition and composite definitions of worsening. Participants reporting worsening on the transition scale did not consistently meet the WOMAC-G definition of worsening symptoms. TJA waitlist status was predictive of the composite definition of worsening, a trend apparent for the transition definition. However, variables that predict worsening remain largely unknown. Further research is required to direct comprehensive and targeted management of patients with hip and knee OA. © 2016, American College of Rheumatology.

Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.

PubMed

Michael, Edwin; Singh, Brajendra K; Mayala, Benjamin K; Smith, Morgan E; Hampton, Scott; Nabrzyski, Jaroslaw

2017-09-27

There are growing demands for predicting the prospects of achieving the global elimination of neglected tropical diseases as a result of the institution of large-scale nation-wide intervention programs by the WHO-set target year of 2020. Such predictions will be uncertain due to the impacts that spatial heterogeneity and scaling effects will have on parasite transmission processes, which will introduce significant aggregation errors into any attempt aiming to predict the outcomes of interventions at the broader spatial levels relevant to policy making. We describe a modeling platform that addresses this problem of upscaling from local settings to facilitate predictions at regional levels by the discovery and use of locality-specific transmission models, and we illustrate the utility of using this approach to evaluate the prospects for eliminating the vector-borne disease, lymphatic filariasis (LF), in sub-Saharan Africa by the WHO target year of 2020 using currently applied or newly proposed intervention strategies. METHODS AND RESULTS: We show how a computational platform that couples site-specific data discovery with model fitting and calibration can allow both learning of local LF transmission models and simulations of the impact of interventions that take a fuller account of the fine-scale heterogeneous transmission of this parasitic disease within endemic countries. We highlight how such a spatially hierarchical modeling tool that incorporates actual data regarding the roll-out of national drug treatment programs and spatial variability in infection patterns into the modeling process can produce more realistic predictions of timelines to LF elimination at coarse spatial scales, ranging from district to country to continental levels. Our results show that when locally applicable extinction thresholds are used, only three countries are likely to meet the goal of LF elimination by 2020 using currently applied mass drug treatments, and that switching to more intensive drug regimens, increasing the frequency of treatments, or switching to new triple drug regimens will be required if LF elimination is to be accelerated in Africa. The proportion of countries that would meet the goal of eliminating LF by 2020 may, however, reach up to 24/36 if the WHO 1% microfilaremia prevalence threshold is used and sequential mass drug deliveries are applied in countries. We have developed and applied a data-driven spatially hierarchical computational platform that uses the discovery of locally applicable transmission models in order to predict the prospects for eliminating the macroparasitic disease, LF, at the coarser country level in sub-Saharan Africa. We show that fine-scale spatial heterogeneity in local parasite transmission and extinction dynamics, as well as the exact nature of intervention roll-outs in countries, will impact the timelines to achieving national LF elimination on this continent.

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

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.

Predicting selective drug targets in cancer through metabolic networks

PubMed Central

Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

2011-01-01

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

Targets of opportunity : community based alcohol programs

DOT National Transportation Integrated Search

1988-04-01

Targets of Opportunity (TOP), were comprehensive community based programs addressing the drinking and driving concerns within a particular community. The program incorporated six elements: 1) General deterrence - public information,leducation and enf...

Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation

PubMed Central

Doench, John G.; Hartenian, Ella; Graham, Daniel B.; Tothova, Zuzana; Hegde, Mudra; Smith, Ian; Sullender, Meagan; Ebert, Benjamin L.; Xavier, Ramnik J.; Root, David E.

2014-01-01

Components of the prokaryotic clustered regularly interspersed palindromic repeat (CRISPR) loci have recently been repurposed for use in mammalian cells1–6. The Cas9 protein can be programmed with a single guide RNA (sgRNA) to generate site-specific DNA breaks, but there are few known rules governing on-target efficacy of this system7,8. We created a pool of sgRNAs, tiling across all possible target sites of a panel of six endogenous mouse and three endogenous human genes and quantitatively assessed their ability to produce null alleles of their target gene by antibody staining and flow cytometry. We discovered sequence features that improved activity, including a further optimization of the proto-spacer adjacent motif (PAM) of Streptococcus pyogenes Cas9. The results from 1,841 sgRNAs were used to construct a predictive model of sgRNA activity to improve sgRNA design for gene editing and genetic screens. We provide an online tool for the design of highly active sgRNAs for any gene of interest. PMID:25184501

Targeted human papillomavirus vaccination for young men who have sex with men in Australia yields significant population benefits and is cost-effective.

PubMed

Zhang, Lei; Regan, David G; Ong, Jason J; Gambhir, Manoj; Chow, Eric P F; Zou, Huachun; Law, Matthew; Hocking, Jane; Fairley, Christopher K

2017-09-05

We investigated the effectiveness and cost-effectiveness of a targeted human papillomavirus (HPV) vaccination program for young (15-26) men who have sex with men (MSM). We developed a compartmental model to project HPV epidemic trajectories in MSM for three vaccination scenarios: a boys program, a targeted program for young MSM only and the combination of the two over 2017-2036. We assessed the gain in quality-adjusted-life-years (QALY) in 190,000 Australian MSM. A targeted program for young MSM only that achieved 20% coverage per year, without a boys program, will prevent 49,283 (31,253-71,500) cases of anogenital warts, 191 (88-319) person-years living with anal cancer through 2017-2036 but will only stablise anal cancer incidence. In contrast, a boys program will prevent 82,056 (52,100-117,164) cases of anogenital warts, 447 (204-725) person-years living with anal cancers through 2017-2036 and see major declines in anal cancer. This can reduce 90% low- and high-risk HPV in young MSM by 2024 and 2032, respectively, but will require vaccinating ≥84% of boys. Adding a targeted program for young MSM to an existing boys program would prevent an additional 14,912 (8479-21,803) anogenital wart and 91 (42-152) person-years living with anal cancer. In combination with a boys' program, a catch-up program for young MSM will cost an additional $AUD 6788 ($4628-11,989) per QALY gained, but delaying its implementation reduced its cost-effectiveness. A boys program that achieved coverage of about 84% will result in a 90% reduction in HPV. A targeted program for young MSM is cost-effective if timely implemented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

A Modeling and Experimental Investigation of the Effects of Antigen Density, Binding Affinity, and Antigen Expression Ratio on Bispecific Antibody Binding to Cell Surface Targets.

PubMed

Rhoden, John J; Dyas, Gregory L; Wroblewski, Victor J

2016-05-20

Despite the increasing number of multivalent antibodies, bispecific antibodies, fusion proteins, and targeted nanoparticles that have been generated and studied, the mechanism of multivalent binding to cell surface targets is not well understood. Here, we describe a conceptual and mathematical model of multivalent antibody binding to cell surface antigens. Our model predicts that properties beyond 1:1 antibody:antigen affinity to target antigens have a strong influence on multivalent binding. Predicted crucial properties include the structure and flexibility of the antibody construct, the target antigen(s) and binding epitope(s), and the density of antigens on the cell surface. For bispecific antibodies, the ratio of the expression levels of the two target antigens is predicted to be critical to target binding, particularly for the lower expressed of the antigens. Using bispecific antibodies of different valencies to cell surface antigens including MET and EGF receptor, we have experimentally validated our modeling approach and its predictions and observed several nonintuitive effects of avidity related to antigen density, target ratio, and antibody affinity. In some biological circumstances, the effect we have predicted and measured varied from the monovalent binding interaction by several orders of magnitude. Moreover, our mathematical framework affords us a mechanistic interpretation of our observations and suggests strategies to achieve the desired antibody-antigen binding goals. These mechanistic insights have implications in antibody engineering and structure/activity relationship determination in a variety of biological contexts. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

45 CFR 400.75 - Registration for employment services, participation in employability service programs and...

Code of Federal Regulations, 2010 CFR

2010-10-01

..., participation in employability service programs and targeted assistance programs, going to job interviews, and... service programs and targeted assistance programs, going to job interviews, and acceptance of appropriate... part. (2) Go to a job interview which is arranged by the State agency or its designee. (3) Accept at...

Research of maneuvering target prediction and tracking technology based on IMM algorithm

NASA Astrophysics Data System (ADS)

Cao, Zheng; Mao, Yao; Deng, Chao; Liu, Qiong; Chen, Jing

2016-09-01

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms.

Predicting spillover risk to non-target plants pre-release: Bikasha collaris a potential biological control agent of Chinese tallowtree (Triadica sebifera)

USDA-ARS?s Scientific Manuscript database

Quarantine host range tests accurately predict direct risk of biological control agents to non-target species. However, a well-known indirect effect of biological control of weeds releases is spillover damage to non-target species. Spillover damage may occur when the population of agents achieves ou...