Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

PubMed Central

Sahaï, Aïsha; Pacherie, Elisabeth; Grynszpan, Ouriel; Berberian, Bruno

2017-01-01

Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents. PMID:29081744

DIANA-microT web server: elucidating microRNA functions through target prediction.

PubMed

Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

2009-07-01

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Hierarchical Ensemble Methods for Protein Function Prediction

PubMed Central

2014-01-01

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

A Prediction Model for Functional Outcomes in Spinal Cord Disorder Patients Using Gaussian Process Regression.

PubMed

Lee, Sunghoon Ivan; Mortazavi, Bobak; Hoffman, Haydn A; Lu, Derek S; Li, Charles; Paak, Brian H; Garst, Jordan H; Razaghy, Mehrdad; Espinal, Marie; Park, Eunjeong; Lu, Daniel C; Sarrafzadeh, Majid

2016-01-01

Predicting the functional outcomes of spinal cord disorder patients after medical treatments, such as a surgical operation, has always been of great interest. Accurate posttreatment prediction is especially beneficial for clinicians, patients, care givers, and therapists. This paper introduces a prediction method for postoperative functional outcomes by a novel use of Gaussian process regression. The proposed method specifically considers the restricted value range of the target variables by modeling the Gaussian process based on a truncated Normal distribution, which significantly improves the prediction results. The prediction has been made in assistance with target tracking examinations using a highly portable and inexpensive handgrip device, which greatly contributes to the prediction performance. The proposed method has been validated through a dataset collected from a clinical cohort pilot involving 15 patients with cervical spinal cord disorder. The results show that the proposed method can accurately predict postoperative functional outcomes, Oswestry disability index and target tracking scores, based on the patient's preoperative information with a mean absolute error of 0.079 and 0.014 (out of 1.0), respectively.

Automated prediction of protein function and detection of functional sites from structure.

PubMed

Pazos, Florencio; Sternberg, Michael J E

2004-10-12

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

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

He, Zhili; Zhang, Ping; Wu, Linwei

Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminantsmore » would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. Here, this study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.« less

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

PubMed Central

Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

2018-01-01

ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

DOE PAGES

He, Zhili; Zhang, Ping; Wu, Linwei; ...

2018-02-20

Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminantsmore » would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. Here, this study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.« less

Origin and Functional Prediction of Pollen Allergens in Plants.

PubMed

Chen, Miaolin; Xu, Jie; Devis, Deborah; Shi, Jianxin; Ren, Kang; Searle, Iain; Zhang, Dabing

2016-09-01

Pollen allergies have long been a major pandemic health problem for human. However, the evolutionary events and biological function of pollen allergens in plants remain largely unknown. Here, we report the genome-wide prediction of pollen allergens and their biological function in the dicotyledonous model plant Arabidopsis (Arabidopsis thaliana) and the monocotyledonous model plant rice (Oryza sativa). In total, 145 and 107 pollen allergens were predicted from rice and Arabidopsis, respectively. These pollen allergens are putatively involved in stress responses and metabolic processes such as cell wall metabolism during pollen development. Interestingly, these putative pollen allergen genes were derived from large gene families and became diversified during evolution. Sequence analysis across 25 plant species from green alga to angiosperms suggest that about 40% of putative pollen allergenic proteins existed in both lower and higher plants, while other allergens emerged during evolution. Although a high proportion of gene duplication has been observed among allergen-coding genes, our data show that these genes might have undergone purifying selection during evolution. We also observed that epitopes of an allergen might have a biological function, as revealed by comprehensive analysis of two known allergens, expansin and profilin. This implies a crucial role of conserved amino acid residues in both in planta biological function and allergenicity. Finally, a model explaining how pollen allergens were generated and maintained in plants is proposed. Prediction and systematic analysis of pollen allergens in model plants suggest that pollen allergens were evolved by gene duplication and then functional specification. This study provides insight into the phylogenetic and evolutionary scenario of pollen allergens that will be helpful to future characterization and epitope screening of pollen allergens. © 2016 American Society of Plant Biologists. All rights reserved.

Network control principles predict neuron function in the Caenorhabditis elegans connectome

PubMed Central

Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-01-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social and technological networks1–3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode C. elegans4–6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires twelve neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7–13, as well as one previously uncharacterised neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed, with single-cell ablations of DD04 or DD05, but not DD02 or DD03, specifically affecting posterior body movements. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterised connectomes. PMID:29045391

Network control principles predict neuron function in the Caenorhabditis elegans connectome

NASA Astrophysics Data System (ADS)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-10-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectome.

PubMed

Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

2017-10-26

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Extensive complementarity between gene function prediction methods.

PubMed

Vidulin, Vedrana; Šmuc, Tomislav; Supek, Fran

2016-12-01

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them. The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials 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.

PREDICTION OF NONLINEAR SPATIAL FUNCTIONALS. (R827257)

EPA Science Inventory

Spatial statistical methodology can be useful in the arena of environmental regulation. Some regulatory questions may be addressed by predicting linear functionals of the underlying signal, but other questions may require the prediction of nonlinear functionals of the signal. ...

A predictive index of axillary nodal involvement in operable breast cancer.

PubMed Central

De Laurentiis, M.; Gallo, C.; De Placido, S.; Perrone, F.; Pettinato, G.; Petrella, G.; Carlomagno, C.; Panico, L.; Delrio, P.; Bianco, A. R.

1996-01-01

We investigated the association between pathological characteristics of primary breast cancer and degree of axillary nodal involvement and obtained a predictive index of the latter from the former. In 2076 cases, 17 histological features, including primary tumour and local invasion variables, were recorded. The whole sample was randomly split in a training (75% of cases) and a test sample. Simple and multiple correspondence analysis were used to select the variables to enter in a multinomial logit model to build an index predictive of the degree of nodal involvement. The response variable was axillary nodal status coded in four classes (N0, N1-3, N4-9, N > or = 10). The predictive index was then evaluated by testing goodness-of-fit and classification accuracy. Covariates significantly associated with nodal status were tumour size (P < 0.0001), tumour type (P < 0.0001), type of border (P = 0.048), multicentricity (P = 0.003), invasion of lymphatic and blood vessels (P < 0.0001) and nipple invasion (P = 0.006). Goodness-of-fit was validated by high concordance between observed and expected number of cases in each decile of predicted probability in both training and test samples. Classification accuracy analysis showed that true node-positive cases were well recognised (84.5%), but there was no clear distinction among the classes of node-positive cases. However, 10 year survival analysis showed a superimposible prognostic behaviour between predicted and observed nodal classes. Moreover, misclassified node-negative patients (i.e. those who are predicted positive) showed an outcome closer to patients with 1-3 metastatic nodes than to node-negative ones. In conclusion, the index cannot completely substitute for axillary node information, but it is a predictor of prognosis as accurate as nodal involvement and identifies a subgroup of node-negative patients with unfavourable prognosis. PMID:8630286

Prediction of Research Self-Efficacy and Future Research Involvement.

ERIC Educational Resources Information Center

Bishop, Rosean M.; And Others

Although graduate programs hope that their students will be committed to research in their careers, most students express ambivalence towards research. Identifying the variables that predict involvement in research thus seems crucial. In this study 136 doctoral students from a wide range of disciplines completed the Research Self-Efficacy Scale…

A traveling salesman approach for predicting protein functions.

PubMed

Johnson, Olin; Liu, Jing

2006-10-12

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

A traveling salesman approach for predicting protein functions

PubMed Central

Johnson, Olin; Liu, Jing

2006-01-01

Background Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Results Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm [1] on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Conclusion Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems. PMID:17147783

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

PredictProtein—an open resource for online prediction of protein structural and functional features

PubMed Central

Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

2014-01-01

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

Roles for text mining in protein function prediction.

PubMed

Verspoor, Karin M

2014-01-01

The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers.

PubMed

Lefcheck, Jonathan S; Duffy, J Emmett

2015-11-01

The use of functional traits to explain how biodiversity affects ecosystem functioning has attracted intense interest, yet few studies have a priori altered functional diversity, especially in multitrophic communities. Here, we manipulated multivariate functional diversity of estuarine grazers and predators within multiple levels of species richness to test how species richness and functional diversity predicted ecosystem functioning in a multitrophic food web. Community functional diversity was a better predictor than species richness for the majority of ecosystem properties, based on generalized linear mixed-effects models. Combining inferences from eight traits into a single multivariate index increased prediction accuracy of these models relative to any individual trait. Structural equation modeling revealed that functional diversity of both grazers and predators was important in driving final biomass within trophic levels, with stronger effects observed for predators. We also show that different species drove different ecosystem responses, with evidence for both sampling effects and complementarity. Our study extends experimental investigations of functional trait diversity to a multilevel food web, and demonstrates that functional diversity can be more accurate and effective than species richness in predicting community biomass in a food web context.

Origin and Functional Prediction of Pollen Allergens in Plants1[OPEN

PubMed Central

Chen, Miaolin; Xu, Jie; Ren, Kang; Searle, Iain

2016-01-01

Pollen allergies have long been a major pandemic health problem for human. However, the evolutionary events and biological function of pollen allergens in plants remain largely unknown. Here, we report the genome-wide prediction of pollen allergens and their biological function in the dicotyledonous model plant Arabidopsis (Arabidopsis thaliana) and the monocotyledonous model plant rice (Oryza sativa). In total, 145 and 107 pollen allergens were predicted from rice and Arabidopsis, respectively. These pollen allergens are putatively involved in stress responses and metabolic processes such as cell wall metabolism during pollen development. Interestingly, these putative pollen allergen genes were derived from large gene families and became diversified during evolution. Sequence analysis across 25 plant species from green alga to angiosperms suggest that about 40% of putative pollen allergenic proteins existed in both lower and higher plants, while other allergens emerged during evolution. Although a high proportion of gene duplication has been observed among allergen-coding genes, our data show that these genes might have undergone purifying selection during evolution. We also observed that epitopes of an allergen might have a biological function, as revealed by comprehensive analysis of two known allergens, expansin and profilin. This implies a crucial role of conserved amino acid residues in both in planta biological function and allergenicity. Finally, a model explaining how pollen allergens were generated and maintained in plants is proposed. Prediction and systematic analysis of pollen allergens in model plants suggest that pollen allergens were evolved by gene duplication and then functional specification. This study provides insight into the phylogenetic and evolutionary scenario of pollen allergens that will be helpful to future characterization and epitope screening of pollen allergens. PMID:27436829

Rumination prospectively predicts executive functioning impairments in adolescents.

PubMed

Connolly, Samantha L; Wagner, Clara A; Shapero, Benjamin G; Pendergast, Laura L; Abramson, Lyn Y; Alloy, Lauren B

2014-03-01

The current study tested the resource allocation hypothesis, examining whether baseline rumination or depressive symptom levels prospectively predicted deficits in executive functioning in an adolescent sample. The alternative to this hypothesis was also evaluated by testing whether lower initial levels of executive functioning predicted increases in rumination or depressive symptoms at follow-up. A community sample of 200 adolescents (ages 12-13) completed measures of depressive symptoms, rumination, and executive functioning at baseline and at a follow-up session approximately 15 months later. Adolescents with higher levels of baseline rumination displayed decreases in selective attention and attentional switching at follow-up. Rumination did not predict changes in working memory or sustained and divided attention. Depressive symptoms were not found to predict significant changes in executive functioning scores at follow-up. Baseline executive functioning was not associated with change in rumination or depression over time. Findings partially support the resource allocation hypothesis that engaging in ruminative thoughts consumes cognitive resources that would otherwise be allocated towards difficult tests of executive functioning. Support was not found for the alternative hypothesis that lower levels of initial executive functioning would predict increased rumination or depressive symptoms at follow-up. Our study is the first to find support for the resource allocation hypothesis using a longitudinal design and an adolescent sample. Findings highlight the potentially detrimental effects of rumination on executive functioning during early adolescence. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rumination prospectively predicts executive functioning impairments in adolescents

PubMed Central

Connolly, Samantha L.; Wagner, Clara A.; Shapero, Benjamin G.; Pendergast, Laura L.; Abramson, Lyn Y.; Alloy, Lauren B.

2014-01-01

Background and objectives The current study tested the resource allocation hypothesis, examining whether baseline rumination or depressive symptom levels prospectively predicted deficits in executive functioning in an adolescent sample. The alternative to this hypothesis was also evaluated by testing whether lower initial levels of executive functioning predicted increases in rumination or depressive symptoms at follow-up. Methods A community sample of 200 adolescents (ages 12–13) completed measures of depressive symptoms, rumination, and executive functioning at baseline and at a follow-up session approximately 15 months later. Results Adolescents with higher levels of baseline rumination displayed decreases in selective attention and attentional switching at follow-up. Rumination did not predict changes in working memory or sustained and divided attention. Depressive symptoms were not found to predict significant changes in executive functioning scores at follow-up. Baseline executive functioning was not associated with change in rumination or depression over time. Conclusions Findings partially support the resource allocation hypothesis that engaging in ruminative thoughts consumes cognitive resources that would otherwise be allocated towards difficult tests of executive functioning. Support was not found for the alternative hypothesis that lower levels of initial executive functioning would predict increased rumination or depressive symptoms at follow-up. Our study is the first to find support for the resource allocation hypothesis using a longitudinal design and an adolescent sample. Findings highlight the potentially detrimental effects of rumination on executive functioning during early adolescence. PMID:23978629

Early Childhood Practitioner Involvement in Functional Behavioral Assessment and Function-Based Interventions: A Literature Review

ERIC Educational Resources Information Center

Wood, Brenna K.; Drogan, Robin R.; Janney, Donna M.

2014-01-01

Reviewers analyzed studies published from 1990 to 2012 to determine early childhood practitioner involvement in functional behavioral assessment (FBA) and function-based behavioral intervention plans (BIP) for children with challenging behavior, age 6 and younger. Coding of 30 studies included practitioner involvement in FBA and BIP processes,…

Emotion Regulation Predicts Pain and Functioning in Children With Juvenile Idiopathic Arthritis: An Electronic Diary Study

PubMed Central

Bromberg, Maggie H.; Anthony, Kelly K.; Gil, Karen M.; Franks, Lindsey; Schanberg, Laura E.

2012-01-01

Objectives This study utilized e-diaries to evaluate whether components of emotion regulation predict daily pain and function in children with juvenile idiopathic arthritis (JIA). Methods 43 children ages 8–17 years and their caregivers provided baseline reports of child emotion regulation. Children then completed thrice daily e-diary assessments of emotion, pain, and activity involvement for 28 days. E-diary ratings of negative and positive emotions were used to calculate emotion variability and to infer adaptive emotion modulation following periods of high or low emotion intensity. Hierarchical linear models were used to evaluate how emotion regulation related to pain and function. Results The attenuation of negative emotion following a period of high negative emotion predicted reduced pain; greater variability of negative emotion predicted higher pain and increased activity limitation. Indices of positive emotion regulation also significantly predicted pain. Conclusions Components of emotion regulation as captured by e-diaries predict important health outcomes in children with JIA. PMID:22037006

Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity

PubMed Central

Benedek, Mathias; Jauk, Emanuel; Sommer, Markus; Arendasy, Martin; Neubauer, Aljoscha C.

2014-01-01

Intelligence and creativity are known to be correlated constructs suggesting that they share a common cognitive basis. The present study assessed three specific executive abilities – updating, shifting, and inhibition – and examined their common and differential relations to fluid intelligence and creativity (i.e., divergent thinking ability) within a latent variable model approach. Additionally, it was tested whether the correlation of fluid intelligence and creativity can be explained by a common executive involvement. As expected, fluid intelligence was strongly predicted by updating, but not by shifting or inhibition. Creativity was predicted by updating and inhibition, but not by shifting. Moreover, updating (and the personality factor openness) was found to explain a relevant part of the shared variance between intelligence and creativity. The findings provide direct support for the executive involvement in creative thought and shed further light on the functional relationship between intelligence and creativity. PMID:25278640

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

PubMed Central

Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

2012-01-01

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

Factors Predictive of Sentinel Lymph Node Involvement in Primary Breast Cancer.

PubMed

Malter, Wolfram; Hellmich, Martin; Badian, Mayhar; Kirn, Verena; Mallmann, Peter; Krämer, Stefan

2018-06-01

Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for axillary staging in patients with early-stage breast cancer. The need for therapeutic ALND is the subject of ongoing debate especially after the publication of the ACOSOG Z0011 trial. In a retrospective trial with univariate and multivariate analyses, factors predictive of sentinel lymph node involvement should be analyzed in order to define tumor characteristics of breast cancer patients, where SLNB should not be spared to receive important indicators for adjuvant treatment decisions (e.g. thoracic wall irradiation after mastectomy with or without reconstruction). Between 2006 and 2010, 1,360 patients with primary breast cancer underwent SLNB with/without ALND with evaluation of tumor localization, multicentricity and multifocality, histological subtype, tumor size, grading, lymphovascular invasion (LVI), and estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status. These characteristics were retrospectively analyzed in univariate and multivariate logistic regression models to define significant predictive factors for sentinel lymph node involvement. The multivariate analysis demonstrated that tumor size and LVI (p<0.001) were independent predictive factors for metastatic sentinel lymph node involvement in patients with early-stage breast cancer. Because of the increased risk for metastatic involvement of axillary sentinel nodes in cases with larger breast cancer or diagnosis of LVI, patients with these breast cancer characteristics should not be spared from SLNB in a clinically node-negative situation in order to avoid false-negative results with a high potential for wrong indication of primary breast reconstruction or wrong non-indication of necessary post-mastectomy radiation therapy. The prognostic impact of avoidance of axillary staging with SLNB is analyzed in the ongoing prospective INSEMA trial. Copyright© 2018, International

Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations.

PubMed

Ruiz, Montse C; Haapanen, Saara; Tolvanen, Asko; Robazza, Claudio; Duda, Joan L

2017-08-01

This study examined the relationships between perceptions of the motivational climate, motivation regulations, and the intensity and functionality levels of athletes' pleasant and unpleasant emotional states. Specifically, we examined the hypothesised mediational role of motivation regulations in the climate-emotion relationship. We also tested a sequence in which emotions were assumed to be predicted by the motivational climate dimensions and then served as antecedents to variability in motivation regulations. Participants (N = 494) completed a multi-section questionnaire assessing targeted variables. Structural equation modelling (SEM) revealed that a perceived task-involving climate was a positive predictor of autonomous motivation and of the impact of functional anger, and a negative predictor of the intensity of anxiety and dysfunctional anger. Autonomous motivation was a partial mediator of perceptions of a task-involving climate and the impact of functional anger. An ego-involving climate was a positive predictor of controlled motivation, and of the intensity and impact of functional anger and the intensity of dysfunctional anger. Controlled motivation partially mediated the relationship between an ego-involving climate and the intensity of dysfunctional anger. Good fit to the data also emerged for the motivational climate, emotional states, and motivation regulations sequence. Findings provide support for the consideration of hedonic tone and functionality distinctions in the assessment of athletes' emotional states.

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

Biological pathways and genetic mechanisms involved in social functioning.

PubMed

Ordoñana, Juan R; Bartels, Meike; Boomsma, Dorret I; Cella, David; Mosing, Miriam; Oliveira, Joao R; Patrick, Donald L; Veenhoven, Ruut; Wagner, Gert G; Sprangers, Mirjam A G

2013-08-01

To describe the major findings in the literature regarding associations between biological and genetic factors and social functioning, paying special attention to: (1) heritability studies on social functioning and related concepts; (2) hypothesized biological pathways and genetic variants that could be involved in social functioning, and (3) the implications of these results for quality-of-life research. A search of Web of Science and PubMed databases was conducted using combinations of the following keywords: genetics, twins, heritability, social functioning, social adjustment, social interaction, and social dysfunction. Variability in the definitions and measures of social functioning was extensive. Moderate to high heritability was reported for social functioning and related concepts, including prosocial behavior, loneliness, and extraversion. Disorders characterized by impairments in social functioning also show substantial heritability. Genetic variants hypothesized to be involved in social functioning are related to the network of brain structures and processes that are known to affect social cognition and behavior. Better knowledge and understanding about the impact of genetic factors on social functioning is needed to help us to attain a more comprehensive view of health-related quality-of-life (HRQOL) and will ultimately enhance our ability to identify those patients who are vulnerable to poor social functioning.

Childhood bullying involvement predicts low-grade systemic inflammation into adulthood.

PubMed

Copeland, William E; Wolke, Dieter; Lereya, Suzet Tanya; Shanahan, Lilly; Worthman, Carol; Costello, E Jane

2014-05-27

Bullying is a common childhood experience that involves repeated mistreatment to improve or maintain one's status. Victims display long-term social, psychological, and health consequences, whereas bullies display minimal ill effects. The aim of this study is to test how this adverse social experience is biologically embedded to affect short- or long-term levels of C-reactive protein (CRP), a marker of low-grade systemic inflammation. The prospective population-based Great Smoky Mountains Study (n = 1,420), with up to nine waves of data per subject, was used, covering childhood/adolescence (ages 9-16) and young adulthood (ages 19 and 21). Structured interviews were used to assess bullying involvement and relevant covariates at all childhood/adolescent observations. Blood spots were collected at each observation and assayed for CRP levels. During childhood and adolescence, the number of waves at which the child was bullied predicted increasing levels of CRP. Although CRP levels rose for all participants from childhood into adulthood, being bullied predicted greater increases in CRP levels, whereas bullying others predicted lower increases in CRP compared with those uninvolved in bullying. This pattern was robust, controlling for body mass index, substance use, physical and mental health status, and exposures to other childhood psychosocial adversities. A child's role in bullying may serve as either a risk or a protective factor for adult low-grade inflammation, independent of other factors. Inflammation is a physiological response that mediates the effects of both social adversity and dominance on decreases in health.

Protein Structure and Function Prediction Using I-TASSER

PubMed Central

Yang, Jianyi; Zhang, Yang

2016-01-01

I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets. PMID:26678386

Calibration and prediction of removal function in magnetorheological finishing.

PubMed

Dai, Yifan; Song, Ci; Peng, Xiaoqiang; Shi, Feng

2010-01-20

A calibrated and predictive model of the removal function has been established based on the analysis of a magnetorheological finishing (MRF) process. By introducing an efficiency coefficient of the removal function, the model can be used to calibrate the removal function in a MRF figuring process and to accurately predict the removal function of a workpiece to be polished whose material is different from the spot part. Its correctness and feasibility have been validated by simulations. Furthermore, applying this model to the MRF figuring experiments, the efficiency coefficient of the removal function can be identified accurately to make the MRF figuring process deterministic and controllable. Therefore, all the results indicate that the calibrated and predictive model of the removal function can improve the finishing determinacy and increase the model applicability in a MRF process.

Metataxonomic profiling and prediction of functional behaviour of wheat straw degrading microbial consortia

PubMed Central

2014-01-01

Background Mixed microbial cultures, in which bacteria and fungi interact, have been proposed as an efficient way to deconstruct plant waste. The characterization of specific microbial consortia could be the starting point for novel biotechnological applications related to the efficient conversion of lignocellulose to cello-oligosaccharides, plastics and/or biofuels. Here, the diversity, composition and predicted functional profiles of novel bacterial-fungal consortia are reported, on the basis of replicated aerobic wheat straw enrichment cultures. Results In order to set up biodegradative microcosms, microbial communities were retrieved from a forest soil and introduced into a mineral salt medium containing 1% of (un)treated wheat straw. Following each incubation step, sequential transfers were carried out using 1 to 1,000 dilutions. The microbial source next to three sequential batch cultures (transfers 1, 3 and 10) were analyzed by bacterial 16S rRNA gene and fungal ITS1 pyrosequencing. Faith’s phylogenetic diversity values became progressively smaller from the inoculum to the sequential batch cultures. Moreover, increases in the relative abundances of Enterobacteriales, Pseudomonadales, Flavobacteriales and Sphingobacteriales were noted along the enrichment process. Operational taxonomic units affiliated with Acinetobacter johnsonii, Pseudomonas putida and Sphingobacterium faecium were abundant and the underlying strains were successfully isolated. Interestingly, Klebsiella variicola (OTU1062) was found to dominate in both consortia, whereas K. variicola-affiliated strains retrieved from untreated wheat straw consortia showed endoglucanase/xylanase activities. Among the fungal players with high biotechnological relevance, we recovered members of the genera Penicillium, Acremonium, Coniochaeta and Trichosporon. Remarkably, the presence of peroxidases, alpha-L-fucosidases, beta-xylosidases, beta-mannases and beta-glucosidases, involved in lignocellulose

Predicting the Impact of Alternative Splicing on Plant MADS Domain Protein Function

PubMed Central

Severing, Edouard I.; van Dijk, Aalt D. J.; Morabito, Giuseppa; Busscher-Lange, Jacqueline; Immink, Richard G. H.; van Ham, Roeland C. H. J.

2012-01-01

Several genome-wide studies demonstrated that alternative splicing (AS) significantly increases the transcriptome complexity in plants. However, the impact of AS on the functional diversity of proteins is difficult to assess using genome-wide approaches. The availability of detailed sequence annotations for specific genes and gene families allows for a more detailed assessment of the potential effect of AS on their function. One example is the plant MADS-domain transcription factor family, members of which interact to form protein complexes that function in transcription regulation. Here, we perform an in silico analysis of the potential impact of AS on the protein-protein interaction capabilities of MIKC-type MADS-domain proteins. We first confirmed the expression of transcript isoforms resulting from predicted AS events. Expressed transcript isoforms were considered functional if they were likely to be translated and if their corresponding AS events either had an effect on predicted dimerisation motifs or occurred in regions known to be involved in multimeric complex formation, or otherwise, if their effect was conserved in different species. Nine out of twelve MIKC MADS-box genes predicted to produce multiple protein isoforms harbored putative functional AS events according to those criteria. AS events with conserved effects were only found at the borders of or within the K-box domain. We illustrate how AS can contribute to the evolution of interaction networks through an example of selective inclusion of a recently evolved interaction motif in the MADS AFFECTING FLOWERING1-3 (MAF1–3) subclade. Furthermore, we demonstrate the potential effect of an AS event in SHORT VEGETATIVE PHASE (SVP), resulting in the deletion of a short sequence stretch including a predicted interaction motif, by overexpression of the fully spliced and the alternatively spliced SVP transcripts. For most of the AS events we were able to formulate hypotheses about the potential impact on

Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

PubMed

Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

2018-02-15

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

COPRED: prediction of fold, GO molecular function and functional residues at the domain level.

PubMed

López, Daniel; Pazos, Florencio

2013-07-15

Only recently the first resources devoted to the functional annotation of proteins at the domain level started to appear. The next step is to develop specific methodologies for predicting function at the domain level based on these resources, and to implement them in web servers to be used by the community. In this work, we present COPRED, a web server for the concomitant prediction of fold, molecular function and functional sites at the domain level, based on a methodology for domain molecular function prediction and a resource of domain functional annotations previously developed and benchmarked. COPRED can be freely accessed at http://csbg.cnb.csic.es/copred. The interface works in all standard web browsers. WebGL (natively supported by most browsers) is required for the in-line preview and manipulation of protein 3D structures. The website includes a detailed help section and usage examples. pazos@cnb.csic.es.

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

Childhood bullying involvement predicts low-grade systemic inflammation into adulthood

PubMed Central

Copeland, William E.; Wolke, Dieter; Lereya, Suzet Tanya; Shanahan, Lilly; Worthman, Carol; Costello, E. Jane

2014-01-01

Bullying is a common childhood experience that involves repeated mistreatment to improve or maintain one’s status. Victims display long-term social, psychological, and health consequences, whereas bullies display minimal ill effects. The aim of this study is to test how this adverse social experience is biologically embedded to affect short- or long-term levels of C-reactive protein (CRP), a marker of low-grade systemic inflammation. The prospective population-based Great Smoky Mountains Study (n = 1,420), with up to nine waves of data per subject, was used, covering childhood/adolescence (ages 9–16) and young adulthood (ages 19 and 21). Structured interviews were used to assess bullying involvement and relevant covariates at all childhood/adolescent observations. Blood spots were collected at each observation and assayed for CRP levels. During childhood and adolescence, the number of waves at which the child was bullied predicted increasing levels of CRP. Although CRP levels rose for all participants from childhood into adulthood, being bullied predicted greater increases in CRP levels, whereas bullying others predicted lower increases in CRP compared with those uninvolved in bullying. This pattern was robust, controlling for body mass index, substance use, physical and mental health status, and exposures to other childhood psychosocial adversities. A child’s role in bullying may serve as either a risk or a protective factor for adult low-grade inflammation, independent of other factors. Inflammation is a physiological response that mediates the effects of both social adversity and dominance on decreases in health. PMID:24821813

Prediction of Erectile Function Following Treatment for Prostate Cancer

PubMed Central

Alemozaffar, Mehrdad; Regan, Meredith M.; Cooperberg, Matthew R.; Wei, John T.; Michalski, Jeff M.; Sandler, Howard M.; Hembroff, Larry; Sadetsky, Natalia; Saigal, Christopher S.; Litwin, Mark S.; Klein, Eric; Kibel, Adam S.; Hamstra, Daniel A.; Pisters, Louis L.; Kuban, Deborah A.; Kaplan, Irving D.; Wood, David P.; Ciezki, Jay; Dunn, Rodney L.; Carroll, Peter R.; Sanda, Martin G.

2013-01-01

Context Sexual function is the health-related quality of life (HRQOL) domain most commonly impaired after prostate cancer treatment; however, validated tools to enable personalized prediction of erectile dysfunction after prostate cancer treatment are lacking. Objective To predict long-term erectile function following prostate cancer treatment based on individual patient and treatment characteristics. Design Pretreatment patient characteristics, sexual HRQOL, and treatment details measured in a longitudinal academic multicenter cohort (Prostate Cancer Outcomes and Satisfaction With Treatment Quality Assessment; enrolled from 2003 through 2006), were used to develop models predicting erectile function 2 years after treatment. A community-based cohort (community-based Cancer of the Prostate Strategic Urologic Research Endeavor [CaPSURE]; enrolled 1995 through 2007) externally validated model performance. Patients in US academic and community-based practices whose HRQOL was measured pretreatment (N = 1201) underwent follow-up after prostatectomy, external radiotherapy, or brachytherapy for prostate cancer. Sexual outcomes among men completing 2 years’ follow-up (n = 1027) were used to develop models predicting erectile function that were externally validated among 1913 patients in a community-based cohort. Main Outcome Measures Patient-reported functional erections suitable for intercourse 2 years following prostate cancer treatment. Results Two years after prostate cancer treatment, 368 (37% [95% CI, 34%–40%]) of all patients and 335 (48% [95% CI, 45%–52%]) of those with functional erections prior to treatment reported functional erections; 531 (53% [95% CI, 50%–56%]) of patients without penile prostheses reported use of medications or other devices for erectile dysfunction. Pretreatment sexual HRQOL score, age, serum prostate-specific antigen level, race/ethnicity, body mass index, and intended treatment details were associated with functional erections 2

Predicting effects of climate change on the composition and function of soil microbial communities

NASA Astrophysics Data System (ADS)

Dubinsky, E.; Brodie, E.; Myint, C.; Ackerly, D.; van Nostrand, J.; Bird, J.; Zhou, J.; Andersen, G.; Firestone, M.

2008-12-01

of these predictable changes in community composition were measured with functional arrays that detect genes involved in the metabolism of carbon, nitrogen and other elements. The response of soil microbial communities to altered precipitation can be predicted from the distribution of microbial taxa across moisture gradients.

Evolution and Cellular Function of Monothiol Glutaredoxins: Involvement in Iron-Sulphur Cluster Assembly

PubMed Central

Vilella, Felipe; Alves, Rui; Rodríguez-Manzaneque, María Teresa; Bellí, Gemma; Swaminathan, Swarna; Sunnerhagen, Per

2004-01-01

A number of bacterial species, mostly proteobacteria, possess monothiol glutaredoxins homologous to the Saccharomyces cerevisiae mitochondrial protein Grx5, which is involved in iron–sulphur cluster synthesis. Phylogenetic profiling is used to predict that bacterial monothiol glutaredoxins also participate in the iron–sulphur cluster (ISC) assembly machinery, because their phylogenetic profiles are similar to the profiles of the bacterial homologues of yeast ISC proteins. High evolutionary co-occurrence is observed between the Grx5 homologues and the homologues of the Yah1 ferredoxin, the scaffold proteins Isa1 and Isa2, the frataxin protein Yfh1 and the Nfu1 protein. This suggests that a specific functional interaction exists between these ISC machinery proteins. Physical interaction analyses using low-definition protein docking predict the formation of strong and specific complexes between Grx5 and several components of the yeast ISC machinery. Two-hybrid analysis has confirmed the in vivo interaction between Grx5 and Isa1. Sequence comparison techniques and cladistics indicate that the other two monothiol glutaredoxins of S. cerevisiae, Grx3 and Grx4, have evolved from the fusion of a thioredoxin gene with a monothiol glutaredoxin gene early in the eukaryotic lineage, leading to differential functional specialization. While bacteria do not contain these chimaeric glutaredoxins, in many eukaryotic species Grx5 and Grx3/4-type monothiol glutaredoxins coexist in the cell. PMID:18629168

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

PubMed

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Three-dimensional computed tomographic volumetry precisely predicts the postoperative pulmonary function.

PubMed

Kobayashi, Keisuke; Saeki, Yusuke; Kitazawa, Shinsuke; Kobayashi, Naohiro; Kikuchi, Shinji; Goto, Yukinobu; Sakai, Mitsuaki; Sato, Yukio

2017-11-01

It is important to accurately predict the patient's postoperative pulmonary function. The aim of this study was to compare the accuracy of predictions of the postoperative residual pulmonary function obtained with three-dimensional computed tomographic (3D-CT) volumetry with that of predictions obtained with the conventional segment-counting method. Fifty-three patients scheduled to undergo lung cancer resection, pulmonary function tests, and computed tomography were enrolled in this study. The postoperative residual pulmonary function was predicted based on the segment-counting and 3D-CT volumetry methods. The predicted postoperative values were compared with the results of postoperative pulmonary function tests. Regarding the linear correlation coefficients between the predicted postoperative values and the measured values, those obtained using the 3D-CT volumetry method tended to be higher than those acquired using the segment-counting method. In addition, the variations between the predicted and measured values were smaller with the 3D-CT volumetry method than with the segment-counting method. These results were more obvious in COPD patients than in non-COPD patients. Our findings suggested that the 3D-CT volumetry was able to predict the residual pulmonary function more accurately than the segment-counting method, especially in patients with COPD. This method might lead to the selection of appropriate candidates for surgery among patients with a marginal pulmonary function.

Predicting Hydrologic Function With Aquatic Gene Fragments

NASA Astrophysics Data System (ADS)

Good, S. P.; URycki, D. R.; Crump, B. C.

2018-03-01

Recent advances in microbiology techniques, such as genetic sequencing, allow for rapid and cost-effective collection of large quantities of genetic information carried within water samples. Here we posit that the unique composition of aquatic DNA material within a water sample contains relevant information about hydrologic function at multiple temporal scales. In this study, machine learning was used to develop discharge prediction models trained on the relative abundance of bacterial taxa classified into operational taxonomic units (OTUs) based on 16S rRNA gene sequences from six large arctic rivers. We term this approach "genohydrology," and show that OTU relative abundances can be used to predict river discharge at monthly and longer timescales. Based on a single DNA sample from each river, the average Nash-Sutcliffe efficiency (NSE) for predicted mean monthly discharge values throughout the year was 0.84, while the NSE for predicted discharge values across different return intervals was 0.67. These are considerable improvements over predictions based only on the area-scaled mean specific discharge of five similar rivers, which had average NSE values of 0.64 and -0.32 for seasonal and recurrence interval discharge values, respectively. The genohydrology approach demonstrates that genetic diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

Prediction of recovery of motor function after stroke.

PubMed

Stinear, Cathy

2010-12-01

Stroke is a leading cause of disability. The ability to live independently after stroke depends largely on the reduction of motor impairment and the recovery of motor function. Accurate prediction of motor recovery assists rehabilitation planning and supports realistic goal setting by clinicians and patients. Initial impairment is negatively related to degree of recovery, but inter-individual variability makes accurate prediction difficult. Neuroimaging and neurophysiological assessments can be used to measure the extent of stroke damage to the motor system and predict subsequent recovery of function, but these techniques are not yet used routinely. The use of motor impairment scores and neuroimaging has been refined by two recent studies in which these investigations were used at multiple time points early after stroke. Voluntary finger extension and shoulder abduction within 5 days of stroke predicted subsequent recovery of upper-limb function. Diffusion-weighted imaging within 7 days detected the effects of stroke on caudal motor pathways and was predictive of lasting motor impairment. Thus, investigations done soon after stroke had good prognostic value. The potential prognostic value of cortical activation and neural plasticity has been explored for the first time by two recent studies. Functional MRI detected a pattern of cortical activation at the acute stage that was related to subsequent reduction in motor impairment. Transcranial magnetic stimulation enabled measurement of neural plasticity in the primary motor cortex, which was related to subsequent disability. These studies open interesting new lines of enquiry. WHERE NEXT?: The accuracy of prediction might be increased by taking into account the motor system's capacity for functional reorganisation in response to therapy, in addition to the extent of stroke-related damage. Improved prognostic accuracy could also be gained by combining simple tests of motor impairment with neuroimaging, genotyping, and

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

Predicting cognitive function from clinical measures of physical function and health status in older adults.

PubMed

Bolandzadeh, Niousha; Kording, Konrad; Salowitz, Nicole; Davis, Jennifer C; Hsu, Liang; Chan, Alison; Sharma, Devika; Blohm, Gunnar; Liu-Ambrose, Teresa

2015-01-01

Current research suggests that the neuropathology of dementia-including brain changes leading to memory impairment and cognitive decline-is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies. We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1-L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation. Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year. We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

Human mobility prediction from region functions with taxi trajectories.

PubMed

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions.

Human mobility prediction from region functions with taxi trajectories

PubMed Central

Wang, Minjie; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions. PMID:29190708

A large-scale evaluation of computational protein function prediction

PubMed Central

Radivojac, Predrag; Clark, Wyatt T; Ronnen Oron, Tal; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kassner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Böhm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

2013-01-01

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools. PMID:23353650

Executive function processes predict mobility outcomes in older adults.

PubMed

Gothe, Neha P; Fanning, Jason; Awick, Elizabeth; Chung, David; Wójcicki, Thomas R; Olson, Erin A; Mullen, Sean P; Voss, Michelle; Erickson, Kirk I; Kramer, Arthur F; McAuley, Edward

2014-02-01

To examine the relationship between performance on executive function measures and subsequent mobility outcomes in community-dwelling older adults. Randomized controlled clinical trial. Champaign-Urbana, Illinois. Community-dwelling older adults (N = 179; mean age 66.4). A 12-month exercise trial with two arms: an aerobic exercise group and a stretching and strengthening group. Established cognitive tests of executive function (flanker task, task switching, and a dual-task paradigm) and the Wisconsin card sort test. Mobility was assessed using the timed 8-foot up and go test and times to climb up and down a flight of stairs. Participants completed the cognitive tests at baseline and the mobility measures at baseline and after 12 months of the intervention. Multiple regression analyses were conducted to determine whether baseline executive function predicted postintervention functional performance after controlling for age, sex, education, cardiorespiratory fitness, and baseline mobility levels. Selective baseline executive function measurements, particularly performance on the flanker task (β = 0.15-0.17) and the Wisconsin card sort test (β = 0.11-0.16) consistently predicted mobility outcomes at 12 months. The estimates were in the expected direction, such that better baseline performance on the executive function measures predicted better performance on the timed mobility tests independent of intervention. Executive functions of inhibitory control, mental set shifting, and attentional flexibility were predictive of functional mobility. Given the literature associating mobility limitations with disability, morbidity, and mortality, these results are important for understanding the antecedents to poor mobility function that well-designed interventions to improve cognitive performance can attenuate. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.

Empowerment through public involvement functional interactive planning (PIFIP)

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

Beck, J. E.; Davidson, S. A.

1993-05-01

This paper constructs a planning process that will enable private industries, government, and public interest organizations to actualize their visions. The public involvement functional interactive planning (PIFIP) model can facilitate these groups in actualizing their visions by forcing them to recognize their stakeholder`s values, interests and expectations.

Utility of functioning in predicting costs of care for patients with mood and anxiety disorders: a prospective cohort study

PubMed Central

Cieza, Alarcos; Baldwin, David S.

2017-01-01

Development of payment systems for mental health services has been hindered by limited evidence for the utility of diagnosis or symptoms in predicting costs of care. We investigated the utility of functioning information in predicting costs for patients with mood and anxiety disorders. This was a prospective cohort study involving 102 adult patients attending a tertiary referral specialist clinic for mood and anxiety disorders. The main outcome was total costs, calculated by applying unit costs to healthcare use data. After adjusting for covariates, a significant total costs association was yielded for functioning (eβ=1.02; 95% confidence interval: 1.01–1.03), but not depressive symptom severity or anxiety symptom severity. When we accounted for the correlations between the main independent variables by constructing an abridged functioning metric, a significant total costs association was again yielded for functioning (eβ=1.04; 95% confidence interval: 1.01–1.09), but not symptom severity. The utility of functioning in predicting costs for patients with mood and anxiety disorders was supported. Functioning information could be useful within mental health payment systems. PMID:28383309

Executive Function Processes Predict Mobility Outcomes in Older Adults

PubMed Central

Gothe, Neha P.; Fanning, Jason; Awick, Elizabeth; Chung, David; Wójcicki, Thomas R.; Olson, Erin A.; Mullen, Sean P.; Voss, Michelle; Erickson, Kirk I.; Kramer, Arthur F.; McAuley, Edward

2013-01-01

BACKGROUND: There is growing evidence suggesting an association between cognitive function and physical performance in late life. This study examined the relationship between performance on executive function measures and subsequent mobility outcomes among community dwelling older adults across a 12-month randomized controlled exercise trial. DESIGN: Randomized controlled clinical trial SETTING: Champaign-Urbana, Illinois PARTICIPANTS: Community dwelling older adults (N = 179; Mage = 66.4) INTERVENTION: A 12-month exercise trial with two arms: an aerobic exercise group and a stretching and strengthening group MEASUREMENTS: Established cognitive tests of executive function including the flanker task, task switching and a dual task paradigm, and the Wisconsin card sort test. Mobility was assessed using the timed 8-foot up and go test and times to climb up and down a flight of stairs. METHODS: Participants completed the cognitive measures at baseline and the mobility measures at baseline and after 12 months of the intervention. Multiple regression analyses were conducted to determine whether baseline executive function predicted post-intervention functional performance after controlling for age, sex, education, cardiorespiratory fitness and baseline mobility levels. RESULTS: Our analyses revealed that selective baseline executive function measures, particularly performance on the flanker task (β’s =.15 to .17) and the Wisconsin card sort test (β’s =.11 to .16) consistently predicted mobility outcomes at month 12. The estimates were in the expected direction, such that better baseline performance on the executive function measures predicted better performance on the timed mobility tests independent of the intervention group. CONCLUSION: Executive functions of inhibitory control, mental set shifting and attentional flexibility were predictive of functional mobility. Given the literature associating mobility limitations with disability, morbidity, and mortality

Cognitive Impairment Precedes and Predicts Functional Impairment in Mild Alzheimer's Disease.

PubMed

Liu-Seifert, Hong; Siemers, Eric; Price, Karen; Han, Baoguang; Selzler, Katherine J; Henley, David; Sundell, Karen; Aisen, Paul; Cummings, Jeffrey; Raskin, Joel; Mohs, Richard

2015-01-01

The temporal relationship of cognitive deficit and functional impairment in Alzheimer's disease (AD) is not well characterized. Recent analyses suggest cognitive decline predicts subsequent functional decline throughout AD progression. To better understand the relationship between cognitive and functional decline in mild AD using autoregressive cross-lagged (ARCL) panel analyses in several clinical trials. Data included placebo patients with mild AD pooled from two multicenter, double-blind, Phase 3 solanezumab (EXPEDITION/2) or semagacestat (IDENTITY/2) studies, and from AD patients participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitive and functional outcomes were assessed using AD Assessment Scale-Cognitive subscale (ADAS-Cog), AD Cooperative Study-Activities of Daily Living instrumental subscale (ADCS-iADL), or Functional Activities Questionnaire (FAQ), respectively. ARCL panel analyses evaluated relationships between cognitive and functional impairment over time. In EXPEDITION, ARCL panel analyses demonstrated cognitive scores significantly predicted future functional impairment at 5 of 6 time points, while functional scores predicted subsequent cognitive scores in only 1 of 6 time points. Data from IDENTITY and ADNI programs yielded consistent results whereby cognition predicted subsequent function, but not vice-versa. Analyses from three databases indicated cognitive decline precedes and predicts subsequent functional decline in mild AD dementia, consistent with previously proposed hypotheses, and corroborate recent publications using similar methodologies. Cognitive impairment may be used as a predictor of future functional impairment in mild AD dementia and can be considered a critical target for prevention strategies to limit future functional decline in the dementia process.

Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods and Challenges

PubMed Central

Zhou, Weiqiang; Sherwood, Ben; Ji, Hongkai

2017-01-01

Technological advances have led to an explosive growth of high-throughput functional genomic data. Exploiting the correlation among different data types, it is possible to predict one functional genomic data type from other data types. Prediction tools are valuable in understanding the relationship among different functional genomic signals. They also provide a cost-efficient solution to inferring the unknown functional genomic profiles when experimental data are unavailable due to resource or technological constraints. The predicted data may be used for generating hypotheses, prioritizing targets, interpreting disease variants, facilitating data integration, quality control, and many other purposes. This article reviews various applications of prediction methods in functional genomics, discusses analytical challenges, and highlights some common and effective strategies used to develop prediction methods for functional genomic data. PMID:28076869

The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

PubMed

Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

2016-01-01

Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network

PubMed Central

Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

2016-01-01

Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

PubMed Central

2012-01-01

Background The first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced. Results In order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated. Conclusions The outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website (http://www.sdstate.edu/ps/research/vitis/pathways.cfm). PMID:22554261

Predictive factors of functional capacity and real-world functioning in patients with schizophrenia.

PubMed

Menendez-Miranda, I; Garcia-Portilla, M P; Garcia-Alvarez, L; Arrojo, M; Sanchez, P; Sarramea, F; Gomar, J; Bobes-Bascaran, M T; Sierra, P; Saiz, P A; Bobes, J

2015-07-01

This study was performed to identify the predictive factors of functional capacity assessed by the Spanish University of California Performance Skills Assessment (Sp-UPSA) and real-world functioning assessed by the Spanish Personal and Social Performance scale (PSP) in outpatients with schizophrenia. Naturalistic, 6-month follow-up, multicentre, validation study. Here, we report data on 139 patients with schizophrenia at their baseline visit. Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression-Severity (CGI-S), Sp-UPSA and PSP. Pearson's correlation coefficient (r) was used to determine the relationships between variables, and multivariable stepwise linear regression analyses to identify predictive variables of Sp-UPSA and PSP total scores. Functional capacity: scores on the PSP and PANSS-GP entered first and second at P<0.0001 and accounted for 21% of variance (R(2)=0.208, model df=2, F=15.724, P<0.0001). Real-world functioning: scores on the CGI-S (B=-5.406), PANSS-N (B=-0.657) and Sp-UPSA (B=0.230) entered first, second and third, and accounted for 51% of variance (model df=3, F=37.741, P<0.0001). In patients with schizophrenia, functional capacity and real-world functioning are two related but different constructs. Each one predicts the other along with other factors; general psychopathology for functional capacity, and severity of the illness and negative symptoms for real-world functioning. These findings have important clinical implications: (1) both types of functioning should be assessed in patients with schizophrenia and (2) strategies for improving them should be different. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Computations involving differential operators and their actions on functions

NASA Technical Reports Server (NTRS)

Crouch, Peter E.; Grossman, Robert; Larson, Richard

1991-01-01

The algorithms derived by Grossmann and Larson (1989) are further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear dynamical systems. These algorithms are extended in two different directions: the algorithms are generalized so that they apply to differential operators on groups and the data structures and algorithms are developed to compute symbolically the action of differential operators on functions. Both of these generalizations are needed for applications.

Extracurricular Activity and Parental Involvement Predict Positive Outcomes in Elementary School Children

ERIC Educational Resources Information Center

Lagace-Seguin, Daniel G.; Case, Emily

2010-01-01

The main goal of this study was to explore if parental involvement and extracurricular activity participation could predict well-being and academic competence in elementary school children. Seventy-two children (mean age = 10.9 years, SD = 0.85) and their parents participated. Results revealed that parental pressure and support, when paired with…

Bone involvement at diagnosis as a predictive factor in children with acute lymphoblastic leukemia.

PubMed

Tragiannidis, A; Vasileiou, E; Papageorgiou, M; Damianidou, L; Hatzipantelis, E; Gombakis, N; Giannopoulos, A

2016-01-01

Bone involvement represents a common symptom at diagnosis in children with acute lymphoblastic leukemia, and its prognostic value is not entirely clarified. The aim of this study was to evaluate bone involvement at diagnosis in children with acute lymphoblastic leukemia as a predictive factor and to correlate its presence with other demographic, clinical, and laboratory findings. We retrospectively reviewed the medical records of 97 children with acute lymphoblastic leukemia diagnosed from January 2005 to December 2014. The mean age of patients was 5.7 years, and 83 (85.6 %) of them were diagnosed with B-acute lymphoblastic leukemia. Among the 97 children, 46 (47.4 %) reported bone involvement at the time of diagnosis. Among children with B-acute lymphoblastic leukemia 43/83 (51.8 %) reported bone involvement, while among children with T-acute lymphoblastic leukemia only 3/14 (21.4 %) (p =0.04). Bone involvement was registered more frequently among males (30/59; 50.8 %) in comparison to females (16/38; 42.2 %) (p =0.414). The mean white blood cell count at diagnosis was lower among children with bone involvement (109,800/mm 3 vs. 184,700/mm 3 ) (p =0.092). The mean age of patients with bone involvement was four years, which differs significantly from those without bone involvement (p =0.029). Moreover, children with bone involvement at diagnosis were prednisone "good responders" (79.5 %) when compared with those without bone involvement (58.8 %) (p =0.046). Additionally, mean serum phosphate values were higher at diagnosis among children with bone involvement (5.3 mg/dl vs. 4.8 mg/dl, p =0.035). The presence of bone involvement at diagnosis is related with immunophenotype of B-acute lymphoblastic leukemia, lower mean age, lower mean white blood cell count and good prednisone response. According to presented data, we conclude that the presence of bone involvement at diagnosis represents a positive predictive factor for outcome/survival. Hippokratia 2016, 20(3): 227-230.

Bone involvement at diagnosis as a predictive factor in children with acute lymphoblastic leukemia

PubMed Central

Tragiannidis, A; Vasileiou, E; Papageorgiou, M; Damianidou, L; Hatzipantelis, E; Gombakis, N; Giannopoulos, A

2016-01-01

Background: Bone involvement represents a common symptom at diagnosis in children with acute lymphoblastic leukemia, and its prognostic value is not entirely clarified. The aim of this study was to evaluate bone involvement at diagnosis in children with acute lymphoblastic leukemia as a predictive factor and to correlate its presence with other demographic, clinical, and laboratory findings. Methods: We retrospectively reviewed the medical records of 97 children with acute lymphoblastic leukemia diagnosed from January 2005 to December 2014. The mean age of patients was 5.7 years, and 83 (85.6 %) of them were diagnosed with B-acute lymphoblastic leukemia. Results: Among the 97 children, 46 (47.4 %) reported bone involvement at the time of diagnosis. Among children with B-acute lymphoblastic leukemia 43/83 (51.8 %) reported bone involvement, while among children with T-acute lymphoblastic leukemia only 3/14 (21.4 %) (p =0.04). Bone involvement was registered more frequently among males (30/59; 50.8 %) in comparison to females (16/38; 42.2 %) (p =0.414). The mean white blood cell count at diagnosis was lower among children with bone involvement (109,800/mm3 vs. 184,700/mm3) (p =0.092). The mean age of patients with bone involvement was four years, which differs significantly from those without bone involvement (p =0.029). Moreover, children with bone involvement at diagnosis were prednisone “good responders” (79.5 %) when compared with those without bone involvement (58.8 %) (p =0.046). Additionally, mean serum phosphate values were higher at diagnosis among children with bone involvement (5.3 mg/dl vs. 4.8 mg/dl, p =0.035). Conclusions: The presence of bone involvement at diagnosis is related with immunophenotype of B-acute lymphoblastic leukemia, lower mean age, lower mean white blood cell count and good prednisone response. According to presented data, we conclude that the presence of bone involvement at diagnosis represents a positive predictive factor for

Thyroid Gland Involvement in Carcinoma Larynx and Hypopharynx-Predictive Factors and Prognostic Significance.

PubMed

Iype, Elizabeth Mathew; Jagad, Vijay; Nochikattil, Santhosh Kumar; Varghese, Bipin T; Sebastian, Paul

2016-02-01

Intraoperative management of thyroid gland in laryngeal and hypopharyngeal cancer is controversial. The objectives of this study were to determine the incidence of thyroid gland invasion in patients undergoing surgery for laryngeal or hypopharyngeal carcinoma, to assess predictive factors and to assess the prognosis in patients with and without thyroid gland invasion. One hundred and thirty-three patients who underwent surgery for carcinoma larynx and hypopharynx from 2006 to 2010 were reviewed retrospectively. Surgical specimens were examined to determine the incidence of thyroid gland invasion and predictive factors were analysed. The recurrence rate and the survival in patients with and without thyroid gland invasion were also analysed. Out of the 133 patients with carcinoma larynx and hypopharynx who underwent surgery, histological thyroid gland invasion was observed in 28/133 (21%) patients. Significant relationship was found between histological thyroid gland invasion and preoperative evidence of thyroid cartilage erosion by CT scan and also when gross thyroid gland involvement observed during surgery. There is significant association between thyroid gland invasion when there is upper oesophageal or subglottic involvement. After analysing the retrospective data from our study, we would like to suggest that thyroid gland need not be removed routinely in all laryngectomies, unless there is advanced disease with thyroid cartilage erosion and gross thyroid gland involvement or disease with significant subglottic or oesophageal involvement.

Functional Independence in Late-Life: Maintaining Physical Functioning in Older Adulthood Predicts Daily Life Function after Age 80

PubMed Central

Leng, Xiaoyan; La Monte, Michael J.; Tindle, Hilary A.; Cochrane, Barbara B.; Shumaker, Sally A.

2016-01-01

Abstract Background. We examined physical functioning (PF) trajectories (maintaining, slowly declining, and rapidly declining) spanning 15 years in older women aged 65–80 and protective factors that predicted better current levels and less decline in functional independence outcomes after age 80. Methods. Women’s Health Initiative extension participants who met criteria (enrolled in either the clinical trial or observational study cohort, >80 years at the data release cutoff, PF survey data from initial enrollment to age 80, and functional independence survey data after age 80) were included in these analyses (mean [ SD ] age = 84.0 [1.4] years; N = 10,478). PF was measured with the SF-36 (mean = 4.9 occasions). Functional independence was measured by self-reported level of dependence in basic and instrumental activities of daily living (ADLs and IADLs) (mean = 3.4 and 3.3 occasions). Results. Maintaining consistent PF in older adulthood extends functional independence in ADL and IADL in late-life. Protective factors shared by ADL and IADL include maintaining PF over time, self-reported excellent or very good health, no history of hip fracture after age 55, and no history of cardiovascular disease. Better IADL function is uniquely predicted by a body mass index less than 25 and no depression. Less ADL and IADL decline is predicted by better self-reported health, and less IADL decline is uniquely predicted by having no history of hip fracture after age 55. Conclusions. Maintaining or improving PF and preventing injury and disease in older adulthood (ages 65–80) has far-reaching implications for improving late-life (after age 80) functional independence. PMID:26858328

Preschool Executive Functioning Abilities Predict Early Mathematics Achievement

ERIC Educational Resources Information Center

Clark, Caron A. C.; Pritchard, Verena E.; Woodward, Lianne J.

2010-01-01

Impairments in executive function have been documented in school-age children with mathematical learning difficulties. However, the utility and specificity of preschool executive function abilities in predicting later mathematical achievement are poorly understood. This study examined linkages between children's developing executive function…

Predicting hydrologic function with the streamwater mircobiome

NASA Astrophysics Data System (ADS)

Good, S. P.; URycki, D. R.; Crump, B. C.

2017-12-01

Recent advances in microbiology allow for rapid and cost-effective determination of the presence of a nearly limitless number of bacterial (and other) species within a water sample. Here, we posit that the quasi-unique taxonomic composition of the aquatic microbiome is an emergent property of a catchment that contains information about hydrologic function at multiple temporal and spatial scales, and term this approach `genohydrolgy.' As first a genohydrology case study, we show that the relative abundance of bacterial species within different operational taxonomic units (OTUs) from six large arctic rivers can be used to predict river discharge at monthly and longer timescales. Using only OTU abundance information and a machine-learning algorithm trained on OTU and discharge data from the other five rivers, our genohydrology approach is able to predict mean monthly discharge values throughout the year with an average Nash-Sutcliffe efficiency (NSE) of 0.50, while the recurrence interval of extreme flows at longer times scales in these rivers was predicted with an NSE of 0.04. This approach demonstrates considerable improvement over prediction of these quantities in each river based only on discharge data from the other five (our null hypothesis), which had average NSE values of -1.19 and -5.50 for the seasonal and recurrence interval discharge values, respectively. Overall the genohydrology approach demonstrates that bacterial diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

Species Diversity and Functional Prediction of Surface Bacterial Communities on Aging Flue-Cured Tobaccos.

PubMed

Wang, Fan; Zhao, Hongwei; Xiang, Haiying; Wu, Lijun; Men, Xiao; Qi, Chang; Chen, Guoqiang; Zhang, Haibo; Wang, Yi; Xian, Mo

2018-06-05

Microbes on aging flue-cured tobaccos (ATFs) improve the aroma and other qualities desirable in products. Understanding the relevant organisms would picture microbial community diversity, metabolic potential, and their applications. However, limited efforts have been made on characterizing the microbial quality and functional profiling. Herein, we present our investigation of the bacterial diversity and predicted potential genetic capability of the bacteria from two AFTs using 16S rRNA gene sequences and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) software. The results show that dominant bacteria from AFT surfaces were classified into 48 genera, 36 families, and 7 phyla. In addition, Bacillus spp. was found prevalent on both ATFs. Furthermore, PICRUSt predictions of bacterial community functions revealed many attractive metabolic capacities in the AFT microbiota, including several involved in the biosynthesis of flavors and fragrances and the degradation of harmful compounds, such as nicotine and nitrite. These results provide insights into the importance of AFT bacteria in determining product qualities and indicate specific microbial species with predicted enzymatic capabilities for the production of high-efficiency flavors, the degradation of undesirable compounds, and the provision of nicotine and nitrite tolerance which suggest fruitful areas of investigation into the manipulation of AFT microbiota for AFT and other product improvements.

Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

PubMed

Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

2013-05-01

Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

PubMed

Dai, Tian; Guo, Ying

2017-02-15

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual

Some subclasses of multivalent functions involving a certain linear operator

NASA Astrophysics Data System (ADS)

Srivastava, H. M.; Patel, J.

2005-10-01

The authors investigate various inclusion and other properties of several subclasses of the class of normalized p-valent analytic functions in the open unit disk, which are defined here by means of a certain linear operator. Problems involving generalized neighborhoods of analytic functions in the class are investigated. Finally, some applications of fractional calculus operators are considered.

Functional Independence in Late-Life: Maintaining Physical Functioning in Older Adulthood Predicts Daily Life Function after Age 80.

PubMed

Vaughan, Leslie; Leng, Xiaoyan; La Monte, Michael J; Tindle, Hilary A; Cochrane, Barbara B; Shumaker, Sally A

2016-03-01

We examined physical functioning (PF) trajectories (maintaining, slowly declining, and rapidly declining) spanning 15 years in older women aged 65-80 and protective factors that predicted better current levels and less decline in functional independence outcomes after age 80. Women's Health Initiative extension participants who met criteria (enrolled in either the clinical trial or observational study cohort, >80 years at the data release cutoff, PF survey data from initial enrollment to age 80, and functional independence survey data after age 80) were included in these analyses (mean [SD] age = 84.0 [1.4] years; N = 10,478). PF was measured with the SF-36 (mean = 4.9 occasions). Functional independence was measured by self-reported level of dependence in basic and instrumental activities of daily living (ADLs and IADLs) (mean = 3.4 and 3.3 occasions). Maintaining consistent PF in older adulthood extends functional independence in ADL and IADL in late-life. Protective factors shared by ADL and IADL include maintaining PF over time, self-reported excellent or very good health, no history of hip fracture after age 55, and no history of cardiovascular disease. Better IADL function is uniquely predicted by a body mass index less than 25 and no depression. Less ADL and IADL decline is predicted by better self-reported health, and less IADL decline is uniquely predicted by having no history of hip fracture after age 55. Maintaining or improving PF and preventing injury and disease in older adulthood (ages 65-80) has far-reaching implications for improving late-life (after age 80) functional independence. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Predictability Effects on Durations of Content and Function Words in Conversational English

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

Bell, Alan; Brenier, Jason; Gregory, Michelle L.

Content and function word duration are affected differently by their frequency and predictability. Regression analyses of conversational speech show that content words are shorter when they are more frequent, but function words are not. Repeated content words are shorter, but function words are not. Furthermore, function words have shorter pronunciations, after controlling for frequency and predictability. both content and function words are strongly affected by predictability from the word following them, and only very frequent function words show sensitivity to predictability from the preceding word. The results support the view that content and function words are accessed by different productionmore » mechanisms. We argue that words’ form differences due to frequency or repetition stem from their faster or slower lexical access, mediated by a general mechanism that coordinates the pace of higher-level planning and the execution of the articulatory plan.« less

Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea.

PubMed

Wemheuer, Bernd; Wemheuer, Franziska; Meier, Dimitri; Billerbeck, Sara; Giebel, Helge-Ansgar; Simon, Meinhard; Scherber, Christoph; Daniel, Rolf

2017-11-05

Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria . Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA) showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

A scoring function based on solvation thermodynamics for protein structure prediction

PubMed Central

Du, Shiqiao; Harano, Yuichi; Kinoshita, Masahiro; Sakurai, Minoru

2012-01-01

We predict protein structure using our recently developed free energy function for describing protein stability, which is focused on solvation thermodynamics. The function is combined with the current most reliable sampling methods, i.e., fragment assembly (FA) and comparative modeling (CM). The prediction is tested using 11 small proteins for which high-resolution crystal structures are available. For 8 of these proteins, sequence similarities are found in the database, and the prediction is performed with CM. Fairly accurate models with average Cα root mean square deviation (RMSD) ∼ 2.0 Å are successfully obtained for all cases. For the rest of the target proteins, we perform the prediction following FA protocols. For 2 cases, we obtain predicted models with an RMSD ∼ 3.0 Å as the best-scored structures. For the other case, the RMSD remains larger than 7 Å. For all the 11 target proteins, our scoring function identifies the experimentally determined native structure as the best structure. Starting from the predicted structure, replica exchange molecular dynamics is performed to further refine the structures. However, we are unable to improve its RMSD toward the experimental structure. The exhaustive sampling by coarse-grained normal mode analysis around the native structures reveals that our function has a linear correlation with RMSDs < 3.0 Å. These results suggest that the function is quite reliable for the protein structure prediction while the sampling method remains one of the major limiting factors in it. The aspects through which the methodology could further be improved are discussed. PMID:27493529

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

PubMed Central

Peña-Castillo, Lourdes; Tasan, Murat; Myers, Chad L; Lee, Hyunju; Joshi, Trupti; Zhang, Chao; Guan, Yuanfang; Leone, Michele; Pagnani, Andrea; Kim, Wan Kyu; Krumpelman, Chase; Tian, Weidong; Obozinski, Guillaume; Qi, Yanjun; Mostafavi, Sara; Lin, Guan Ning; Berriz, Gabriel F; Gibbons, Francis D; Lanckriet, Gert; Qiu, Jian; Grant, Charles; Barutcuoglu, Zafer; Hill, David P; Warde-Farley, David; Grouios, Chris; Ray, Debajyoti; Blake, Judith A; Deng, Minghua; Jordan, Michael I; Noble, William S; Morris, Quaid; Klein-Seetharaman, Judith; Bar-Joseph, Ziv; Chen, Ting; Sun, Fengzhu; Troyanskaya, Olga G; Marcotte, Edward M; Xu, Dong; Hughes, Timothy R; Roth, Frederick P

2008-01-01

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized. PMID:18613946

Learned predictiveness and outcome predictability effects are not simply two sides of the same coin.

PubMed

Thorwart, Anna; Livesey, Evan J; Wilhelm, Francisco; Liu, Wei; Lachnit, Harald

2017-10-01

The Learned Predictiveness effect refers to the observation that learning about the relationship between a cue and an outcome is influenced by the predictive relevance of the cue for other outcomes. Similarly, the Outcome Predictability effect refers to a recent observation that the previous predictability of an outcome affects learning about this outcome in new situations, too. We hypothesize that both effects may be two manifestations of the same phenomenon and stimuli that have been involved in highly predictive relationships may be learned about faster when they are involved in new relationships regardless of their functional role in predictive learning as cues and outcomes. Four experiments manipulated both the relationships and the function of the stimuli. While we were able to replicate the standard effects, they did not survive a transfer to situations where the functional role of the stimuli changed, that is the outcome of the first phase becomes a cue in the second learning phase or the cue of the first phase becomes the outcome of the second phase. Furthermore, unlike learned predictiveness, there was little indication that the distribution of overt attention in the second phase was influenced by previous predictability. The results suggest that these 2 very similar effects are not manifestations of a more general phenomenon but rather independent from each other. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Graph pyramids for protein function prediction.

PubMed

Sandhan, Tushar; Yoo, Youngjun; Choi, Jin; Kim, Sun

2015-01-01

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Clinical Value of Dorsal Medulla Oblongata Involvement Detected with Conventional MRI for Prediction of Outcome in Children with Enterovirus 71-related Brainstem Encephalitis.

PubMed

Liu, Kun; Zhou, Yongjin; Cui, Shihan; Song, Jiawen; Ye, Peipei; Xiang, Wei; Huang, Xiaoyan; Chen, Yiping; Yan, Zhihan; Ye, Xinjian

2018-04-05

Brainstem encephalitis is the most common neurologic complication after enterovirus 71 infection. The involvement of brainstem, especially the dorsal medulla oblongata, can cause severe sequelae or death in children with enterovirus 71 infection. We aimed to determine the prevalence of dorsal medulla oblongata involvement in children with enterovirus 71-related brainstem encephalitis (EBE) by using conventional MRI and to evaluate the value of dorsal medulla oblongata involvement in outcome prediction. 46 children with EBE were enrolled in the study. All subjects underwent a 1.5 Tesla MR examination of the brain. The disease distribution and clinical data were collected. Dichotomized outcomes (good versus poor) at longer than 6 months were available for 28 patients. Logistic regression was used to determine whether the MRI-confirmed dorsal medulla oblongata involvement resulted in improved clinical outcome prediction when compared with other location involvement. Of the 46 patients, 35 had MRI evidence of dorsal medulla oblongata involvement, 32 had pons involvement, 10 had midbrain involvement, and 7 had dentate nuclei involvement. Patients with dorsal medulla oblongata involvement or multiple area involvement were significantly more often in the poor outcome group than in the good outcome group. Logistic regression analysis showed that dorsal medulla oblongata involvement was the most significant single variable in outcome prediction (predictive accuracy, 90.5%), followed by multiple area involvement, age, and initial glasgow coma scale score. Dorsal medulla oblongata involvement on conventional MRI correlated significantly with poor outcomes in EBE children, improved outcome prediction when compared with other clinical and disease location variables, and was most predictive when combined with multiple area involvement, glasgow coma scale score and age.

Predicting human protein function with multi-task deep neural networks.

PubMed

Fa, Rui; Cozzetto, Domenico; Wan, Cen; Jones, David T

2018-01-01

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning

Dopamine neurons share common response function for reward prediction error

PubMed Central

Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

2016-01-01

Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

Genetic Adaptation to Climate in White Spruce Involves Small to Moderate Allele Frequency Shifts in Functionally Diverse Genes

PubMed Central

Hornoy, Benjamin; Pavy, Nathalie; Gérardi, Sébastien; Beaulieu, Jean; Bousquet, Jean

2015-01-01

Understanding the genetic basis of adaptation to climate is of paramount importance for preserving and managing genetic diversity in plants in a context of climate change. Yet, this objective has been addressed mainly in short-lived model species. Thus, expanding knowledge to nonmodel species with contrasting life histories, such as forest trees, appears necessary. To uncover the genetic basis of adaptation to climate in the widely distributed boreal conifer white spruce (Picea glauca), an environmental association study was conducted using 11,085 single nucleotide polymorphisms representing 7,819 genes, that is, approximately a quarter of the transcriptome. Linear and quadratic regressions controlling for isolation-by-distance, and the Random Forest algorithm, identified several dozen genes putatively under selection, among which 43 showed strongest signals along temperature and precipitation gradients. Most of them were related to temperature. Small to moderate shifts in allele frequencies were observed. Genes involved encompassed a wide variety of functions and processes, some of them being likely important for plant survival under biotic and abiotic environmental stresses according to expression data. Literature mining and sequence comparison also highlighted conserved sequences and functions with angiosperm homologs. Our results are consistent with theoretical predictions that local adaptation involves genes with small frequency shifts when selection is recent and gene flow among populations is high. Accordingly, genetic adaptation to climate in P. glauca appears to be complex, involving many independent and interacting gene functions, biochemical pathways, and processes. From an applied perspective, these results shall lead to specific functional/association studies in conifers and to the development of markers useful for the conservation of genetic resources. PMID:26560341

Concomitant prediction of function and fold at the domain level with GO-based profiles.

PubMed

Lopez, Daniel; Pazos, Florencio

2013-01-01

Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required.We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.

Executive function predicts artificial language learning

PubMed Central

Kapa, Leah L.; Colombo, John

2017-01-01

Previous research suggests executive function (EF) advantages among bilinguals compared to monolingual peers, and these advantages are generally attributed to experience controlling two linguistic systems. However, the possibility that the relationship between bilingualism and EF might be bidirectional has not been widely considered; while experience with two languages might improve EF, better EF skills might also facilitate language learning. In the current studies, we tested whether adults’ and preschool children’s EF abilities predicted success in learning a novel artificial language. After controlling for working memory and English receptive vocabulary, adults’ artificial language performance was predicted by their inhibitory control ability (Study 1) and children’s performance was predicted by their attentional monitoring and shifting ability (Study 2). These findings provide preliminary evidence suggesting that EF processes may be employed during initial stages of language learning, particularly vocabulary acquisition, and support the possibility of a bidirectional relationship between EF and language acquisition. PMID:29129958

Comparison of two bioinformatics tools used to characterize the microbial diversity and predictive functional attributes of microbial mats from Lake Obersee, Antarctica.

PubMed

Koo, Hyunmin; Hakim, Joseph A; Morrow, Casey D; Eipers, Peter G; Davila, Alfonso; Andersen, Dale T; Bej, Asim K

2017-09-01

In this study, using NextGen sequencing of the collective 16S rRNA genes obtained from two sets of samples collected from Lake Obersee, Antarctica, we compared and contrasted two bioinformatics tools, PICRUSt and Tax4Fun. We then developed an R script to assess the taxonomic and predictive functional profiles of the microbial communities within the samples. Taxa such as Pseudoxanthomonas, Planctomycetaceae, Cyanobacteria Subsection III, Nitrosomonadaceae, Leptothrix, and Rhodobacter were exclusively identified by Tax4Fun that uses SILVA database; whereas PICRUSt that uses Greengenes database uniquely identified Pirellulaceae, Gemmatimonadetes A1-B1, Pseudanabaena, Salinibacterium and Sinobacteraceae. Predictive functional profiling of the microbial communities using Tax4Fun and PICRUSt separately revealed common metabolic capabilities, while also showing specific functional IDs not shared between the two approaches. Combining these functional predictions using a customized R script revealed a more inclusive metabolic profile, such as hydrolases, oxidoreductases, transferases; enzymes involved in carbohydrate and amino acid metabolisms; and membrane transport proteins known for nutrient uptake from the surrounding environment. Our results present the first molecular-phylogenetic characterization and predictive functional profiles of the microbial mat communities in Lake Obersee, while demonstrating the efficacy of combining both the taxonomic assignment information and functional IDs using the R script created in this study for a more streamlined evaluation of predictive functional profiles of microbial communities. Copyright © 2017 Elsevier B.V. All rights reserved.

Protein function prediction--the power of multiplicity.

PubMed

Rentzsch, Robert; Orengo, Christine A

2009-04-01

Advances in experimental and computational methods have quietly ushered in a new era in protein function annotation. This 'age of multiplicity' is marked by the notion that only the use of multiple tools, multiple evidence and considering the multiple aspects of function can give us the broad picture that 21st century biology will need to link and alter micro- and macroscopic phenotypes. It might also help us to undo past mistakes by removing errors from our databases and prevent us from producing more. On the downside, multiplicity is often confusing. We therefore systematically review methods and resources for automated protein function prediction, looking at individual (biochemical) and contextual (network) functions, respectively.

Urine peptidome analysis predicts risk of end-stage renal disease and reveals proteolytic pathways involved in autosomal dominant polycystic kidney disease progression.

PubMed

Pejchinovski, Martin; Siwy, Justyna; Metzger, Jochen; Dakna, Mohammed; Mischak, Harald; Klein, Julie; Jankowski, Vera; Bae, Kyongtae T; Chapman, Arlene B; Kistler, Andreas D

2017-03-01

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by slowly progressive bilateral renal cyst growth ultimately resulting in loss of kidney function and end-stage renal disease (ESRD). Disease progression rate and age at ESRD are highly variable. Therapeutic interventions therefore require early risk stratification of patients and monitoring of disease progression in response to treatment. We used a urine peptidomic approach based on capillary electrophoresis-mass-spectrometry (CE-MS) to identify potential biomarkers reflecting the risk for early progression to ESRD in the Consortium of Radiologic Imaging in Polycystic Kidney Disease (CRISP) cohort. A biomarker-based classifier consisting of 20 urinary peptides allowed the prediction of ESRD within 10-13 years of follow-up in patients 24-46 years of age at baseline. The performance of the biomarker score approached that of height-adjusted total kidney volume (htTKV) and the combination of the biomarker panel with htTKV improved prediction over either one alone. In young patients (<24 years at baseline), the same biomarker model predicted a 30 mL/min/1.73 m 2 glomerular filtration rate decline over 8 years. Sequence analysis of the altered urinary peptides and the prediction of the involved proteases by in silico analysis revealed alterations in distinct proteolytic pathways, in particular matrix metalloproteinases and cathepsins. We developed a urinary test that accurately predicts relevant clinical outcomes in ADPKD patients and suggests altered proteolytic pathways involved in disease progression. © The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

A remark on fractional differential equation involving I-function

NASA Astrophysics Data System (ADS)

Mishra, Jyoti

2018-02-01

The present paper deals with the solution of the fractional differential equation using the Laplace transform operator and its corresponding properties in the fractional calculus; we derive an exact solution of a complex fractional differential equation involving a special function known as I-function. The analysis of the some fractional integral with two parameters is presented using the suggested Theorem 1. In addition, some very useful corollaries are established and their proofs presented in detail. Some obtained exact solutions are depicted to see the effect of each fractional order. Owing to the wider applicability of the I-function, we can conclude that, the obtained results in our work generalize numerous well-known results obtained by specializing the parameters.

Impairment in Occupational Functioning and Adult ADHD: The Predictive Utility of Executive Function (EF) Ratings Versus EF Tests

PubMed Central

Barkley, Russell A.; Murphy, Kevin R.

2010-01-01

Attention deficit hyperactivity disorder (ADHD) is associated with deficits in executive functioning (EF). ADHD in adults is also associated with impairments in major life activities, particularly occupational functioning. We investigated the extent to which EF deficits assessed by both tests and self-ratings contributed to the degree of impairment in 11 measures involving self-reported occupational problems, employer reported workplace adjustment, and clinician rated occupational adjustment. Three groups of adults were recruited as a function of their severity of ADHD: ADHD diagnosis (n = 146), clinical controls self-referring for ADHD but not diagnosed with it (n = 97), and community controls (n = 109). Groups were combined and regression analyses revealed that self-ratings of EF were significantly predictive of impairments in all 11 measures of occupational adjustment. Although several tests of EF also did so, they contributed substantially less than did the EF ratings, particularly when analyzed jointly with the ratings. We conclude that EF deficits contribute to the impairments in occupational functioning that occur in conjunction with adult ADHD. Ratings of EF in daily life contribute more to such impairments than do EF tests, perhaps because, as we hypothesize, each assesses a different level in the hierarchical organization of EF as a meta-construct. PMID:20197297

An Influence Function Method for Predicting Store Aerodynamic Characteristics during Weapon Separation,

DTIC Science & Technology

1981-05-14

8217 AO-Ail 777 GRUMMAN AEROSPACE CORP BETHPAGE NY F/G 20/4 AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC C--ETCCU) MAY 8 1 R MEYER, A...CENKO, S YARDS UNCLASSIFIED N ’.**~~N**n I EHEEKI j~j .25 Q~4 111110 111_L 5. AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC...extended to their logical conclusion one is led quite naturally to consideration of an " Influence Function Method" for I predicting store aerodynamic

Aortic stiffness predicts functional outcome in patients after ischemic stroke.

PubMed

Gasecki, Dariusz; Rojek, Agnieszka; Kwarciany, Mariusz; Kubach, Marlena; Boutouyrie, Pierre; Nyka, Walenty; Laurent, Stephane; Narkiewicz, Krzysztof

2012-02-01

Increased aortic stiffness (measured by carotid-femoral pulse wave velocity) and central augmentation index have been shown to independently predict cardiovascular events, including stroke. We studied whether pulse wave velocity and central augmentation index predict functional outcome after ischemic stroke. In a prospective study, we enrolled 99 patients with acute ischemic stroke (age 63.7 ± 12.4 years, admission National Institutes of Health Stroke Scale score 6.6 ± 6.6, mean ± SD). Carotid-femoral pulse wave velocity and central augmentation index (SphygmoCor) were measured 1 week after stroke onset. Functional outcome was evaluated 90 days after stroke using the modified Rankin Scale with modified Rankin Scale score of 0 to 1 considered an excellent outcome. In univariate analysis, low carotid-femoral pulse wave velocity (P=0.000001) and low central augmentation index (P=0.028) were significantly associated with excellent stroke outcome. Age, severity of stroke, presence of previous stroke, diabetes, heart rate, and peripheral pressures also predicted stroke functional outcome. In multivariate analysis, the predictive value of carotid-femoral pulse wave velocity (<9.4 m/s) remained significant (OR, 0.21; 95% CI, 0.06-0.79; P=0.02) after adjustment for age, National Institutes of Health Stroke Scale score on admission, and presence of previous stroke. By contrast, central augmentation index had no significant predictive value after adjustment. This study indicates that aortic stiffness is an independent predictor of functional outcome in patients with acute ischemic stroke.

Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease.

PubMed

Lin, Qi; Rosenberg, Monica D; Yoo, Kwangsun; Hsu, Tiffany W; O'Connell, Thomas P; Chun, Marvin M

2018-01-01

Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE PAGES

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

2015-05-15

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

Prospective Prediction of Functional Difficulties among Recently Separated Veterans

DTIC Science & Technology

2014-01-01

while factors following separation from the military have a primary role in predicting functional difficulties during reintegration into civilian...and protective factors for functional difficulties among Veterans. In a sample of recently separated Marines, we used stepwise logistic and multiple...military, posttraumatic stress disorder, prospective, PTSD, reintegration, risk factors , Veterans, work functioning . INTRODUCTION Studies suggest that Iraq

Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach.

PubMed

Foong, Hui Foh; Hamid, Tengku Aizan; Ibrahim, Rahimah; Haron, Sharifah Azizah; Shahar, Suzana

2018-01-01

The aim of this study was to identify the predictors of elderly's cognitive function based on biopsychosocial and cognitive reserve perspectives. The study included 2322 community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, biomarkers, psychosocial status, disability, and cognitive function. A biopsychosocial model of cognitive function was developed to test variables' predictive power on cognitive function. Statistical analyses were performed using SPSS (version 15.0) in conjunction with Analysis of Moment Structures Graphics (AMOS 7.0). The estimated theoretical model fitted the data well. Psychosocial stress and metabolic syndrome (MetS) negatively predicted cognitive function and psychosocial stress appeared as a main predictor. Socio-demographic characteristics, except gender, also had significant effects on cognitive function. However, disability failed to predict cognitive function. Several factors together may predict cognitive function in the Malaysian elderly population, and the variance accounted for it is large enough to be considered substantial. Key factor associated with the elderly's cognitive function seems to be psychosocial well-being. Thus, psychosocial well-being should be included in the elderly assessment, apart from medical conditions, both in clinical and community setting.

Graph pyramids for protein function prediction

PubMed Central

2015-01-01

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Do parents' exercise habits predict 13-18-year-old adolescents' involvement in sport?

PubMed

Sukys, Saulius; Majauskienė, Daiva; Cesnaitiene, Vida J; Karanauskiene, Diana

2014-09-01

This study examined links between parents' exercise habits and adolescents' participation in sports activities, considering the aspects of gender and age. It was hypothesized that regular exercise by both parents would be related to children's involvement in sport regardless of their gender and age. Moreover, it was hypothesized that children's sports activities would be more strongly related to their father's exercise activities. The study also examined the links between parents' exercise habits and children's motivation for sports. It was hypothesized that competition motives would be more important for children whose parents exercised regularly. The research sample included 2335 students from the seventh (n = 857), ninth (n = 960) and eleventh (n = 518) grades of various Lithuanian schools. The study used a questionnaire survey method, which revealed the links between parents' exercise habits and their children's participation in sport. Assessment of data for girls and boys showed that daughters' participation in sport could be predicted by both their fathers' and mothers' exercise habits, but sons' sports activities could be predicted only by the regular physical activities of their fathers. The assessment of children's sporting activities according to age revealed links between parental exercising and the engagement of older (15-16 years old), but not younger adolescents (13-14 years old). Analysis of sports motivation showed that competition motives were more important for boys than for girls. Fitness, well-being and appearance motives were more important for older adolescents (15-18 years old), while competition motives were more important for younger adolescents (13-14 years old). Research revealed the relationship between children's sport motives and fathers' exercise habits, while examination of mothers' exercise revealed no difference. Key pointsParental exercising significantly predicts adolescents' engagement in sport. Daughter's engagement in sport is

Identification of Resting State Networks Involved in Executive Function.

PubMed

Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

2016-06-01

The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.

Psychological commitment as a mediator of the relationship between involvement and loyalty

Treesearch

Joohyun Lee; Alan Graefe

2002-01-01

This study tested the ill-understood issues of involvement and loyalty relations. Even though many studies have indicated that loyalty is a function of involvement, only minimal agreement has been reached on the extent to which the constructs of involvement would predict repeat participation. A structural model is developed that relates members' involvement and...

The influence of a wall function on turbine blade heat transfer prediction

NASA Technical Reports Server (NTRS)

Whitaker, Kevin W.

1989-01-01

The second phase of a continuing investigation to improve the prediction of turbine blade heat transfer coefficients was completed. The present study specifically investigated how a numeric wall function in the turbulence model of a two-dimensional boundary layer code, STAN5, affected heat transfer prediction capabilities. Several sources of inaccuracy in the wall function were identified and then corrected or improved. Heat transfer coefficient predictions were then obtained using each one of the modifications to determine its effect. Results indicated that the modifications made to the wall function can significantly affect the prediction of heat transfer coefficients on turbine blades. The improvement in accuracy due the modifications is still inconclusive and is still being investigated.

Predicting functional decline and survival in amyotrophic lateral sclerosis.

PubMed

Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

2017-01-01

Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

The Function and Catalysis of 2-Oxoglutarate-Dependent Oxygenases Involved in Plant Flavonoid Biosynthesis

PubMed Central

Cheng, Ai-Xia; Han, Xiao-Juan; Wu, Yi-Feng; Lou, Hong-Xiang

2014-01-01

Flavonoids are secondary metabolites derived from phenylalanine and acetate metabolism. They fulfil a variety of functions in plants and have health benefits for humans. During the synthesis of the tricyclic flavonoid natural products in plants, oxidative modifications to the central C ring are catalyzed by four of FeII and 2-oxoglutarate dependent (2-ODD) oxygenases, namely flavone synthase I (FNS I), flavonol synthase (FLS), anthocyanidin synthase (ANS) and flavanone 3β-hydroxylase (FHT). FNS I, FLS and ANS are involved in desaturation of C2–C3 of flavonoids and FHT in hydroxylation of C3. FNS I, which is restricted to the Apiaceae species and in rice, is predicted to have evolved from FHT by duplication. Due to their sequence similarity and substrate specificity, FLS and ANS, which interact with the α surface of the substrate, belong to a group of dioxygenases having a broad substrate specificity, while FNS I and FHT are more selective, and interact with the naringenin β surface. Here, we summarize recent findings regarding the function of the four 2-ODD oxygenases and the relationship between their catalytic activity, their polypeptide sequence and their tertiary structure. PMID:24434621

Protein function prediction using neighbor relativity in protein-protein interaction network.

PubMed

Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

2013-04-01

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Genetic Adaptation to Climate in White Spruce Involves Small to Moderate Allele Frequency Shifts in Functionally Diverse Genes.

PubMed

Hornoy, Benjamin; Pavy, Nathalie; Gérardi, Sébastien; Beaulieu, Jean; Bousquet, Jean

2015-11-11

Understanding the genetic basis of adaptation to climate is of paramount importance for preserving and managing genetic diversity in plants in a context of climate change. Yet, this objective has been addressed mainly in short-lived model species. Thus, expanding knowledge to nonmodel species with contrasting life histories, such as forest trees, appears necessary. To uncover the genetic basis of adaptation to climate in the widely distributed boreal conifer white spruce (Picea glauca), an environmental association study was conducted using 11,085 single nucleotide polymorphisms representing 7,819 genes, that is, approximately a quarter of the transcriptome.Linear and quadratic regressions controlling for isolation-by-distance, and the Random Forest algorithm, identified several dozen genes putatively under selection, among which 43 showed strongest signals along temperature and precipitation gradients. Most of them were related to temperature. Small to moderate shifts in allele frequencies were observed. Genes involved encompassed a wide variety of functions and processes, some of them being likely important for plant survival under biotic and abiotic environmental stresses according to expression data. Literature mining and sequence comparison also highlighted conserved sequences and functions with angiosperm homologs.Our results are consistent with theoretical predictions that local adaptation involves genes with small frequency shifts when selection is recent and gene flow among populations is high. Accordingly, genetic adaptation to climate in P. glauca appears to be complex, involving many independent and interacting gene functions, biochemical pathways, and processes. From an applied perspective, these results shall lead to specific functional/association studies in conifers and to the development of markers useful for the conservation of genetic resources. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.

PubMed

Yoo, Kwangsun; Rosenberg, Monica D; Hsu, Wei-Ting; Zhang, Sheng; Li, Chiang-Shan R; Scheinost, Dustin; Constable, R Todd; Chun, Marvin M

2018-02-15

Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with

Predicting the thermodynamic stability of double-perovskite halides from density functional theory

DOE PAGES

Han, Dan; Zhang, Tao; Huang, Menglin; ...

2018-05-24

Recently, a series of double-perovskite halide compounds such as Cs 2AgBiCl 6 and Cs 2AgBiBr 6 have attracted intensive interest as promising alternatives to the solar absorber material CH 3NH 3PbI 3 because they are Pb-free and may exhibit enhanced stability. The thermodynamic stability of a number of double-perovskite halides has been predicted based on density functional theory (DFT) calculations of compound formation energies. In this paper, we found that the stability prediction can be dependent on the approximations used for the exchange-correlation functionals, e.g., the DFT calculations using the widely used Perdew, Burke, Ernzerhof (PBE) functional predict that Csmore » 2AgBiBr 6 is thermodynamically unstable against phase-separation into the competing phases such as AgBr, Cs 2AgBr 3, Cs 3Bi 2Br 9, etc., obviously inconsistent with the good stability observed experimentally. The incorrect prediction by the PBE calculation results from its failure to predict the correct ground-state structures of AgBr, AgCl, and CsCl. By contrast, the DFT calculations based on local density approximation, optB86b-vdW, and optB88-vdW functionals predict the ground-state structures of these binary halides correctly. Furthermore, the optB88-vdW functional is found to give the most accurate description of the lattice constants of the double-perovskite halides and their competing phases. Given these two aspects, we suggest that the optB88-vdW functional should be used for predicting thermodynamic stability in the future high-throughput computational material design or the construction of the Materials Genome database for new double-perovskite halides. As a result, using different exchange-correlation functionals has little influence on the dispersion of the conduction and the valence bands near the electronic bandgap; however, the calculated bandgap can be affected indirectly by the optimized lattice constant, which varies for different functionals.« less

Predicting the thermodynamic stability of double-perovskite halides from density functional theory

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

Han, Dan; Zhang, Tao; Huang, Menglin

Recently, a series of double-perovskite halide compounds such as Cs 2AgBiCl 6 and Cs 2AgBiBr 6 have attracted intensive interest as promising alternatives to the solar absorber material CH 3NH 3PbI 3 because they are Pb-free and may exhibit enhanced stability. The thermodynamic stability of a number of double-perovskite halides has been predicted based on density functional theory (DFT) calculations of compound formation energies. In this paper, we found that the stability prediction can be dependent on the approximations used for the exchange-correlation functionals, e.g., the DFT calculations using the widely used Perdew, Burke, Ernzerhof (PBE) functional predict that Csmore » 2AgBiBr 6 is thermodynamically unstable against phase-separation into the competing phases such as AgBr, Cs 2AgBr 3, Cs 3Bi 2Br 9, etc., obviously inconsistent with the good stability observed experimentally. The incorrect prediction by the PBE calculation results from its failure to predict the correct ground-state structures of AgBr, AgCl, and CsCl. By contrast, the DFT calculations based on local density approximation, optB86b-vdW, and optB88-vdW functionals predict the ground-state structures of these binary halides correctly. Furthermore, the optB88-vdW functional is found to give the most accurate description of the lattice constants of the double-perovskite halides and their competing phases. Given these two aspects, we suggest that the optB88-vdW functional should be used for predicting thermodynamic stability in the future high-throughput computational material design or the construction of the Materials Genome database for new double-perovskite halides. As a result, using different exchange-correlation functionals has little influence on the dispersion of the conduction and the valence bands near the electronic bandgap; however, the calculated bandgap can be affected indirectly by the optimized lattice constant, which varies for different functionals.« less

Analysis of molecular markers as predictive factors of lymph node involvement in breast carcinoma.

PubMed

Paula, Luciana Marques; De Moraes, Luis Henrique Ferreira; Do Canto, Abaeté Leite; Dos Santos, Laurita; Martin, Airton Abrahão; Rogatto, Silvia Regina; De Azevedo Canevari, Renata

2017-01-01

Nodal status is the most significant independent prognostic factor in breast cancer. Identification of molecular markers would allow stratification of patients who require surgical assessment of lymph nodes from the large numbers of patients for whom this surgical procedure is unnecessary, thus leading to a more accurate prognosis. However, up to now, the reported studies are preliminary and controversial, and although hundreds of markers have been assessed, few of them have been used in clinical practice for treatment or prognosis in breast cancer. The purpose of the present study was to determine whether protein phosphatase Mg2+/Mn2+ dependent 1D, β-1,3-N-acetylglucosaminyltransferase, neural precursor cell expressed, developmentally down-regulated 9, prohibitin, phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5), phosphatidylinositol-5-phosphate 4-kinase type IIα, TRF1-interacting ankyrin-related ADP-ribose polymerase 2, BCL2 associated agonist of cell death, G2 and S-phase expressed 1 and PAX interacting protein 1 genes, described as prognostic markers in breast cancer in a previous microarray study, are also predictors of lymph node involvement in breast carcinoma Reverse transcription-quantitative polymerase chain reaction analysis was performed on primary breast tumor tissues from women with negative lymph node involvement (n=27) compared with primary tumor tissues from women with positive lymph node involvement (n=23), and was also performed on primary tumors and paired lymph node metastases (n=11). For all genes analyzed, only the PIK3R5 gene exhibited differential expression in samples of primary tumors with positive lymph node involvement compared with primary tumors with negative lymph node involvement (P=0.0347). These results demonstrate that the PIK3R5 gene may be considered predictive of lymph node involvement in breast carcinoma. Although the other genes evaluated in the present study have been previously characterized to be involved with

Weaknesses in executive functioning predict the initiating of adolescents' alcohol use.

PubMed

Peeters, Margot; Janssen, Tim; Monshouwer, Karin; Boendermaker, Wouter; Pronk, Thomas; Wiers, Reinout; Vollebergh, Wilma

2015-12-01

Recently, it has been suggested that impairments in executive functioning might be risk factors for the onset of alcohol use rather than a result of heavy alcohol use. In the present study, we examined whether two aspects of executive functioning, working memory and response inhibition, predicted the first alcoholic drink and first binge drinking episode in young adolescents using discrete survival analyses. Adolescents were selected from several Dutch secondary schools including both mainstream and special education (externalizing behavioral problems). Participants were 534 adolescents between 12 and 14 years at baseline. Executive functioning and alcohol use were assessed four times over a period of two years. Working memory uniquely predicted the onset of first drink (p=.01) and first binge drinking episode (p=.04) while response inhibition only uniquely predicted the initiating of the first drink (p=.01). These results suggest that the association of executive functioning and alcohol consumption found in former studies cannot simply be interpreted as an effect of alcohol consumption, as weaknesses in executive functioning, found in alcohol naïve adolescents, predict the initiating of (binge) drinking. Though, prolonged and heavy alcohol use might further weaken already existing deficiencies. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

PubMed

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (p<.001), associations with productivity (r=.51), mental health (r=.48), and distress (r=.47). The screener (WFS-H) had good predictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

Predicting the preferences for involvement in medical decision making among patients with mental disorders

PubMed Central

Michaelis, Svea; Kriston, Levente; Härter, Martin; Watzke, Birgit; Schulz, Holger; Melchior, Hanne

2017-01-01

Background The involvement of patients in medical decision making has been investigated widely in somatic diseases. However, little is known about the preferences for involvement and variables that could predict these preferences in patients with mental disorders. Objective This study aims to determine what roles mentally ill patients actually want to assume when making medical decisions and to identify the variables that could predict this role, including patients’ self-efficacy. Method Demographic and clinical data of 798 patients with mental disorders from three psychotherapeutic units in Germany were elicited using self-report questionnaires. Control preference was measured using the Control Preferences Scale, and patients’ perceived self-efficacy was assessed using the Self-Efficacy Scale. Bivariate and multivariate regression analyses were conducted to investigate the associations between patient variables and control preference. Results Most patients preferred a collaborative role (57.5%), followed by a semi passive (21.2%), a partly autonomous (16.2%), an autonomous (2.8%) and a fully passive (2.3%) role when making medical decisions. Age, sex, diagnosis, employment status, medical pretreatment and perceived self-efficacy were associated with the preference for involvement in the multivariate logistic model. Conclusion Our results confirm the preferences for involvement in medical decisions of mentally ill patients. We reconfirmed previous findings that older patients prefer a shared role over an autonomous role and that subjects with a high qualification prefer a more autonomous role over a shared role. The knowledge about predictors may help strengthen treatment effectiveness because matching the preferred and actual role preferences has been shown to improve clinical outcome. PMID:28837621

Predicting the preferences for involvement in medical decision making among patients with mental disorders.

PubMed

Michaelis, Svea; Kriston, Levente; Härter, Martin; Watzke, Birgit; Schulz, Holger; Melchior, Hanne

2017-01-01

The involvement of patients in medical decision making has been investigated widely in somatic diseases. However, little is known about the preferences for involvement and variables that could predict these preferences in patients with mental disorders. This study aims to determine what roles mentally ill patients actually want to assume when making medical decisions and to identify the variables that could predict this role, including patients' self-efficacy. Demographic and clinical data of 798 patients with mental disorders from three psychotherapeutic units in Germany were elicited using self-report questionnaires. Control preference was measured using the Control Preferences Scale, and patients' perceived self-efficacy was assessed using the Self-Efficacy Scale. Bivariate and multivariate regression analyses were conducted to investigate the associations between patient variables and control preference. Most patients preferred a collaborative role (57.5%), followed by a semi passive (21.2%), a partly autonomous (16.2%), an autonomous (2.8%) and a fully passive (2.3%) role when making medical decisions. Age, sex, diagnosis, employment status, medical pretreatment and perceived self-efficacy were associated with the preference for involvement in the multivariate logistic model. Our results confirm the preferences for involvement in medical decisions of mentally ill patients. We reconfirmed previous findings that older patients prefer a shared role over an autonomous role and that subjects with a high qualification prefer a more autonomous role over a shared role. The knowledge about predictors may help strengthen treatment effectiveness because matching the preferred and actual role preferences has been shown to improve clinical outcome.

Dynamic prediction in functional concurrent regression with an application to child growth.

PubMed

Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

2018-04-15

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

High Precision Prediction of Functional Sites in Protein Structures

PubMed Central

Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

2014-01-01

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

Do functional tests predict low back pain?

PubMed

Takala, E P; Viikari-Juntura, E

2000-08-15

A cohort of 307 nonsymptomatic workers and another cohort of 123 workers with previous episodes of low back pain were followed up for 2 years. The outcomes were measured by symptoms, medical consultations, and sick leaves due to low back disorders. To study the predictive value of a set of tests measuring the physical performance of the back in a working population. The hypothesis was that subjects with poor functional capacity are liable to back disorders. Reduced functional performance has been associated with back pain. There are few data to show whether reduced functional capacity is a cause or a consequence of pain. Mobility of the trunk in forward and side bending, maximal isokinetic trunk extension, flexion and lifting strength, and static endurance of back extension were measured. Standing balance and foot reaction time were recorded with a force plate. Clinical tests for the provocation of back or leg pain were performed. Gender, workload, age, and anthropometrics were managed as potential confounders in the analysis. Marked overlapping was seen in the measures of the subjects with different outcomes. Among the nonsymptomatic subjects, low performance in tests of mobility and standing balance was associated with future back disorders. Among workers with previous episodes of back pain, low isokinetic extension strength, poor standing balance, and positive clinical signs predicted future pain. Some associations were found between the functional tests and future low back pain. The wide variation in the results questions the value of the tests in health examinations (e.g., in screening or surveillance of low back disorders).

Self-Determination and Student Involvement in Functional Assessment: Innovative Practices

ERIC Educational Resources Information Center

Wehmeyer, Michael L.; Baker, Daniel J.; Blumberg, Richard; Harrison, Richard

2004-01-01

The fundamental feature that distinguishes positive behavior support (PBS) from previous generations of applied behavior analysis is its focus on the remediation of deficient contexts that are determined to be the source of the problem. Determining this source involves conducting a functional assessment. This innovative practices article presents…

Children on the Autism Spectrum: Grandmother Involvement and Family Functioning

ERIC Educational Resources Information Center

Sullivan, Alison; Winograd, Greta; Verkuilen, Jay; Fish, Marian C.

2012-01-01

Background: This study investigated associations between the presence of a child with autism or Asperger's disorder in the family, family functioning and grandmother experiences with the goal of better understanding grandparent involvement in the lives of grandchildren on the autism spectrum and their families. Methods: Mothers and grandmothers of…

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

Interactions between callous unemotional behaviors and executive function in early childhood predict later socioemotional functioning

PubMed Central

Waller, Rebecca; Hyde, Luke W.; Baskin-Sommers, Arielle; Olson, Sheryl L.

2018-01-01

Callous unemotional (CU) behaviors are linked to aggression, behavior problems, and difficulties in peer relationships in children and adolescents. However, few studies have examined whether early childhood CU behaviors predict aggression or peer-rejection during late-childhood or potential moderation of this relationship by executive function. The current study examined whether the interaction of CU behaviors and executive function in early childhood predicted different forms of aggression in late-childhood, including proactive, reactive, and relational aggression, as well as how much children were liked by their peers. Data from cross-informant reports and multiple observational tasks were collected from a high-risk sample (N=240; female=118) at ages 3 and 10 years old. Parent reports of CU behaviors at age 3 predicted teacher reports of reactive, proactive, and relational aggression, as well as lower peer-liking at age 10. Moderation analysis showed that specifically at high levels of CU behaviors and low levels of observed executive function, children were reported by teachers as showing greater reactive and proactive aggression, and were less-liked by peers. Findings demonstrate that early childhood CU behaviors and executive function have unique main and interactive effects on both later aggression and lower peer-liking even when taking into account stability in behavior problems over time. By elucidating how CU behaviors and deficits in executive function potentiate each other during early childhood, we can better characterize the emergence of severe and persistent behavior and interpersonal difficulties across development. PMID:27418255

Metagenomic Functional Potential Predicts Degradation Rates of a Model Organophosphorus Xenobiotic in Pesticide Contaminated Soils

PubMed Central

Jeffries, Thomas C.; Rayu, Smriti; Nielsen, Uffe N.; Lai, Kaitao; Ijaz, Ali; Nazaries, Loic; Singh, Brajesh K.

2018-01-01

Chemical contamination of natural and agricultural habitats is an increasing global problem and a major threat to sustainability and human health. Organophosphorus (OP) compounds are one major class of contaminant and can undergo microbial degradation, however, no studies have applied system-wide ecogenomic tools to investigate OP degradation or use metagenomics to understand the underlying mechanisms of biodegradation in situ and predict degradation potential. Thus, there is a lack of knowledge regarding the functional genes and genomic potential underpinning degradation and community responses to contamination. Here we address this knowledge gap by performing shotgun sequencing of community DNA from agricultural soils with a history of pesticide usage and profiling shifts in functional genes and microbial taxa abundance. Our results showed two distinct groups of soils defined by differing functional and taxonomic profiles. Degradation assays suggested that these groups corresponded to the organophosphorus degradation potential of soils, with the fastest degrading community being defined by increases in transport and nutrient cycling pathways and enzymes potentially involved in phosphorus metabolism. This was against a backdrop of taxonomic community shifts potentially related to contamination adaptation and reflecting the legacy of exposure. Overall our results highlight the value of using holistic system-wide metagenomic approaches as a tool to predict microbial degradation in the context of the ecology of contaminated habitats. PMID:29515526

Technique Feature Analysis or Involvement Load Hypothesis: Estimating Their Predictive Power in Vocabulary Learning.

PubMed

Gohar, Manoochehr Jafari; Rahmanian, Mahboubeh; Soleimani, Hassan

2018-02-05

Vocabulary learning has always been a great concern and has attracted the attention of many researchers. Among the vocabulary learning hypotheses, involvement load hypothesis and technique feature analysis have been proposed which attempt to bring some concepts like noticing, motivation, and generation into focus. In the current study, 90 high proficiency EFL students were assigned into three vocabulary tasks of sentence making, composition, and reading comprehension in order to examine the power of involvement load hypothesis and technique feature analysis frameworks in predicting vocabulary learning. It was unraveled that involvement load hypothesis cannot be a good predictor, and technique feature analysis was a good predictor in pretest to posttest score change and not in during-task activity. The implications of the results will be discussed in the light of preparing vocabulary tasks.

Benchmarking the Performance of Exchange-Correlation Functionals for Predicting Two-Photon Absorption Strengths.

PubMed

Beerepoot, Maarten T P; Alam, Md Mehboob; Bednarska, Joanna; Bartkowiak, Wojciech; Ruud, Kenneth; Zaleśny, Robert

2018-06-15

The present work investigates the performance of exchange-correlation functionals in the prediction of two-photon absorption (2PA) strengths. For this purpose, we considered six common functionals used for studying 2PA processes and tested these on six organoboron chelates. The set consisted of two semilocal (PBE and BLYP), two hybrid (B3LYP and PBE0), and two range-separated (LC-BLYP and CAM-B3LYP) functionals. The RI-CC2 method was chosen as a reference level and was found to give results consistent with the experimental data that are available for three of the molecules considered. Of the six exchange-correlation functionals studied, only the range-separated functionals predict an ordering of the 2PA strengths that is consistent with experimental and RI-CC2 results. Even though the range-separated functionals predict correct relative trends, the absolute values for the 2PA strengths are underestimated by a factor of 2-6 for the molecules considered. An in-depth analysis, on the basis of the derived generalized few-state model expression for the 2PA strength for a coupled-cluster wave function, reveals that the problem with these functionals can be linked to underestimated excited-state dipole moments and, to a lesser extent, overestimated excitation energies. The semilocal and hybrid functionals exhibit less predictable errors and a variation in the 2PA strengths in disagreement with the reference results. The semilocal and hybrid functionals show smaller average errors than the range-separated functionals, but our analysis reveals that this is due to fortuitous error cancellation between excitation energies and the transition dipole moments. Our results constitute a warning against using currently available exchange-correlation functionals in the prediction of 2PA strengths and highlight the need for functionals that correctly describe the electron density of excited electronic states.

Predicting the stability of ternary intermetallics with density functional theory and machine learning

NASA Astrophysics Data System (ADS)

Schmidt, Jonathan; Chen, Liming; Botti, Silvana; Marques, Miguel A. L.

2018-06-01

We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures. Our results suggest that there may be ˜10 times more stable compounds in these phases than previously known. These are mostly metallic and non-magnetic. While the use of machine learning reduces the overall calculation cost by around 75%, some limitations of its predictive power still exist, in particular, for compounds involving the second-row of the periodic table or magnetic elements.

EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

PubMed

Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

2014-07-01

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Sum rules across the unpolarized Compton processes involving generalized polarizabilities and moments of nucleon structure functions

NASA Astrophysics Data System (ADS)

Lensky, Vadim; Hagelstein, Franziska; Pascalutsa, Vladimir; Vanderhaeghen, Marc

2018-04-01

We derive two new sum rules for the unpolarized doubly virtual Compton scattering process on a nucleon, which establish novel low-Q2 relations involving the nucleon's generalized polarizabilities and moments of the nucleon's unpolarized structure functions F1(x ,Q2) and F2(x ,Q2). These relations facilitate the determination of some structure constants which can only be accessed in off-forward doubly virtual Compton scattering, not experimentally accessible at present. We perform an empirical determination for the proton and compare our results with a next-to-leading-order chiral perturbation theory prediction. We also show how these relations may be useful for a model-independent determination of the low-Q2 subtraction function in the Compton amplitude, which enters the two-photon-exchange contribution to the Lamb shift of (muonic) hydrogen. An explicit calculation of the Δ (1232 )-resonance contribution to the muonic-hydrogen 2 P -2 S Lamb shift yields -1 ±1 μ eV , confirming the previously conjectured smallness of this effect.

Predicting functional mitral stenosis after restrictive annuloplasty for ischemic mitral regurgitation.

PubMed

Li, Baotong; Wu, Hengchao; Sun, Hansong; Xu, Jianping; Song, Yunhu; Wang, Wei; Wang, Shuiyun

2018-03-07

Although it has been realized that restrictive mitral valve annuloplasty (MVA) may result in clinically significant functional mitral stenosis (MS), it still cannot be predicted. The purpose of this study was to identify risk factors for clinically significant functional MS following restrictive MVA surgery for chronic ischemic mitral regurgitation (CIMR). 114 patients who underwent restrictive MVA with coronary artery bypass grafting (CABG) for treatment of CIMR were retrospectively reviewed. Clinically significant functional MS was defined as resting transmitral peak pressure gradient (PPG) ≥ 13 mmHg. During the follow-up period (range 6-12 months), 28 (24.56%) patients developed clinically significant functional MS. The PPG at follow-up was significantly higher than that measured in the early postoperative stage (3-5 days after surgery). Moreover, there was a linear correlation between the two measurements (r = 0.398, p < 0.001). Annuloplasty size ≤ 27 mm and early postoperative PPG ≥ 7.4 mmHg could predict clinically significant functional MS at 6-12 months postoperatively. Chronic ischemic mitral regurgitation patients treated with restrictive MVA and CABG have significant increases in PPG postoperatively. Annuloplasty size ≤ 27 mm and early postoperative PPG ≥ 7.4 mmHg can predict clinically significant functional MS at 6-12 months after surgery.

Taper Functions for Predicting Product Volumes in Natural Shortleaf Pines

Treesearch

Robert M. Farrar; Paul A. Murphy

1987-01-01

Taper (stem-profile) functions are presented for natural shortleaf pine (Pinus echinata Mill.) trees growing in the West Gulf area. These functions, when integrated, permit the prediction of volume between any two heights on a stem and, conversely by iteration, the volume between any two diameters on a stem. Examples are given of use of the functions...

Do Parents’ Exercise Habits Predict 13–18-Year-Old Adolescents’ Involvement in Sport?

PubMed Central

Sukys, Saulius; Majauskienė, Daiva; Cesnaitiene, Vida J.; Karanauskiene, Diana

2014-01-01

This study examined links between parents’ exercise habits and adolescents’ participation in sports activities, considering the aspects of gender and age. It was hypothesized that regular exercise by both parents would be related to children’s involvement in sport regardless of their gender and age. Moreover, it was hypothesized that children’s sports activities would be more strongly related to their father’s exercise activities. The study also examined the links between parents’ exercise habits and children’s motivation for sports. It was hypothesized that competition motives would be more important for children whose parents exercised regularly. The research sample included 2335 students from the seventh (n = 857), ninth (n = 960) and eleventh (n = 518) grades of various Lithuanian schools. The study used a questionnaire survey method, which revealed the links between parents’ exercise habits and their children’s participation in sport. Assessment of data for girls and boys showed that daughters’ participation in sport could be predicted by both their fathers’ and mothers’ exercise habits, but sons’ sports activities could be predicted only by the regular physical activities of their fathers. The assessment of children’s sporting activities according to age revealed links between parental exercising and the engagement of older (15–16 years old), but not younger adolescents (13–14 years old). Analysis of sports motivation showed that competition motives were more important for boys than for girls. Fitness, well-being and appearance motives were more important for older adolescents (15–18 years old), while competition motives were more important for younger adolescents (13–14 years old). Research revealed the relationship between children’s sport motives and fathers’ exercise habits, while examination of mothers’ exercise revealed no difference. Key points Parental exercising significantly predicts adolescents�

Natural killer cell function predicts severe infection in kidney transplant recipients.

PubMed

Dendle, Claire; Gan, Poh-Yi; Polkinghorne, Kevan R; Ngui, James; Stuart, Rhonda L; Kanellis, John; Thursky, Karin; Mulley, William R; Holdsworth, Stephen

2018-04-30

The aim of this study was to determine if natural killer cell number (CD3 - /CD16 ± /CD56 ± ) and cytotoxic killing function predicts severity and frequency of infection in kidney transplant recipients. A cohort of 168 kidney transplant recipients with stable graft function underwent assessment of natural killer cell number and functional killing capacity immediately prior to entry into this prospective study. Participants were followed for 2 years for development of severe infection, defined as hospitalization for infection. Area under receiver operating characteristic (AUROC) curves were used to evaluate the accuracy of natural killer cell number and function for predicting severe infection. Adjusted odds ratios were determined by logistic regression. Fifty-nine kidney transplant recipients (35%) developed severe infection and 7 (4%) died. Natural killer cell function was a better predictor of severe infection than natural killer cell number: AUROC 0.84 and 0.75, respectively (P = .018). Logistic regression demonstrated that after adjustment for age, transplant function, transplant duration, mycophenolate use, and increasing natural killer function (odds ratio [OR] 0.82, 95% confidence interval [CI] 0.74-0.90; P < .0001) but not natural killer number (OR 0.96, 95% CI 0.93-1.00; P = .051) remained significantly associated with a reduced likelihood of severe infection. Natural killer cell function predicts severe infection in kidney transplant recipients. © 2018 The American Society of Transplantation and the American Society of Transplant Surgeons.

A simple attention test in the acute phase of a major depressive episode is predictive of later functional remission.

PubMed

Cléry-Melin, Marie-Laure; Gorwood, Philip

2017-02-01

Functional recovery after a major depressive episode (MDE) requires both clinical remission and preservation of cognitive skills. As attentional deficit may persist after remission, leading to functional impairment, its role as a prognosis marker needs to be considered. Five hundred eight depressed outpatients (DSM-IV) were assessed at baseline for clinical symptoms (QIDS-SR), social functioning (Sheehan Disability Scale, SDS) and attention through the d2 test of attention and the trail making test, simple tests, respectively, requiring to quote or to interconnect relevant items. All patients were treated by agomelatine, and examined 6 to 8 weeks after baseline to assess clinical remission (QIDS-SR ≤ 5) and/or functional remission (SDS ≤ 6). At follow up, 154 patients (31%) were in clinical and functional remission. Shorter cumulative duration of prior depression, shorter present MDE, and two parameters of the d2 test of attention were predictive of such positive outcome, the number of omission mistakes (F1) being the only one still significantly predictive (P < .05) with a multivariate approach. F1 was unchanged after remission, patients with less than 11 mistakes had a 2.27 times increased chance to reach full remission, and a dose-response relationship was observed, with a regular increase of positive outcome for less mistakes. The number of omission mistakes (F1) of the d2 test of attention was a stable marker, being predictive of, and with a dose-effect for, clinical plus functional remission. It may constitute a specific marker of attentional deficit, involved in the resilience process that enables individuals to develop more adequate strategies to cope with everyday functional activities. © 2016 Wiley Periodicals, Inc.

Water hammer prediction and control: the Green's function method

NASA Astrophysics Data System (ADS)

Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi

2012-04-01

By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.

Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task

NASA Astrophysics Data System (ADS)

Qin, Yulin; Sohn, Myeong-Ho; Anderson, John R.; Stenger, V. Andrew; Fissell, Kate; Goode, Adam; Carter, Cameron S.

2003-04-01

Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

Infancy to Age Five: Predicting Fathers' Involvement.

ERIC Educational Resources Information Center

Bailey, William T.

Four years after a study of paternal involvement among intact, middle-class families with an infant, a follow-up was conducted of 26 of the still intact families to determine the stability of paternal involvement and the psychological predictors of fathers' behavior at the time. Paternal involvement was assessed at both times in terms of care,…

Non sentinel node involvement prediction for sentinel node micrometastases in breast cancer: nomogram validation and comparison with other models.

PubMed

Houvenaeghel, Gilles; Bannier, Marie; Nos, Claude; Giard, Sylvia; Mignotte, Herve; Jacquemier, Jocelyne; Martino, Marc; Esterni, Benjamin; Belichard, Catherine; Classe, Jean-Marc; Tunon de Lara, Christine; Cohen, Monique; Payan, Raoul; Blanchot, Jerome; Rouanet, Philippe; Penault-Llorca, Frederique; Bonnier, Pascal; Fournet, Sandrine; Agostini, Aubert; Marchal, Frederique; Garbay, Jean-Remi

2012-04-01

The risk of non sentinel node (NSN) involvement varies in function of the characteristics of sentinel nodes (SN) and primary tumor. Our aim was to determine and validate a statistical tool (a nomogram) able to predict the risk of NSN involvement in case of SN micro or sub-micrometastasis of breast cancer. We have compared this monogram with other models described in the literature. We have collected data on 905 patients, then 484 other patients, to build and validate the nomogram and compare it with other published scores and nomograms. Multivariate analysis conducted on the data of the first cohort allowed us to define a nomogram based on 5 criteria: the method of SN detection (immunohistochemistry or by standard coloration with HES); the ratio of positive SN out of total removed SN; the pathologic size of the tumor; the histological type; and the presence (or not) of lympho-vascular invasion. The nomogram developed here is the only one dedicated to micrometastasis and developed on the basis of two large cohorts. The results of this statistical tool in the calculation of the risk of NSN involvement is similar to those of the MSKCC (the similarly more effective nomogram according to the literature), with a lower rate of false negatives. this nomogram is dedicated specifically to cases of SN involvement by metastasis lower or equal to 2 mm. It could be used in clinical practice in the way to omit ALND when the risk of NSN involvement is low. Copyright © 2011 Elsevier Ltd. All rights reserved.

Plant functional traits predict green roof ecosystem services.

PubMed

Lundholm, Jeremy; Tran, Stephanie; Gebert, Luke

2015-02-17

Plants make important contributions to the services provided by engineered ecosystems such as green roofs. Ecologists use plant species traits as generic predictors of geographical distribution, interactions with other species, and ecosystem functioning, but this approach has been little used to optimize engineered ecosystems. Four plant species traits (height, individual leaf area, specific leaf area, and leaf dry matter content) were evaluated as predictors of ecosystem properties and services in a modular green roof system planted with 21 species. Six indicators of ecosystem services, incorporating thermal, hydrological, water quality, and carbon sequestration functions, were predicted by the four plant traits directly or indirectly via their effects on aggregate ecosystem properties, including canopy density and albedo. Species average height and specific leaf area were the most useful traits, predicting several services via effects on canopy density or growth rate. This study demonstrates that easily measured plant traits can be used to select species to optimize green roof performance across multiple key services.

Characterization and prediction of residues determining protein functional specificity.

PubMed

Capra, John A; Singh, Mona

2008-07-01

Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/. Supplementary data are available at Bioinformatics online.

Parent Involvement and Children's Academic and Social Development in Elementary School

PubMed Central

El Nokali, Nermeen E.; Bachman, Heather J.; Votruba-Drzal, Elizabeth

2010-01-01

Data from the NICHD Study of Early Childcare and Youth Development (N= 1364) were used to investigate children's trajectories of academic and social development across first, third and fifth grade. Hierarchical linear modeling was used to examine within- and between-child associations among maternal- and teacher-reports of parent involvement and children's standardized achievement scores, social skills, and problem behaviors. Findings suggest that within-child improvements in parent involvement predict declines in problem behaviors and improvements in social skills but do not predict changes in achievement. Between-child analyses demonstrated that children with highly involved parents had enhanced social functioning and fewer behavior problems. Similar patterns of findings emerged for teacher- and parent-reports of parent involvement. Implications for policy and practice are discussed. PMID:20573118

Neural Networks Involved in Adolescent Reward Processing: An Activation Likelihood Estimation Meta-Analysis of Functional Neuroimaging Studies

PubMed Central

Silverman, Merav H.; Jedd, Kelly; Luciana, Monica

2015-01-01

Behavioral responses to, and the neural processing of, rewards change dramatically during adolescence and may contribute to observed increases in risk-taking during this developmental period. Functional MRI (fMRI) studies suggest differences between adolescents and adults in neural activation during reward processing, but findings are contradictory, and effects have been found in non-predicted directions. The current study uses an activation likelihood estimation (ALE) approach for quantitative meta-analysis of functional neuroimaging studies to: 1) confirm the network of brain regions involved in adolescents’ reward processing, 2) identify regions involved in specific stages (anticipation, outcome) and valence (positive, negative) of reward processing, and 3) identify differences in activation likelihood between adolescent and adult reward-related brain activation. Results reveal a subcortical network of brain regions involved in adolescent reward processing similar to that found in adults with major hubs including the ventral and dorsal striatum, insula, and posterior cingulate cortex (PCC). Contrast analyses find that adolescents exhibit greater likelihood of activation in the insula while processing anticipation relative to outcome and greater likelihood of activation in the putamen and amygdala during outcome relative to anticipation. While processing positive compared to negative valence, adolescents show increased likelihood for activation in the posterior cingulate cortex (PCC) and ventral striatum. Contrasting adolescent reward processing with the existing ALE of adult reward processing (Liu et al., 2011) reveals increased likelihood for activation in limbic, frontolimbic, and striatal regions in adolescents compared with adults. Unlike adolescents, adults also activate executive control regions of the frontal and parietal lobes. These findings support hypothesized elevations in motivated activity during adolescence. PMID:26254587

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

PubMed

Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

2017-06-15

Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Characterization and Prediction of Chemical Functions and ...

EPA Pesticide Factsheets

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-b

Idiopathic Pulmonary Fibrosis: Gender-Age-Physiology Index Stage for Predicting Future Lung Function Decline.

PubMed

Salisbury, Margaret L; Xia, Meng; Zhou, Yueren; Murray, Susan; Tayob, Nabihah; Brown, Kevin K; Wells, Athol U; Schmidt, Shelley L; Martinez, Fernando J; Flaherty, Kevin R

2016-02-01

Idiopathic pulmonary fibrosis is a progressive lung disease with variable course. The Gender-Age-Physiology (GAP) Index and staging system uses clinical variables to stage mortality risk. It is unknown whether clinical staging predicts future decline in pulmonary function. We assessed whether the GAP stage predicts future pulmonary function decline and whether interval pulmonary function change predicts mortality after accounting for stage. Patients with idiopathic pulmonary fibrosis (N = 657) were identified retrospectively at three tertiary referral centers, and baseline GAP stages were assessed. Mixed models were used to describe average trajectories of FVC and diffusing capacity of the lung for carbon monoxide (Dlco). Multivariable Cox proportional hazards models were used to assess whether declines in pulmonary function ≥ 10% in 6 months predict mortality after accounting for GAP stage. Over a 2-year period, GAP stage was not associated with differences in yearly lung function decline. After accounting for stage, a 10% decrease in FVC or Dlco over 6 months independently predicted death or transplantation (FVC hazard ratio, 1.37; Dlco hazard ratio, 1.30; both, P ≤ .03). Patients with GAP stage 2 with declining pulmonary function experienced a survival profile similar to patients with GAP stage 3, with 1-year event-free survival of 59.3% (95% CI, 49.4-67.8) vs 56.9% (95% CI, 42.2-69.1). Baseline GAP stage predicted death or lung transplantation but not the rate of future pulmonary function decline. After accounting for GAP stage, a decline of ≥ 10% over 6 months independently predicted death or lung transplantation. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

No Parent Left Behind: Predicting Parental Involvement in Adolescents' Education within a Sociodemographically Diverse Population

ERIC Educational Resources Information Center

Park, Sira; Holloway, Susan D.

2013-01-01

Numerous studies have investigated the utility of the Hoover-Dempsey and Sandler (HDS) model for predicting parents' involvement in students' education. Yet, the model has yet to be thoroughly evaluated with respect to youth who are (a) in high school and (b) from sociodemographically diverse families. Using a nationally representative sample of…

Prediction of biological functions on glycosylation site migrations in human influenza H1N1 viruses.

PubMed

Sun, Shisheng; Wang, Qinzhe; Zhao, Fei; Chen, Wentian; Li, Zheng

2012-01-01

Protein glycosylation alteration is typically employed by various viruses for escaping immune pressures from their hosts. Our previous work had shown that not only the increase of glycosylation sites (glycosites) numbers, but also glycosite migration might be involved in the evolution of human seasonal influenza H1N1 viruses. More importantly, glycosite migration was likely a more effectively alteration way for the host adaption of human influenza H1N1 viruses. In this study, we provided more bioinformatics and statistic evidences for further predicting the significant biological functions of glycosite migration in the host adaptation of human influenza H1N1 viruses, by employing homology modeling and in silico protein glycosylation of representative HA and NA proteins as well as amino acid variability analysis at antigenic sites of HA and NA. The results showed that glycosite migrations in human influenza viruses have at least five possible functions: to more effectively mask the antigenic sites, to more effectively protect the enzymatic cleavage sites of neuraminidase (NA), to stabilize the polymeric structures, to regulate the receptor binding and catalytic activities and to balance the binding activity of hemagglutinin (HA) with the release activity of NA. The information here can provide some constructive suggestions for the function research related to protein glycosylation of influenza viruses, although these predictions still need to be supported by experimental data.

COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps.

PubMed

Chang, Yi-Chien; Hu, Zhenjun; Rachlin, John; Anton, Brian P; Kasif, Simon; Roberts, Richard J; Steffen, Martin

2016-01-04

The COMBREX database (COMBREX-DB; combrex.bu.edu) is an online repository of information related to (i) experimentally determined protein function, (ii) predicted protein function, (iii) relationships among proteins of unknown function and various types of experimental data, including molecular function, protein structure, and associated phenotypes. The database was created as part of the novel COMBREX (COMputational BRidges to EXperiments) effort aimed at accelerating the rate of gene function validation. It currently holds information on ∼ 3.3 million known and predicted proteins from over 1000 completely sequenced bacterial and archaeal genomes. The database also contains a prototype recommendation system for helping users identify those proteins whose experimental determination of function would be most informative for predicting function for other proteins within protein families. The emphasis on documenting experimental evidence for function predictions, and the prioritization of uncharacterized proteins for experimental testing distinguish COMBREX from other publicly available microbial genomics resources. This article describes updates to COMBREX-DB since an initial description in the 2011 NAR Database Issue. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

PubMed

Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

2009-02-15

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http

Functional mechanisms involved in the internal inhibition of taboo words.

PubMed

Severens, Els; Kühn, Simone; Hartsuiker, Robert J; Brass, Marcel

2012-04-01

The present study used functional magnetic resonance imaging to investigate brain processes associated with the inhibition of socially undesirable speech. It is tested whether the inhibition of undesirable speech is solely related to brain areas associated with classical stop signal tasks or rather also involves brain areas involved in endogenous self-control. During the experiment, subjects had to do a SLIP task, which was designed to elicit taboo or neutral spoonerisms. Here we show that the internal inhibition of taboo words activates the right inferior frontal gyrus, an area that has previously been associated with externally triggered inhibition. This finding strongly suggests that external social rules become internalized and act as a stop-signal.

Functional mechanisms involved in the internal inhibition of taboo words

PubMed Central

Kühn, Simone; Hartsuiker, Robert J.; Brass, Marcel

2012-01-01

The present study used functional magnetic resonance imaging to investigate brain processes associated with the inhibition of socially undesirable speech. It is tested whether the inhibition of undesirable speech is solely related to brain areas associated with classical stop signal tasks or rather also involves brain areas involved in endogenous self-control. During the experiment, subjects had to do a SLIP task, which was designed to elicit taboo or neutral spoonerisms. Here we show that the internal inhibition of taboo words activates the right inferior frontal gyrus, an area that has previously been associated with externally triggered inhibition. This finding strongly suggests that external social rules become internalized and act as a stop-signal. PMID:21609970

Predicting maize phenology: Intercomparison of functions for developmental response to temperature

USDA-ARS?s Scientific Manuscript database

Accurate prediction of phenological development in maize is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were t...

Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2012-07-01

Despite ever-increasing amounts of sequence and functional genomics data, there is still a deficiency of functional annotation for many newly sequenced proteins. For Mycobacterium tuberculosis (MTB), more than half of its genome is still uncharacterized, which hampers the search for new drug targets within the bacterial pathogen and limits our understanding of its pathogenicity. As for many other genomes, the annotations of proteins in the MTB proteome were generally inferred from sequence homology, which is effective but its applicability has limitations. We have carried out large-scale biological data integration to produce an MTB protein functional interaction network. Protein functional relationships were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and additional functional interactions from microarray, sequence and protein signature data. The confidence level of protein relationships in the additional functional interaction data was evaluated using a dynamic data-driven scoring system. This functional network has been used to predict functions of uncharacterized proteins using Gene Ontology (GO) terms, and the semantic similarity between these terms measured using a state-of-the-art GO similarity metric. To achieve better trade-off between improvement of quality, genomic coverage and scalability, this prediction is done by observing the key principles driving the biological organization of the functional network. This study yields a new functionally characterized MTB strain CDC1551 proteome, consisting of 3804 and 3698 proteins out of 4195 with annotations in terms of the biological process and molecular function ontologies, respectively. These data can contribute to research into the Development of effective anti-tubercular drugs with novel biological mechanisms of action. Copyright © 2011 Elsevier B.V. All rights reserved.

Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation.

PubMed

Klein, Julie; Eales, James; Zürbig, Petra; Vlahou, Antonia; Mischak, Harald; Stevens, Robert

2013-04-01

In this study, we have developed Proteasix, an open-source peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. We developed a curated cleavage site (CS) database, containing 3500 entries about human protease/CS combinations. On top of this database, we built a tool, Proteasix, which allows CS retrieval and protease associations from a list of peptides. To establish the proof of concept of the approach, we used a list of 1388 peptides identified from human urine samples, and compared the prediction to the analysis of 1003 randomly generated amino acid sequences. Metalloprotease activity was predominantly involved in urinary peptide generation, and more particularly to peptides associated with extracellular matrix remodelling, compared to proteins from other origins. In comparison, random sequences returned almost no results, highlighting the specificity of the prediction. This study provides a tool that can facilitate linking of identified protein fragments to predicted protease activity, and therefore into presumed mechanisms of disease. Experiments are needed to confirm the in silico hypotheses; nevertheless, this approach may be of great help to better understand molecular mechanisms of disease, and define new biomarkers, and therapeutic targets. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions.

PubMed

Wuchty, S; Rajagopala, S V; Blazie, S M; Parrish, J R; Khuri, S; Finley, R L; Uetz, P

2017-01-01

The functions of roughly a third of all proteins in Streptococcus pneumoniae , a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae . We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae , the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

PubMed Central

Rajagopala, S. V.; Blazie, S. M.; Parrish, J. R.; Khuri, S.; Finley, R. L.

2017-01-01

ABSTRACT The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. PMID:28744484

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

PubMed

Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

Quantifying confidence in density functional theory predictions of magnetic ground states

NASA Astrophysics Data System (ADS)

Houchins, Gregory; Viswanathan, Venkatasubramanian

2017-10-01

Density functional theory (DFT) simulations, at the generalized gradient approximation (GGA) level, are being routinely used for material discovery based on high-throughput descriptor-based searches. The success of descriptor-based material design relies on eliminating bad candidates and keeping good candidates for further investigation. While DFT has been widely successfully for the former, oftentimes good candidates are lost due to the uncertainty associated with the DFT-predicted material properties. Uncertainty associated with DFT predictions has gained prominence and has led to the development of exchange correlation functionals that have built-in error estimation capability. In this work, we demonstrate the use of built-in error estimation capabilities within the BEEF-vdW exchange correlation functional for quantifying the uncertainty associated with the magnetic ground state of solids. We demonstrate this approach by calculating the uncertainty estimate for the energy difference between the different magnetic states of solids and compare them against a range of GGA exchange correlation functionals as is done in many first-principles calculations of materials. We show that this estimate reasonably bounds the range of values obtained with the different GGA functionals. The estimate is determined as a postprocessing step and thus provides a computationally robust and systematic approach to estimating uncertainty associated with predictions of magnetic ground states. We define a confidence value (c-value) that incorporates all calculated magnetic states in order to quantify the concurrence of the prediction at the GGA level and argue that predictions of magnetic ground states from GGA level DFT is incomplete without an accompanying c-value. We demonstrate the utility of this method using a case study of Li-ion and Na-ion cathode materials and the c-value metric correctly identifies that GGA-level DFT will have low predictability for NaFePO4F . Further, there

Predicting future forests: Understanding diverse phenological responses within a community and functional trait framework

NASA Astrophysics Data System (ADS)

Wolkovich, E. M.; Flynn, D. F. B.

2016-12-01

In recent years increasing attention has focused on plant phenology as an important indicator of the biological impacts of climate change, as many plants have shifted their leafing and flowering earlier with increasing temperatures. As data have accumulated, researchers have found a link between phenological responses to warming and plant performance and invasions. Such work suggests phenology may not only be a major impact of warming, but a critical predictor of future plant performance. Yet alongside this increasing interest in phenology, important issues remain unanswered: responses to warming for species at the same site or in the same genus vary often by weeks or more and the explanatory power of phenology for performance and invasions when analyzed across diverse datasets remains low. We propose progress can come from explicitly considering phenology within a community context and as a critical plant trait correlated with other major plant functional traits. Here, we lay out a framework for our proposal: specifically we review how we expect phenology and phenological cues of different species within a community to vary and what other functional traits are predicted to co-vary with phenological traits. Much research currently suggests phenology is a critical functional trait that is shaped strongly by the environment. Plants are expected to adjust their phenologies to avoid periods of high abiotic risk and/or high competition. Thus we may expect phenology to correlate strongly to other traits involved in mitigating risk and high competition. Results from recent meta-analyses as well as experimental and observational research from 28 species in northeastern North American temperate forests suggest that species within a community show the predicted diversified set of phenological cues. We review early work on links to other functional traits and in closing review how these correlations may in turn determine the diversity of phenological responses observed for

Executive functioning predicts reading, mathematics, and theory of mind during the elementary years.

PubMed

Cantin, Rachelle H; Gnaedinger, Emily K; Gallaway, Kristin C; Hesson-McInnis, Matthew S; Hund, Alycia M

2016-06-01

The goal of this study was to specify how executive functioning components predict reading, mathematics, and theory of mind performance during the elementary years. A sample of 93 7- to 10-year-old children completed measures of working memory, inhibition, flexibility, reading, mathematics, and theory of mind. Path analysis revealed that all three executive functioning components (working memory, inhibition, and flexibility) mediated age differences in reading comprehension, whereas age predicted mathematics and theory of mind directly. In addition, reading mediated the influence of executive functioning components on mathematics and theory of mind, except that flexibility also predicted mathematics directly. These findings provide important details about the development of executive functioning, reading, mathematics, and theory of mind during the elementary years. Copyright © 2016 Elsevier Inc. All rights reserved.

Dynamic functional brain networks involved in simple visual discrimination learning.

PubMed

Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

2014-10-01

Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

PubMed

Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

2014-03-12

Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

Executive Functions Predict the Success of Top-Soccer Players

PubMed Central

Vestberg, Torbjörn; Gustafson, Roland; Maurex, Liselotte; Ingvar, Martin; Petrovic, Predrag

2012-01-01

While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player. The present study used standardized neuropsychological assessment tools assessing players' general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group. The result shows that both HD and LD players had significantly better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later. The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players. PMID:22496850

An empirical propellant response function for combustion stability predictions

NASA Technical Reports Server (NTRS)

Hessler, R. O.

1980-01-01

An empirical response function model was developed for ammonium perchlorate propellants to supplant T-burner testing at the preliminary design stage. The model was developed by fitting a limited T-burner data base, in terms of oxidizer size and concentration, to an analytical two parameter response function expression. Multiple peaks are predicted, but the primary effect is of a single peak for most formulations, with notable bulges for the various AP size fractions. The model was extended to velocity coupling with the assumption that dynamic response was controlled primarily by the solid phase described by the two parameter model. The magnitude of velocity coupling was then scaled using an erosive burning law. Routine use of the model for stability predictions on a number of propulsion units indicates that the model tends to overpredict propellant response. It is concluded that the model represents a generally conservative prediction tool, suited especially for the preliminary design stage when T-burner data may not be readily available. The model work included development of a rigorous summation technique for pseudopropellant properties and of a concept for modeling ordered packing of particulates.

Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

NASA Astrophysics Data System (ADS)

Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

2010-10-01

We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

Predicting College Students' Positive Psychology Associated Traits with Executive Functioning Dimensions

ERIC Educational Resources Information Center

Marshall, Seth

2016-01-01

More research is needed that investigates how positive psychology-associated traits are predicted by neurocognitive processes. Correspondingly, the purpose of this study was to ascertain how, and to what extent, four traits, namely, grit, optimism, positive affect, and life satisfaction were predicted by the executive functioning (EF) dimensions…

Towards fully automated structure-based function prediction in structural genomics: a case study.

PubMed

Watson, James D; Sanderson, Steve; Ezersky, Alexandra; Savchenko, Alexei; Edwards, Aled; Orengo, Christine; Joachimiak, Andrzej; Laskowski, Roman A; Thornton, Janet M

2007-04-13

As the global Structural Genomics projects have picked up pace, the number of structures annotated in the Protein Data Bank as hypothetical protein or unknown function has grown significantly. A major challenge now involves the development of computational methods to assign functions to these proteins accurately and automatically. As part of the Midwest Center for Structural Genomics (MCSG) we have developed a fully automated functional analysis server, ProFunc, which performs a battery of analyses on a submitted structure. The analyses combine a number of sequence-based and structure-based methods to identify functional clues. After the first stage of the Protein Structure Initiative (PSI), we review the success of the pipeline and the importance of structure-based function prediction. As a dataset, we have chosen all structures solved by the MCSG during the 5 years of the first PSI. Our analysis suggests that two of the structure-based methods are particularly successful and provide examples of local similarity that is difficult to identify using current sequence-based methods. No one method is successful in all cases, so, through the use of a number of complementary sequence and structural approaches, the ProFunc server increases the chances that at least one method will find a significant hit that can help elucidate function. Manual assessment of the results is a time-consuming process and subject to individual interpretation and human error. We present a method based on the Gene Ontology (GO) schema using GO-slims that can allow the automated assessment of hits with a success rate approaching that of expert manual assessment.

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

PubMed Central

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

2011-01-01

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

Hydrological-niche models predict water plant functional group distributions in diverse wetland types.

PubMed

Deane, David C; Nicol, Jason M; Gehrig, Susan L; Harding, Claire; Aldridge, Kane T; Goodman, Abigail M; Brookes, Justin D

2017-06-01

Human use of water resources threatens environmental water supplies. If resource managers are to develop policies that avoid unacceptable ecological impacts, some means to predict ecosystem response to changes in water availability is necessary. This is difficult to achieve at spatial scales relevant for water resource management because of the high natural variability in ecosystem hydrology and ecology. Water plant functional groups classify species with similar hydrological niche preferences together, allowing a qualitative means to generalize community responses to changes in hydrology. We tested the potential for functional groups in making quantitative prediction of water plant functional group distributions across diverse wetland types over a large geographical extent. We sampled wetlands covering a broad range of hydrogeomorphic and salinity conditions in South Australia, collecting both hydrological and floristic data from 687 quadrats across 28 wetland hydrological gradients. We built hydrological-niche models for eight water plant functional groups using a range of candidate models combining different surface inundation metrics. We then tested the predictive performance of top-ranked individual and averaged models for each functional group. Cross validation showed that models achieved acceptable predictive performance, with correct classification rates in the range 0.68-0.95. Model predictions can be made at any spatial scale that hydrological data are available and could be implemented in a geographical information system. We show the response of water plant functional groups to inundation is consistent enough across diverse wetland types to quantify the probability of hydrological impacts over regional spatial scales. © 2017 by the Ecological Society of America.

Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction.

PubMed

Xu, Yonghui; Min, Huaqing; Wu, Qingyao; Song, Hengjie; Ye, Bicui

2017-02-06

Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate Multi-Instance Learning (MIL) method for genome-wide protein function prediction under a usual assumption, the underlying distribution from testing data (target domain, i.e., TD) is the same as that from training data (source domain, i.e., SD). However, this assumption may be violated in real practice. To tackle this problem, in this paper, we propose a Multi-Instance Metric Transfer Learning (MIMTL) approach for genome-wide protein function prediction. In MIMTL, we first transfer the source domain distribution to the target domain distribution by utilizing the bag weights. Then, we construct a distance metric learning method with the reweighted bags. At last, we develop an alternative optimization scheme for MIMTL. Comprehensive experimental evidence on seven real-world organisms verifies the effectiveness and efficiency of the proposed MIMTL approach over several state-of-the-art methods.

Molecular characterization of HIV-1 Nef and ACOT8 interaction: insights from in silico structural predictions and in vitro functional assays

NASA Astrophysics Data System (ADS)

Serena, Michela; Giorgetti, Alejandro; Busato, Mirko; Gasparini, Francesca; Diani, Erica; Romanelli, Maria Grazia; Zipeto, Donato

2016-03-01

HIV-1 Nef interacts with several cellular proteins, among which the human peroxisomal thioesterase 8 (ACOT8). This interaction may be involved in the endocytosis regulation of membrane proteins and might modulate lipid composition in membrane rafts. Nef regions involved in the interaction have been experimentally characterized, whereas structural details of the ACOT8 protein are unknown. The lack of structural information hampers the comprehension of the functional consequences of the complex formation during HIV-1 infection. We modelled, through in silico predictions, the ACOT8 structure and we observed a high charge complementarity between Nef and ACOT8 surfaces, which allowed the identification of the ACOT8 putative contact points involved in the interaction. The predictions were validated by in vitro assays through the development of ACOT8 deletion mutants. Coimmunoprecipitation and immunofluorescence analyses showed that ACOT8 Arg45-Phe55 and Arg86-Pro93 regions are involved in Nef association. In addition, K91S mutation abrogated the interaction with Nef, indicating that Lys91 plays a key role in the interaction. Finally, when associated with ACOT8, Nef may be preserved from degradation. These findings improve the comprehension of the association between HIV-1 Nef and ACOT8, helping elucidating the biological effect of their interaction.

Evidence that specific executive functions predict symptom variance among schizophrenia patients with a predominantly negative symptom profile.

PubMed

Donohoe, Gary; Corvin, Aiden; Robertson, Ian H

2006-01-01

Although deficits in executive functioning in schizophrenia have been consistently reported, their precise relationship to symptomatology remains unclear. Recent approaches to executive functioning in nonschizophrenia studies have aimed to "fractionate" the individual cognitive processes involved. In this study, we hypothesised that if these processes are fractionable, then particular symptom syndromes may be selectively related to executive deficits. In particular, it was hoped that this approach could clarify whether negative and positive symptoms of schizophrenia are differentially related to particular aspects of executive/attentional functions. A total of 32 patients with schizophrenia and 16 matched controls were assessed on a series of tasks designed to tap the theoretically derived executive functions of Inhibition, Shifting set, Working memory, and Sustained attention. Negative symptoms were significantly predicted by performance on an "Inhibition" task (Stroop), and not by performance on any other task. Furthermore, for a subgroup of patients with predominantly negative symptoms variance in positive symptoms was only significantly predicted by performance on a set-shifting task (Visual Elevator), and not by performance on other tasks, including inhibition. Our results support the contention that negative symptoms can, at least partly, be conceived of as cognitive behaviours expressing specific executive deficits. Specifically, we discuss the possibility that negative symptoms may, in part, express a failure in response monitoring. We further suggest that the disordered metacognition resulting in positive symptoms may be mediated by cognitive flexibility in patients with a predominantly negative symptom profile.

Prediction of margin involvement and local recurrence after skin-sparing and simple mastectomy.

PubMed

Al-Himdani, S; Timbrell, S; Tan, K T; Morris, J; Bundred, N J

2016-07-01

Skin-sparing mastectomy (SSM) facilitates immediate breast reconstruction. We investigated locoregional recurrence rates after SSM compared with simple mastectomy and the factors predicting oncological failure. Patients with early breast cancer that underwent mastectomy between 2000 and 2005 at a single institution were studied to ascertain local and systemic recurrence rates between groups. Kaplan-Meier curves and log-rank test were used to evaluate disease-free survival. Patients (n = 577) underwent simple mastectomy (80%) or SSM (20%). Median follow up was 80 months. Patients undergoing SSM were of younger average age, less often had involved lymph nodes (22% vs 44%, p < 0.001), more often had DCIS present (79% vs 53%, p < 0.001) and involved margins (29% vs 15%, p = 0.001). Involved surgical margins were associated with large size (p = 0.001). The 8-year local recurrence (LR) rates were 7.9% for SSM and 5% for simple mastectomy respectively (p = 0.35). Predictors of locoregional recurrence were lymph node involvement (HR 8.0, for >4 nodes, p < 0.001) and involved surgical margins (HR 3.3, p = 0.002). In node negative patients, SSM was a predictor of locoregional recurrence (HR 4.8 [1.1, 19.9], p = 0.033). Delayed reconstruction is more appropriate for node positive early breast cancer after post-mastectomy radiotherapy. Re-excision of involved margins is essential to prevent local recurrence after mastectomy. Copyright © 2016 Elsevier Ltd, BASO ~ the Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Pretransplantation recipient regulatory T cell suppressive function predicts delayed and slow graft function after kidney transplantation.

PubMed

Nguyen, Minh-Tri J P; Fryml, Elise; Sahakian, Sossy K; Liu, Shuqing; Michel, Rene P; Lipman, Mark L; Mucsi, Istvan; Cantarovich, Marcelo; Tchervenkov, Jean I; Paraskevas, Steven

2014-10-15

Delayed graft function (DGF) and slow graft function (SGF) are a continuous spectrum of ischemia-reperfusion-related acute kidney injury (AKI) that increases the risk for acute rejection and graft loss after kidney transplantation. Regulatory T cells (Tregs) are critical in transplant tolerance and attenuate murine AKI. In this prospective observational cohort study, we evaluated whether pretransplantation peripheral blood recipient Treg frequency and suppressive function are predictors of DGF and SGF after kidney transplantation. Deceased donor kidney transplant recipients (n=53) were divided into AKI (n=37; DGF, n=10; SGF, n=27) and immediate graft function (n=16) groups. Pretransplantation peripheral blood CD4CD25FoxP3 Treg frequency was quantified by flow cytometry. Regulatory T-cell suppressive function was measured by suppression of autologous effector T-cell proliferation by Treg in co-culture. Pretransplantation Treg suppressive function, but not frequency, was decreased in AKI recipients (P<0.01). In univariate and multivariate analyses accounting for the effects of cold ischemic time and donor age, Treg suppressive function discriminated DGF from immediate graft function recipients in multinomial logistic regression (odds ratio, 0.77; P<0.01), accurately predicted AKI in receiver operating characteristic curve (area under the curve, 0.82; P<0.01), and predicted 14-day estimated glomerular filtration rate in linear regression (P<0.01). Our results indicate that recipient peripheral blood Treg suppressive function is a potential independent pretransplantation predictor of DGF and SGF.

An integrative approach to ortholog prediction for disease-focused and other functional studies.

PubMed

Hu, Yanhui; Flockhart, Ian; Vinayagam, Arunachalam; Bergwitz, Clemens; Berger, Bonnie; Perrimon, Norbert; Mohr, Stephanie E

2011-08-31

Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

Negative Example Selection for Protein Function Prediction: The NoGO Database

PubMed Central

Youngs, Noah; Penfold-Brown, Duncan; Bonneau, Richard; Shasha, Dennis

2014-01-01

Negative examples – genes that are known not to carry out a given protein function – are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html). PMID:24922051

A density functional theory based approach for predicting melting points of ionic liquids

DOE PAGES

Chen, Lihua; Bryantsev, Vyacheslav S.

2017-01-17

Accurate prediction of melting points of ILs is important both from the fundamental point of view and from the practical perspective for screening ILs with low melting points and broadening their utilization in a wider temperature range. In this work, we present an ab initio approach to calculating melting points of ILs with known crystal structures and illustrate its application for a series of 11 ILs containing imidazolium/pyrrolidinium cations and halide/polyatomic fluoro-containing anions. The melting point is determined as a temperature at which the Gibbs free energy of fusion is zero. The Gibbs free energy of fusion can be expressedmore » through the use of the Born-Fajans-Haber cycle via the lattice free energy of forming a solid IL from gaseous phase ions and the sum of the solvation free energies of ions comprising IL. Dispersion-corrected density functional theory (DFT) involving (semi)local (PBE-D3) and hybrid exchange-correlation (HSE06-D3) functionals is applied to estimate the lattice enthalpy, entropy, and free energy. The ions solvation free energies are calculated with the SMD-generic-IL solvation model at the M06-2X/6-31+G(d) level of theory under standard conditions. The melting points of ILs computed with the HSE06-D3 functional are in good agreement with the experimental data, with a mean absolute error of 30.5 K and a mean relative error of 8.5%. The model is capable of accurately reproducing the trends in melting points upon variation of alkyl substituents in organic cations and replacement one anion by another. The results verify that the lattice energies of ILs containing polyatomic fluoro-containing anions can be approximated reasonably well using the volume-based thermodynamic approach. However, there is no correlation of the computed lattice energies with molecular volume for ILs containing halide anions. Moreover, entropies of solid ILs follow two different linear relationships with molecular volume for halides and polyatomic fluoro

A density functional theory based approach for predicting melting points of ionic liquids

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

Chen, Lihua; Bryantsev, Vyacheslav S.

Accurate prediction of melting points of ILs is important both from the fundamental point of view and from the practical perspective for screening ILs with low melting points and broadening their utilization in a wider temperature range. In this work, we present an ab initio approach to calculating melting points of ILs with known crystal structures and illustrate its application for a series of 11 ILs containing imidazolium/pyrrolidinium cations and halide/polyatomic fluoro-containing anions. The melting point is determined as a temperature at which the Gibbs free energy of fusion is zero. The Gibbs free energy of fusion can be expressedmore » through the use of the Born-Fajans-Haber cycle via the lattice free energy of forming a solid IL from gaseous phase ions and the sum of the solvation free energies of ions comprising IL. Dispersion-corrected density functional theory (DFT) involving (semi)local (PBE-D3) and hybrid exchange-correlation (HSE06-D3) functionals is applied to estimate the lattice enthalpy, entropy, and free energy. The ions solvation free energies are calculated with the SMD-generic-IL solvation model at the M06-2X/6-31+G(d) level of theory under standard conditions. The melting points of ILs computed with the HSE06-D3 functional are in good agreement with the experimental data, with a mean absolute error of 30.5 K and a mean relative error of 8.5%. The model is capable of accurately reproducing the trends in melting points upon variation of alkyl substituents in organic cations and replacement one anion by another. The results verify that the lattice energies of ILs containing polyatomic fluoro-containing anions can be approximated reasonably well using the volume-based thermodynamic approach. However, there is no correlation of the computed lattice energies with molecular volume for ILs containing halide anions. Moreover, entropies of solid ILs follow two different linear relationships with molecular volume for halides and polyatomic fluoro

Predicting Multicomponent Adsorption Isotherms in Open-Metal Site Materials Using Force Field Calculations Based on Energy Decomposed Density Functional Theory.

PubMed

Heinen, Jurn; Burtch, Nicholas C; Walton, Krista S; Fonseca Guerra, Célia; Dubbeldam, David

2016-12-12

For the design of adsorptive-separation units, knowledge is required of the multicomponent adsorption behavior. Ideal adsorbed solution theory (IAST) breaks down for olefin adsorption in open-metal site (OMS) materials due to non-ideal donor-acceptor interactions. Using a density-function-theory-based energy decomposition scheme, we develop a physically justifiable classical force field that incorporates the missing orbital interactions using an appropriate functional form. Our first-principles derived force field shows greatly improved quantitative agreement with the inflection points, initial uptake, saturation capacity, and enthalpies of adsorption obtained from our in-house adsorption experiments. While IAST fails to make accurate predictions, our improved force field model is able to correctly predict the multicomponent behavior. Our approach is also transferable to other OMS structures, allowing the accurate study of their separation performances for olefins/paraffins and further mixtures involving complex donor-acceptor interactions. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Improved Displacement Transfer Functions for Structure Deformed Shape Predictions Using Discretely Distributed Surface Strains

NASA Technical Reports Server (NTRS)

Ko, William L.; Fleischer, Van Tran

2012-01-01

In the formulations of earlier Displacement Transfer Functions for structure shape predictions, the surface strain distributions, along a strain-sensing line, were represented with piecewise linear functions. To improve the shape-prediction accuracies, Improved Displacement Transfer Functions were formulated using piecewise nonlinear strain representations. Through discretization of an embedded beam (depth-wise cross section of a structure along a strain-sensing line) into multiple small domains, piecewise nonlinear functions were used to describe the surface strain distributions along the discretized embedded beam. Such piecewise approach enabled the piecewise integrations of the embedded beam curvature equations to yield slope and deflection equations in recursive forms. The resulting Improved Displacement Transfer Functions, written in summation forms, were expressed in terms of beam geometrical parameters and surface strains along the strain-sensing line. By feeding the surface strains into the Improved Displacement Transfer Functions, structural deflections could be calculated at multiple points for mapping out the overall structural deformed shapes for visual display. The shape-prediction accuracies of the Improved Displacement Transfer Functions were then examined in view of finite-element-calculated deflections using different tapered cantilever tubular beams. It was found that by using the piecewise nonlinear strain representations, the shape-prediction accuracies could be greatly improved, especially for highly-tapered cantilever tubular beams.

Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area.

PubMed

Fang, S; Liang, J; Qian, T; Wang, Y; Liu, X; Fan, X; Li, S; Wang, Y; Jiang, T

2017-10-01

The accuracy of preoperative blood oxygen level-dependent fMRI remains controversial. This study assessed the association between the anatomic location of a tumor and the accuracy of fMRI-based motor function mapping in diffuse lower-grade gliomas. Thirty-five patients with lower-grade gliomas involving motor areas underwent preoperative blood oxygen level-dependent fMRI scans with grasping tasks and received intraoperative direct cortical stimulation. Patients were classified into an overlapping group and a nonoverlapping group, depending on the extent to which blood oxygen level-dependent fMRI and direct cortical stimulation results concurred. Tumor location was quantitatively measured, including the shortest distance from the tumor to the hand knob and the deviation distance of the midpoint of the hand knob in the lesion hemisphere relative to the midline compared with the normal contralateral hemisphere. A 4-mm shortest distance from the tumor to the hand knob value was identified as optimal for differentiating the overlapping and nonoverlapping group with the receiver operating characteristic curve (sensitivity, 84.6%; specificity, 77.8%). The shortest distances from the tumor to the hand knob of ≤4 mm were associated with inaccurate fMRI-based localizations of the hand motor cortex. The shortest distances from the tumor to the hand knob were larger ( P = .002), and the deviation distances for the midpoint of the hand knob in the lesion hemisphere were smaller ( P = .003) in the overlapping group than in the nonoverlapping group. This study suggests that the shortest distance from the tumor to the hand knob and the deviation distance for the midpoint of the hand knob on the lesion hemisphere are predictive of the accuracy of blood oxygen level-dependent fMRI results. Smaller shortest distances from the tumor to the hand knob and larger deviation distances for the midpoint of hand knob on the lesion hemisphere are associated with less accuracy of motor cortex

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

PubMed

Wang, Yong-Cui; Wang, Yong; Yang, Zhi-Xia; Deng, Nai-Yang

2011-06-20

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

Preoperative Falls Predict Postoperative Falls, Functional Decline, and Surgical Complications.

PubMed

Kronzer, Vanessa L; Jerry, Michelle R; Ben Abdallah, Arbi; Wildes, Troy S; Stark, Susan L; McKinnon, Sherry L; Helsten, Daniel L; Sharma, Anshuman; Avidan, Michael S

2016-10-01

Falls are common and linked to morbidity. Our objectives were to characterize postoperative falls, and determine whether preoperative falls independently predicted postoperative falls (primary outcome), functional dependence, quality of life, complications, and readmission. This prospective cohort study included 7982 unselected patients undergoing elective surgery. Data were collected from the medical record, a baseline survey, and follow-up surveys approximately 30days and one year after surgery. Fall rates (per 100 person-years) peaked at 175 (hospitalization), declined to 140 (30-day survey), and then to 97 (one-year survey). After controlling for confounders, a history of one, two, and ≥three preoperative falls predicted postoperative falls at 30days (adjusted odds ratios [aOR] 2.3, 3.6, 5.5) and one year (aOR 2.3, 3.4, 6.9). One, two, and ≥three falls predicted functional decline at 30days (aOR 1.2, 2.4, 2.4) and one year (aOR 1.3, 1.5, 3.2), along with in-hospital complications (aOR 1.2, 1.3, 2.0). Fall history predicted adverse outcomes better than commonly-used metrics, but did not predict quality of life deterioration or readmission. Falls are common after surgery, and preoperative falls herald postoperative falls and other adverse outcomes. A history of preoperative falls should be routinely ascertained. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Impaired executive function can predict recurrent falls in Parkinson's disease.

PubMed

Mak, Margaret K; Wong, Adrian; Pang, Marco Y

2014-12-01

To examine whether impairment in executive function independently predicts recurrent falls in people with Parkinson's disease (PD). Prospective cohort study. University motor control research laboratory. A convenience sample of community-dwelling people with PD (N=144) was recruited from a patient self-help group and movement disorders clinics. Not applicable. Executive function was assessed with the Mattis Dementia Rating Scale Initiation/Perseveration (MDRS-IP) subtest, and fear of falling (FoF) with the Activities-specific Balance Confidence (ABC) Scale. All participants were followed up for 12 months to record the number of monthly fall events. Forty-two people with PD had at least 2 falls during the follow-up period and were classified as recurrent fallers. After accounting for demographic variables and fall history (P=.001), multiple logistic regression analysis showed that the ABC scores (P=.014) and MDRS-IP scores (P=.006) were significantly associated with future recurrent falls among people with PD. The overall accuracy of the prediction was 85.9%. With the use of the significant predictors identified in multiple logistic regression analysis, a prediction model determined by the logistic function was generated: Z = 1.544 + .378 (fall history) - .045 (ABC) - .145 (MDRS-IP). Impaired executive function is a significant predictor of future recurrent falls in people with PD. Participants with executive dysfunction and greater FoF at baseline had a significantly greater risk of sustaining a recurrent fall within the subsequent 12 months. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Robust prediction of individual creative ability from brain functional connectivity.

PubMed

Beaty, Roger E; Kenett, Yoed N; Christensen, Alexander P; Rosenberg, Monica D; Benedek, Mathias; Chen, Qunlin; Fink, Andreas; Qiu, Jiang; Kwapil, Thomas R; Kane, Michael J; Silvia, Paul J

2018-01-30

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Cardiac structure and function predicts functional decline in the oldest old.

PubMed

Leibowitz, David; Jacobs, Jeremy M; Lande-Stessman, Irit; Gilon, Dan; Stessman, Jochanan

2018-02-01

Background This study examined the association between cardiac structure and function and the deterioration in activities of daily living (ADLs) in an age-homogenous, community-dwelling population of patients born in 1920-1921 over a five-year follow-up period. Design Longitudinal cohort study. Methods Patients were recruited from the Jerusalem Longitudinal Cohort Study, which has followed an age-homogenous cohort of Jerusalem residents born in 1920-1921. Patients underwent home echocardiography and were followed up for five years. Dependence was defined as needing assistance with one or more basic ADL. Standard echocardiographic assessment of cardiac structure and function, including systolic and diastolic function, was performed. Reassessment of ADLs was performed at the five-year follow-up. Results A total of 459 patients were included in the study. Of these, 362 (79%) showed a deterioration in at least one ADL at follow-up. Patients with functional deterioration had a significantly higher left ventricular mass index and left atrial volume with a lower ejection fraction. There was no significant difference between the diastolic parameters the groups in examined. When the data were examined categorically, a significantly larger percentage of patients with functional decline had an abnormal left ventricular ejection fraction and left ventricular hypertrophy. The association between left ventricular mass index and functional decline remained significant in all multivariate models. Conclusions In this cohort of the oldest old, an elevated left ventricular mass index, higher left atrial volumes and systolic, but not diastolic dysfunction, were predictive of functional disability.

Does sociability predict civic involvement and political participation?

PubMed

Foschi, Renato; Lauriola, Marco

2014-02-01

In contemporary history as well as in political science, a strong associational life known as sociability is thought to explain the roots of modern democracy by establishing a link between the increasing availability of free time to the middle classes, increasing willingness to gather with others in circles or associations, and increasing social capital. In personality psychology, sociability is related to prosocial behavior (i.e., the need for affiliation, agreeableness, openness, and extraversion), whose importance in different political behaviors is increasingly recognized. In the present article, we carried out 5 studies (N = 1,429) that showed that political and associative sociability (a) can be reliably assessed, can have cross-cultural validity, and are properly associated with general social interest measures and personality domains and facets in the five-factor model; (b) do not overlap with similar concepts used in political psychology to account for political participation (political expertise, political interest, political self-efficacy); and (c) predicted political and nonpolitical group membership as well as observable choices in decision-making tasks with political and nonpolitical outcomes. The results are discussed, taking into consideration the extent to which specific facets of sociability can mediate between general personality traits and measures of civic involvement and political participation in a holistic model of political behavior. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Executive functions predict conceptual learning of science.

PubMed

Rhodes, Sinéad M; Booth, Josephine N; Palmer, Lorna Elise; Blythe, Richard A; Delibegovic, Mirela; Wheate, Nial J

2016-06-01

We examined the relationship between executive functions and both factual and conceptual learning of science, specifically chemistry, in early adolescence. Sixty-three pupils in their second year of secondary school (aged 12-13 years) participated. Pupils completed tasks of working memory (Spatial Working Memory), inhibition (Stop-Signal), attention set-shifting (ID/ED), and planning (Stockings of Cambridge), from the CANTAB. They also participated in a chemistry teaching session, practical, and assessment on the topic of acids and alkalis designed specifically for this study. Executive function data were related to (1) the chemistry assessment which included aspects of factual and conceptual learning and (2) a recent school science exam. Correlational analyses between executive functions and both the chemistry assessment and science grades revealed that science achievements were significantly correlated with working memory. Linear regression analysis revealed that visuospatial working memory ability was predictive of chemistry performance. Interestingly, this relationship was observed solely in relation to the conceptual learning condition of the assessment highlighting the role of executive functions in understanding and applying knowledge about what is learned within science teaching. © 2016 The British Psychological Society.

Curved Displacement Transfer Functions for Geometric Nonlinear Large Deformation Structure Shape Predictions

NASA Technical Reports Server (NTRS)

Ko, William L.; Fleischer, Van Tran; Lung, Shun-Fat

2017-01-01

For shape predictions of structures under large geometrically nonlinear deformations, Curved Displacement Transfer Functions were formulated based on a curved displacement, traced by a material point from the undeformed position to deformed position. The embedded beam (depth-wise cross section of a structure along a surface strain-sensing line) was discretized into multiple small domains, with domain junctures matching the strain-sensing stations. Thus, the surface strain distribution could be described with a piecewise linear or a piecewise nonlinear function. The discretization approach enabled piecewise integrations of the embedded-beam curvature equations to yield the Curved Displacement Transfer Functions, expressed in terms of embedded beam geometrical parameters and surface strains. By entering the surface strain data into the Displacement Transfer Functions, deflections along each embedded beam can be calculated at multiple points for mapping the overall structural deformed shapes. Finite-element linear and nonlinear analyses of a tapered cantilever tubular beam were performed to generate linear and nonlinear surface strains and the associated deflections to be used for validation. The shape prediction accuracies were then determined by comparing the theoretical deflections with the finiteelement- generated deflections. The results show that the newly developed Curved Displacement Transfer Functions are very accurate for shape predictions of structures under large geometrically nonlinear deformations.

Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.

PubMed

Funk, Christopher S; Kahanda, Indika; Ben-Hur, Asa; Verspoor, Karin M

2015-01-01

Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a "medium-throughput" pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.

Computational predictions of energy materials using density functional theory

NASA Astrophysics Data System (ADS)

Jain, Anubhav; Shin, Yongwoo; Persson, Kristin A.

2016-01-01

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery.

Relationship between global structural parameters and Enzyme Commission hierarchy: implications for function prediction.

PubMed

Boareto, Marcelo; Yamagishi, Michel E B; Caticha, Nestor; Leite, Vitor B P

2012-10-01

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

PubMed Central

Huynen, Martijn; Snel, Berend; Lathe, Warren; Bork, Peer

2000-01-01

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes. PMID:10958638

Confronting species distribution model predictions with species functional traits.

PubMed

Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

Phagonaute: A web-based interface for phage synteny browsing and protein function prediction.

PubMed

Delattre, Hadrien; Souiai, Oussema; Fagoonee, Khema; Guerois, Raphaël; Petit, Marie-Agnès

2016-09-01

Distant homology search tools are of great help to predict viral protein functions. However, due to the lack of profile databases dedicated to viruses, they can lack sensitivity. We constructed HMM profiles for more than 80,000 proteins from both phages and archaeal viruses, and performed all pairwise comparisons with HHsearch program. The whole resulting database can be explored through a user-friendly "Phagonaute" interface to help predict functions. Results are displayed together with their genetic context, to strengthen inferences based on remote homology. Beyond function prediction, this tool permits detections of co-occurrences, often indicative of proteins completing a task together, and observation of conserved patterns across large evolutionary distances. As a test, Herpes simplex virus I was added to Phagonaute, and 25% of its proteome matched to bacterial or archaeal viral protein counterparts. Phagonaute should therefore help virologists in their quest for protein functions and evolutionary relationships. Copyright © 2016 Elsevier Inc. All rights reserved.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

An assessment of catalytic residue 3D ensembles for the prediction of enzyme function.

PubMed

Žváček, Clemens; Friedrichs, Gerald; Heizinger, Leonhard; Merkl, Rainer

2015-11-04

The central element of each enzyme is the catalytic site, which commonly catalyzes a single biochemical reaction with high specificity. It was unclear to us how often sites that catalyze the same or highly similar reactions evolved on different, i. e. non-homologous protein folds and how similar their 3D poses are. Both similarities are key criteria for assessing the usability of pose comparison for function prediction. We have analyzed the SCOP database on the superfamily level in order to estimate the number of non-homologous enzymes possessing the same function according to their EC number. 89% of the 873 substrate-specific functions (four digit EC number) assigned to mono-functional, single-domain enzymes were only found in one superfamily. For a reaction-specific grouping (three digit EC number), this value dropped to 35%, indicating that in approximately 65% of all enzymes the same function evolved in two or more non-homologous proteins. For these isofunctional enzymes, structural similarity of the catalytic sites may help to predict function, because neither high sequence similarity nor identical folds are required for a comparison. To assess the specificity of catalytic 3D poses, we compiled the redundancy-free set ENZ_SITES, which comprises 695 sites, whose composition and function are well-defined. We compared their poses with the help of the program Superpose3D and determined classification performance. If the sites were from different superfamilies, the number of true and false positive predictions was similarly high, both for a coarse and a detailed grouping of enzyme function. Moreover, classification performance did not improve drastically, if we additionally used homologous sites to predict function. For a large number of enzymatic functions, dissimilar sites evolved that catalyze the same reaction and it is the individual substrate that determines the arrangement of the catalytic site and its local environment. These substrate-specific requirements

Modified Displacement Transfer Functions for Deformed Shape Predictions of Slender Curved Structures with Varying Curvatives

NASA Technical Reports Server (NTRS)

Ko, William L.; Fleischer, Van Tran

2014-01-01

To eliminate the need to use finite-element modeling for structure shape predictions, a new method was invented. This method is to use the Displacement Transfer Functions to transform the measured surface strains into deflections for mapping out overall structural deformed shapes. The Displacement Transfer Functions are expressed in terms of rectilinearly distributed surface strains, and contain no material properties. This report is to apply the patented method to the shape predictions of non-symmetrically loaded slender curved structures with different curvatures up to a full circle. Because the measured surface strains are not available, finite-element analysis had to be used to analytically generate the surface strains. Previously formulated straight-beam Displacement Transfer Functions were modified by introducing the curvature-effect correction terms. Through single-point or dual-point collocations with finite-elementgenerated deflection curves, functional forms of the curvature-effect correction terms were empirically established. The resulting modified Displacement Transfer Functions can then provide quite accurate shape predictions. Also, the uniform straight-beam Displacement Transfer Function was applied to the shape predictions of a section-cut of a generic capsule (GC) outer curved sandwich wall. The resulting GC shape predictions are quite accurate in partial regions where the radius of curvature does not change sharply.

The Predictive Role of Maternal Parenting and Stress on Pupils' Bullying involvement.

PubMed

Alizadeh Maralani, Fatemeh; Mirnasab, Mirmahmoud; Hashemi, Touraj

2016-10-01

The link between inappropriate parenting style and both bullying and victimization is well documented. However, it is not clear as to which kind of parenting style is associated with victimization. Furthermore, no studies have yet been conducted regarding the role of parental stress in bullying and victimization. This study aimed to examine the role of parenting styles and maternal stress in pupils' bullying and victimization. A total of 300 primary school pupils, enrolled in fourth and fifth grades, participated in the study. Initially, 100 noninvolved pupils were randomly selected using a multistage cluster sampling method. Then using a screening method, 100 bully pupils and 100 victimized peers were selected. Olweus Bullying Scale and teacher nomination were administered for screening these pupils. Baumrind Parenting Style Questionnaire and revised version of Abidin Parental Stress Index (short form) were also applied to all pupils in the study. Data were analyzed using discriminant function analysis. The findings showed that (a) with regard to parenting styles, significant differences were found among groups. Authoritarian parenting style could significantly predict pupils' bullying behavior, whereas victimization was predictable in families with permissive parenting style. In addition, noninvolved pupils were predicted to have authoritative parenting style. (b) Considering maternal stress, significant differences were observed across groups. Parents of bullies and victims were predicted to have higher maternal stress than noninvolved pupils. The implications of the study in relation to the role of mothers in bullying and victimization are discussed.

Oil palm phenolics confer neuroprotective effects involving cognitive and motor functions in mice

PubMed Central

Leow, Soon-Sen; Sekaran, Shamala Devi; Tan, YewAi; Sundram, Kalyana; Sambanthamurthi, Ravigadevi

2013-01-01

Objectives Phenolics are important phytochemicals which have positive effects on chronic diseases, including neurodegenerative ailments. The oil palm (Elaeis guineensis) is a rich source of water-soluble phenolics. This study was carried out to discover the effects of administering oil palm phenolics (OPP) to mice, with the aim of identifying whether these compounds possess significant neuroprotective properties. Methods OPP was given to BALB/c mice on a normal diet as fluids for 6 weeks while the controls were given distilled water. These animals were tested in a water maze and on a rotarod weekly to assess the effects of OPP on cognitive and motor functions, respectively. Using Illumina microarrays, we further explored the brain gene expression changes caused by OPP in order to determine the molecular mechanisms involved. Real-time quantitative reverse transcription-polymerase chain reaction experiments were then carried out to validate the microarray data. Results We found that mice given OPP showed better cognitive function and spatial learning when tested in a water maze, and their performance also improved when tested on a rotarod, possibly due to better motor function and balance. Microarray gene expression analysis showed that these compounds up-regulated genes involved in brain development and activity, such as those under the regulation of the brain-derived neurotrophic factor. OPP also down-regulated genes involved in inflammation. Discussion These results suggest that the improvement of mouse cognitive and motor functions by OPP is caused by the neuroprotective and anti-inflammatory effects of the extract. PMID:23433062

Iowa Gambling Task Performance and Executive Function Predict Low-income Urban Preadolescents’ Risky Behaviors

PubMed Central

Ursache, Alexandra; Raver, C. Cybele

2015-01-01

This study examines preadolescents’ reports of risk-taking as predicted by two different, but related inhibitory control systems involving sensitivity to reward and loss on the one hand, and higher order processing in the context of cognitive conflict, known as executive functioning (EF), on the other. Importantly, this study examines these processes with a sample of inner-city, low-income preadolescents and as such examines the ways in which these processes may be related to risky behaviors as a function of children's levels of both concurrent and chronic exposure to household poverty. As part of a larger longitudinal study, 382 children (ages 9 -11) provided a self-report of risky behaviors and participated in the Iowa Gambling task, assessing bias for infrequent loss (preference for infrequent, high magnitude versus frequent, low magnitude loss) and the Hearts and Flowers task assessing executive functioning. Results demonstrated that a higher bias for infrequent loss was associated with higher risky behaviors for children who demonstrated lower EF. Furthermore, bias for infrequent loss was most strongly associated with higher risk-taking for children facing highest levels of poverty. Implications for early identification and prevention of risk-taking in inner-city preadolescents are discussed. PMID:26412918

Early post-stroke cognition in stroke rehabilitation patients predicts functional outcome at 13 months.

PubMed

Wagle, Jørgen; Farner, Lasse; Flekkøy, Kjell; Bruun Wyller, Torgeir; Sandvik, Leiv; Fure, Brynjar; Stensrød, Brynhild; Engedal, Knut

2011-01-01

To identify prognostic factors associated with functional outcome at 13 months in a sample of stroke rehabilitation patients. Specifically, we hypothesized that cognitive functioning early after stroke would predict long-term functional outcome independently of other factors. 163 stroke rehabilitation patients underwent a structured neuropsychological examination 2-3 weeks after hospital admittance, and their functional status was subsequently evaluated 13 months later with the modified Rankin Scale (mRS) as outcome measure. Three predictive models were built using linear regression analyses: a biological model (sociodemographics, apolipoprotein E genotype, prestroke vascular factors, lesion characteristics and neurological stroke-related impairment); a functional model (pre- and early post-stroke cognitive functioning, personal and instrumental activities of daily living, ADL, and depressive symptoms), and a combined model (including significant variables, with p value <0.05, from the biological and functional models). A combined model of 4 variables best predicted long-term functional outcome with explained variance of 49%: neurological impairment (National Institute of Health Stroke Scale; β = 0.402, p < 0.001), age (β = 0.233, p = 0.001), post-stroke cognitive functioning (Repeatable Battery of Neuropsychological Status, RBANS; β = -0.248, p = 0.001) and prestroke personal ADL (Barthel Index; β = -0.217, p = 0.002). Further linear regression analyses of which RBANS indexes and subtests best predicted long-term functional outcome showed that Coding (β = -0.484, p < 0.001) and Figure Copy (β = -0.233, p = 0.002) raw scores at baseline explained 42% of the variance in mRS scores at follow-up. Early post-stroke cognitive functioning as measured by the RBANS is a significant and independent predictor of long-term functional post-stroke outcome. Copyright © 2011 S. Karger AG, Basel.

WE-AB-202-02: Incorporating Regional Ventilation Function in Predicting Radiation Fibrosis After Concurrent Chemoradiotherapy for Lung Cancer

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

Lan, F; Jeudy, J; Tseng, H

Purpose: To investigate the incorporation of pre-therapy regional ventilation function in predicting radiation fibrosis (RF) in stage III non-small-cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: 37 stage III NSCLC patients were retrospectively studied. Patients received one cycle of cisplatin-gemcitabine, followed by two to three cycles of cisplatin-etoposide concurrently with involved-field thoracic radiotherapy between 46 and 66 Gy (2 Gy per fraction). Pre-therapy regional ventilation images of the lung were derived from 4DCT via a density-change-based image registration algorithm with mass correction. RF was evaluated at 6-months post-treatment using radiographic scoring based on airway dilation and volumemore » loss. Three types of ipsilateral lung metrics were studied: (1) conventional dose-volume metrics (V20, V30, V40, and mean-lung-dose (MLD)), (2) dose-function metrics (fV20, fV30, fV40, and functional mean-lung-dose (fMLD) generated by combining regional ventilation and dose), and (3) dose-subvolume metrics (sV20, sV30, sV40, and subvolume mean-lung-dose (sMLD) defined as the dose-volume metrics computed on the sub-volume of the lung with at least 60% of the quantified maximum ventilation status). Receiver operating characteristic (ROC) curve analysis and logistic regression analysis were used to evaluate the predictability of these metrics for RF. Results: In predicting airway dilation, the area under the ROC curve (AUC) values for (V20, MLD), (fV20, fMLD), and (sV20, and sMLD) were (0.76, 0.70), (0.80, 0.74) and (0.82, 0.80), respectively. The logistic regression p-values were (0.09, 0.18), (0.02, 0.05) and (0.004, 0.006), respectively. With regard to volume loss, the corresponding AUC values for these metrics were (0.66, 0.57), (0.67, 0.61) and (0.71, 0.69), and p-values were (0.95, 0.90), (0.43, 0.64) and (0.08, 0.12), respectively. Conclusion: The inclusion of regional ventilation function improved

The Density Functional Theory of Flies: Predicting distributions of interacting active organisms

NASA Astrophysics Data System (ADS)

Kinkhabwala, Yunus; Valderrama, Juan; Cohen, Itai; Arias, Tomas

On October 2nd, 2016, 52 people were crushed in a stampede when a crowd panicked at a religious gathering in Ethiopia. The ability to predict the state of a crowd and whether it is susceptible to such transitions could help prevent such catastrophes. While current techniques such as agent based models can predict transitions in emergent behaviors of crowds, the assumptions used to describe the agents are often ad hoc and the simulations are computationally expensive making their application to real-time crowd prediction challenging. Here, we pursue an orthogonal approach and ask whether a reduced set of variables, such as the local densities, are sufficient to describe the state of a crowd. Inspired by the theoretical framework of Density Functional Theory, we have developed a system that uses only measurements of local densities to extract two independent crowd behavior functions: (1) preferences for locations and (2) interactions between individuals. With these two functions, we have accurately predicted how a model system of walking Drosophila melanogaster distributes itself in an arbitrary 2D environment. In addition, this density-based approach measures properties of the crowd from only observations of the crowd itself without any knowledge of the detailed interactions and thus it can make predictions about the resulting distributions of these flies in arbitrary environments, in real-time. This research was supported in part by ARO W911NF-16-1-0433.

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

PubMed Central

Widder, Stefanie; Allen, Rosalind J; Pfeiffer, Thomas; Curtis, Thomas P; Wiuf, Carsten; Sloan, William T; Cordero, Otto X; Brown, Sam P; Momeni, Babak; Shou, Wenying; Kettle, Helen; Flint, Harry J; Haas, Andreas F; Laroche, Béatrice; Kreft, Jan-Ulrich; Rainey, Paul B; Freilich, Shiri; Schuster, Stefan; Milferstedt, Kim; van der Meer, Jan R; Groβkopf, Tobias; Huisman, Jef; Free, Andrew; Picioreanu, Cristian; Quince, Christopher; Klapper, Isaac; Labarthe, Simon; Smets, Barth F; Wang, Harris; Soyer, Orkun S

2016-01-01

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved. PMID:27022995

Physical Function Does Not Predict Care Assessment Need Score in Older Veterans.

PubMed

Serra, Monica C; Addison, Odessa; Giffuni, Jamie; Paden, Lydia; Morey, Miriam C; Katzel, Leslie

2017-01-01

The Veterans Health Administration's Care Assessment Need (CAN) score is a statistical model, aimed to predict high-risk patients. We were interested in determining if a relationship existed between physical function and CAN scores. Seventy-four older (71 ± 1 years) male Veterans underwent assessment of CAN score and subjective (Short Form-36 [SF-36]) and objective (self-selected walking speed, four square step test, short physical performance battery) assessment of physical function. Approximately 25% of participants self-reported limitations performing lower intensity activities, while 70% to 90% reported limitations with more strenuous activities. When compared with cut points indicative of functional limitations, 35% to 65% of participants had limitations for each of the objective measures. Any measure of subjective or objective physical function did not predict CAN score. These data indicate that the addition of a physical function assessment may complement the CAN score in the identification of high-risk patients.

Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

PubMed Central

Nagao, Chioko; Nagano, Nozomi; Mizuguchi, Kenji

2014-01-01

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily. PMID:24416252

Circadian misalignment, reward-related brain function, and adolescent alcohol involvement.

PubMed

Hasler, Brant P; Clark, Duncan B

2013-04-01

Developmental changes in sleep and circadian rhythms that occur during adolescence may contribute to reward-related brain dysfunction, and consequently increase the risk of alcohol use disorders (AUDs). This review (i) describes marked changes in circadian rhythms, reward-related behavior and brain function, and alcohol involvement that occur during adolescence, (ii) offers evidence that these parallel developmental changes are associated, and (iii) posits a conceptual model by which misalignment between sleep-wake timing and endogenous circadian timing may increase the risk of adolescent AUDs by altering reward-related brain function. The timing of sleep shifts later throughout adolescence, in part due to developmental changes in endogenous circadian rhythms, which tend to become more delayed. This tendency for delayed sleep and circadian rhythms is at odds with early school start times during secondary education, leading to misalignment between many adolescents' sleep-wake schedules and their internal circadian timing. Circadian misalignment is associated with increased alcohol use and other risk-taking behaviors, as well as sleep loss and sleep disturbance. Growing evidence indicates that circadian rhythms modulate the reward system, suggesting that circadian misalignment may impact adolescent alcohol involvement by altering reward-related brain function. Neurocognitive function is also subject to sleep and circadian influence, and thus circadian misalignment may also impair inhibitory control and other cognitive processes relevant to alcohol use. Specifically, circadian misalignment may further exacerbate the cortical-subcortical imbalance within the reward circuit, an imbalance thought to explain increased risk-taking and sensation-seeking during adolescence. Adolescent alcohol use is highly contextualized, however, and thus studies testing this model will also need to consider factors that may influence both circadian misalignment and alcohol use. This review

Can patients' preferences for involvement in decision-making regarding the use of medicines be predicted?

PubMed

Garfield, S; Smith, F; Francis, S A; Chalmers, C

2007-06-01

The current study aimed to develop a model of patients' preferences for involvement in decision-making concerning the use of medicines for chronic conditions in the UK and test it in a large representative sample of patients with one of two clinical conditions. Following a structured literature review, an instrument was developed which measured the variables that had been identified as predictors of patients' preferences for involvement in decision making in previous research. Five hundred and sixteen patients with rheumatoid arthritis or type 2 diabetes were recruited from outpatient and primary care clinics and asked to complete the instrument. Multivariate analysis revealed that age, social class and clinical condition were associated with preferences for involvement in decision-making concerning the use of medicines for chronic illness but gender, ethnic group, concerns about medicines, beliefs about necessity of medicines, health status, quality of life and time since diagnosis were not. In total, the fitted model explained only 14% of the variance. This study has demonstrated that current research does not provide a basis for predicting patients' preferences for involvement in decision-making. Building concordant relationships may depend on practitioners developing strategies to establish individuals' preferences for involvement in decision-making as part of the ongoing prescriber-patient relationship.

Diuretic renography in hydronephrosis: renal tissue tracer transit predicts functional course and thereby need for surgery.

PubMed

Schlotmann, Andreas; Clorius, John H; Clorius, Sandra N

2009-10-01

The recognition of those hydronephrotic kidneys which require therapy to preserve renal function remains difficult. We retrospectively compared the 'tissue tracer transit' (TTT) of (99m)Tc-mercaptoacetyltriglycine ((99m)Tc-MAG(3)) with 'response to furosemide stimulation' (RFS) and with 'single kidney function < 40%' (SKF < 40%) to predict functional course and thereby need for surgery. Fifty patients with suspected unilateral obstruction and normal contralateral kidney had 115 paired (baseline/follow-up) (99m)Tc-MAG(3) scintirenographies. Three predictions of the functional development were derived from each baseline examination: the first based on TTT (visually assessed), the second on RFS and the third on SKF < 40%. Each prediction also considered whether the patient had surgery. Possible predictions were 'better', 'worse' or 'stable' function. A comparison of SKF at baseline and follow-up verified the predictions. The frequency of correct predictions for functional improvement following surgery was 8 of 10 kidneys with delayed TTT, 9 of 22 kidneys with obstructive RFS and 9 of 21 kidneys with SKF < 40%; for functional deterioration without surgery it was 2 of 3 kidneys with delayed TTT, 3 of 20 kidneys with obstructive RFS and 3 of 23 kidneys with SKF < 40%. Without surgery 67 of 70 kidneys with timely TTT maintained function. Without surgery 0 of 9 kidneys with timely TTT but obstructive RFS and only 1 of 16 kidneys with timely TTT but SKF < 40% lost function. Delayed TTT appears to identify the need for therapy to preserve function of hydronephrotic kidneys, while timely TTT may exclude risk even in the presence of an obstructive RFS or SKF < 40%.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

PubMed Central

2013-01-01

Background Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs. Results A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and

Skill versus luck: A motivational analysis of gambling involvement.

PubMed

Chantal, Y; Vallerand, R J

1996-12-01

The purpose of the present investigation was to test the skill/luck distinction among gambling games by comparing the motivations underlying participation in a skill (horse racing) and a luck (lottery) betting activity. Predictions were made using Self-Determination Theory (Deci & Ryan, 1985, 1991). It was predicted that self-determined motivations (intrinsic motivation and identified regulation) would be more prominent for the skill game because it is conducive to optimal challenges, fun, and self-involvement. Conversely, the non self-determined forms of motivation (especially external regulation) should be more important for the game of luck because the luck dimension precludes true involvement of the self and orients the individual towards material gains. Results from a hierarchical discriminant function analysis, with 120 gamblers predominantly involved in one of the two betting activities, supported these hypotheses. These results highlight the relevance of a motivational analysis for a better understanding of the inherent properties of gambling games.

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

PubMed

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Prediction of EST functional relationships via literature mining with user-specified parameters.

PubMed

Wang, Hei-Chia; Huang, Tian-Hsiang

2009-04-01

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Identification of optimal soil hydraulic functions and parameters for predicting soil moisture

EPA Science Inventory

We examined the accuracy of several commonly used soil hydraulic functions and associated parameters for predicting observed soil moisture data. We used six combined methods formed by three commonly used soil hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...

Predicting functional outcomes among college drinkers: reliability and predictive validity of the Young Adult Alcohol Consequences Questionnaire.

PubMed

Read, Jennifer P; Merrill, Jennifer E; Kahler, Christopher W; Strong, David R

2007-11-01

Heavy drinking and associated consequences are widespread among U.S. college students. Recently, Read et al. (Read, J. P., Kahler, C. W., Strong, D., & Colder, C. R. (2006). Development and preliminary validation of the Young Adult Alcohol Consequences Questionnaire. Journal of Studies on Alcohol, 67, 169-178) developed the Young Adult Alcohol Consequences Questionnaire (YAACQ) to assess the broad range of consequences that may result from heavy drinking in the college milieu. In the present study, we sought to add to the psychometric validation of this measure by employing a prospective design to examine the test-retest reliability, concurrent validity, and predictive validity of the YAACQ. We also sought to examine the utility of the YAACQ administered early in the semester in the prediction of functional outcomes later in the semester, including the persistence of heavy drinking, and academic functioning. Ninety-two college students (48 females) completed a self-report assessment battery during the first weeks of the Fall semester, and approximately one week later. Additionally, 64 subjects (37 females) participated at an optional third time point at the end of the semester. Overall, the YAACQ demonstrated strong internal consistency, test-retest reliability, and concurrent and predictive validity. YAACQ scores also were predictive of both drinking frequency, and "binge" drinking frequency. YAACQ total scores at baseline were an early indicator of academic performance later in the semester, with greater number of total consequences experienced being negatively associated with end-of-semester grade point average. Specific YAACQ subscale scores (Impaired Control, Dependence Symptoms, Blackout Drinking) showed unique prediction of persistent drinking and academic outcomes.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

PubMed

Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-03-19

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

PubMed Central

Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.

2000-01-01

A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (Like Sm) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative genomic two-hybrid screens, starting with the Lsm proteins as baits. Indeed, extensive interactions amongst eight Lsm proteins were found that suggest the existence of a Lsm complex or complexes. These Lsm interactions apparently involve the conserved Sm domain that also mediates interactions between the Sm proteins. The screens also reveal functionally significant interactions with splicing factors, in particular with Prp4 and Prp24, compatible with genetic studies and with the reported association of Lsm proteins with spliceosomal U6 and U4/U6 particles. In addition, interactions with proteins involved in mRNA turnover, such as Mrt1, Dcp1, Dcp2 and Xrn1, point to roles for Lsm complexes in distinct RNA metabolic processes, that are confirmed in independent functional studies. These results provide compelling evidence that two-hybrid screens yield functionally meaningful information about protein–protein interactions and can suggest functions for uncharacterized proteins, especially when they are performed on a genome-wide scale. PMID:10900456

Network-based function prediction and interactomics: the case for metabolic enzymes.

PubMed

Janga, S C; Díaz-Mejía, J Javier; Moreno-Hagelsieb, G

2011-01-01

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases. © 2010 Elsevier Inc. All rights reserved.

Prediction of CpG-island function: CpG clustering vs. sliding-window methods

PubMed Central

2010-01-01

Background Unmethylated stretches of CpG dinucleotides (CpG islands) are an outstanding property of mammal genomes. Conventionally, these regions are detected by sliding window approaches using %G + C, CpG observed/expected ratio and length thresholds as main parameters. Recently, clustering methods directly detect clusters of CpG dinucleotides as a statistical property of the genome sequence. Results We compare sliding-window to clustering (i.e. CpGcluster) predictions by applying new ways to detect putative functionality of CpG islands. Analyzing the co-localization with several genomic regions as a function of window size vs. statistical significance (p-value), CpGcluster shows a higher overlap with promoter regions and highly conserved elements, at the same time showing less overlap with Alu retrotransposons. The major difference in the prediction was found for short islands (CpG islets), often exclusively predicted by CpGcluster. Many of these islets seem to be functional, as they are unmethylated, highly conserved and/or located within the promoter region. Finally, we show that window-based islands can spuriously overlap several, differentially regulated promoters as well as different methylation domains, which might indicate a wrong merge of several CpG islands into a single, very long island. The shorter CpGcluster islands seem to be much more specific when concerning the overlap with alternative transcription start sites or the detection of homogenous methylation domains. Conclusions The main difference between sliding-window approaches and clustering methods is the length of the predicted islands. Short islands, often differentially methylated, are almost exclusively predicted by CpGcluster. This suggests that CpGcluster may be the algorithm of choice to explore the function of these short, but putatively functional CpG islands. PMID:20500903

Prediction of functional loss in glaucoma from progressive optic disc damage.

PubMed

Medeiros, Felipe A; Alencar, Luciana M; Zangwill, Linda M; Bowd, Christopher; Sample, Pamela A; Weinreb, Robert N

2009-10-01

To evaluate the ability of progressive optic disc damage detected by assessment of longitudinal stereophotographs to predict future development of functional loss in those with suspected glaucoma. The study included 639 eyes of 407 patients with suspected glaucoma followed up for an average of 8.0 years with annual standard automated perimetry visual field and optic disc stereophotographs. All patients had normal and reliable standard automated perimetry results at baseline. Conversion to glaucoma was defined as development of 3 consecutive abnormal visual fields during follow-up. Presence of progressive optic disc damage was evaluated by grading longitudinally acquired simultaneous stereophotographs. Other predictive factors included age, intraocular pressure, central corneal thickness, pattern standard deviation, and baseline stereophotograph grading. Hazard ratios for predicting visual field loss were obtained by extended Cox models, with optic disc progression as a time-dependent covariate. Predictive accuracy was evaluated using a modified R(2) index. Progressive optic disc damage had a hazard ratio of 25.8 (95% confidence interval, 16.0-41.7) and was the most important risk factor for development of visual field loss with an R(2) of 79%. The R(2)s for other predictive factors ranged from 6% to 26%. Presence of progressive optic disc damage on stereophotographs was a highly predictive factor for future development of functional loss in glaucoma. These findings suggest the importance of careful monitoring of the optic disc appearance and a potential role for longitudinal assessment of the optic disc as an end point in clinical trials and as a reference for evaluation of diagnostic tests in glaucoma.

Less-structured time in children's daily lives predicts self-directed executive functioning.

PubMed

Barker, Jane E; Semenov, Andrei D; Michaelson, Laura; Provan, Lindsay S; Snyder, Hannah R; Munakata, Yuko

2014-01-01

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

Less-structured time in children's daily lives predicts self-directed executive functioning

PubMed Central

Barker, Jane E.; Semenov, Andrei D.; Michaelson, Laura; Provan, Lindsay S.; Snyder, Hannah R.; Munakata, Yuko

2014-01-01

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6–7 year-old children's daily, annual, and typical schedules. We categorized children's activities as “structured” or “less-structured” based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up

Predictive Factors in Undergraduates' Involvement in Campus Secret Cults in Public Universities in Edo State of Nigeria

ERIC Educational Resources Information Center

Azetta Arhedo, Philip; Aluede, Oyaziwo; Adomeh, Ilu O. C.

2011-01-01

This study examined the predictive factors in undergraduates' involvement in campus secret cults in public universities in Edo State of Nigeria. The study employed the descriptive method, specifically the survey format. A random sample of three hundred and eighty (380) undergraduates was drawn from the two public universities. Data were elicited…

Functional imaging of semantic memory predicts postoperative episodic memory functions in chronic temporal lobe epilepsy.

PubMed

Köylü, Bülent; Walser, Gerald; Ischebeck, Anja; Ortler, Martin; Benke, Thomas

2008-08-05

Medial temporal (MTL) structures have crucial functions in episodic (EM), but also in semantic memory (SM) processing. Preoperative functional magnetic resonance imaging (fMRI) activity within the MTL is increasingly used to predict post-surgical memory capacities. Based on the hypothesis that EM and SM memory functions are both hosted by the MTL the present study wanted to explore the relationship between SM related activations in the MTL as assessed before and the capacity of EM functions after surgery. Patients with chronic unilateral left (n=14) and right (n=12) temporal lobe epilepsy (TLE) performed a standard word list learning test pre- and postoperatively, and a fMRI procedure before the operation using a semantic decision task. SM processing caused significant bilateral MTL activations in both patient groups. While right TLE patients showed asymmetry of fMRI activation with more activation in the left MTL, left TLE patients had almost equal activation in both MTL regions. Contrasting left TLE versus right TLE patients revealed greater activity within the right MTL, whereas no significant difference was observed for the reverse contrast. Greater effect size in the MTL region ipsilateral to the seizure focus was significantly and positively correlated with preoperative EM abilities. Greater effect size in the contralateral MTL was correlated with better postoperative verbal EM, especially in left TLE patients. These results suggest that functional imaging of SM tasks may be useful to predict postoperative verbal memory in TLE. They also advocate a common neuroanatomical basis for SM and EM processes in the MTL.

Effort, anhedonia, and function in schizophrenia: reduced effort allocation predicts amotivation and functional impairment.

PubMed

Barch, Deanna M; Treadway, Michael T; Schoen, Nathan

2014-05-01

One of the most debilitating aspects of schizophrenia is an apparent interest in or ability to exert effort for rewards. Such "negative symptoms" may prevent individuals from obtaining potentially beneficial outcomes in educational, occupational, or social domains. In animal models, dopamine abnormalities decrease willingness to work for rewards, implicating dopamine (DA) function as a candidate substrate for negative symptoms given that schizophrenia involves dysregulation of the dopamine system. We used the effort-expenditure for rewards task (EEfRT) to assess the degree to which individuals with schizophrenia were wiling to exert increased effort for either larger magnitude rewards or for rewards that were more probable. Fifty-nine individuals with schizophrenia and 39 demographically similar controls performed the EEfRT task, which involves making choices between "easy" and "hard" tasks to earn potential rewards. Individuals with schizophrenia showed less of an increase in effort allocation as either reward magnitude or probability increased. In controls, the frequency of choosing the hard task in high reward magnitude and probability conditions was negatively correlated with depression severity and anhedonia. In schizophrenia, fewer hard task choices were associated with more severe negative symptoms and worse community and work function as assessed by a caretaker. Consistent with patterns of disrupted dopamine functioning observed in animal models of schizophrenia, these results suggest that 1 mechanism contributing to impaired function and motivational drive in schizophrenia may be a reduced allocation of greater effort for higher magnitude or higher probability rewards.

Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

Predicting the function of an uncharacterized protein is a major challenge in post-genomic era due to problems complexity and scale. Having knowledge of protein function is a crucial link in the development of new drugs, better crops, and even the development of biochemicals such as biofuels. Recently numerous high-throughput experimental procedures have been invented to investigate the mechanisms leading to the accomplishment of a protein’s function and Phylogenetic profile is one of them. Phylogenetic profile is a way of representing a protein which encodes evolutionary history of proteins. In this paper we proposed a method for classification of phylogenetic profiles using supervised machine learning method, support vector machine classification along with radial basis function as kernel for identifying functionally linked proteins. We experimentally evaluated the performance of the classifier with the linear kernel, polynomial kernel and compared the results with the existing tree kernel. In our study we have used proteins of the budding yeast saccharomyces cerevisiae genome. We generated the phylogenetic profiles of 2465 yeast genes and for our study we used the functional annotations that are available in the MIPS database. Our experiments show that the performance of the radial basis kernel is similar to polynomial kernel is some functional classes together are better than linear, tree kernel and over all radial basis kernel outperformed the polynomial kernel, linear kernel and tree kernel. In analyzing these results we show that it will be feasible to make use of SVM classifier with radial basis function as kernel to predict the gene functionality using phylogenetic profiles.

Paternal involvement in pediatric Type 1 diabetes: fathers' and mothers' psychological functioning and disease management.

PubMed

Hansen, Jennifer A; Weissbrod, Carol; Schwartz, David D; Taylor, W Patrick

2012-03-01

Psychological functioning in fathers of children with Type 1 diabetes has received relatively little attention compared to mothers. This study examined fathers' perceived involvement in their children's diabetes care as it related to mothers' and fathers' pediatric parenting stress, depression, anxiety, marital satisfaction, and sleep, and to their children's diabetes regimen adherence and glycemic control. Eighty-two mothers and 43 fathers completed questionnaires. Multivariate linear regressions were conducted separately for mothers and fathers to determine the relationships between the perceived amount and the perceived helpfulness of father involvement in child diabetes care on parental psychosocial functioning and child diabetes control. Maternal perceptions of father helpfulness and amount of involvement in illness care were related to improved marital satisfaction and fewer depressive symptoms in mothers. In fathers, perception of their own amount of involvement was related to increased pediatric parenting stress and anxiety. Better child regimen adherence was associated with maternal perceptions of father helpfulness but not the amount of their involvement, while paternal perceptions of their own helpfulness were related to poorer glycemic control. These findings suggest that fathers and mothers may react differently to their roles in childhood illness and that perceptions of their involvement may be differently associated with children's glycemic control and regimen adherence.

Discovering the Deregulated Molecular Functions Involved in Malignant Transformation of Endometriosis to Endometriosis-Associated Ovarian Carcinoma Using a Data-Driven, Function-Based Analysis

PubMed Central

Chang, Chia-Ming; Yang, Yi-Ping; Chuang, Jen-Hua; Chuang, Chi-Mu; Lin, Tzu-Wei; Wang, Peng-Hui; Yu, Mu-Hsien

2017-01-01

The clinical characteristics of clear cell carcinoma (CCC) and endometrioid carcinoma EC) are concomitant with endometriosis (ES), which leads to the postulation of malignant transformation of ES to endometriosis-associated ovarian carcinoma (EAOC). Different deregulated functional areas were proposed accounting for the pathogenesis of EAOC transformation, and there is still a lack of a data-driven analysis with the accumulated experimental data in publicly-available databases to incorporate the deregulated functions involved in the malignant transformation of EOAC. We used the microarray gene expression datasets of ES, CCC and EC downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database. Then, we investigated the pathogenesis of EAOC by a data-driven, function-based analytic model with the quantified molecular functions defined by 1454 Gene Ontology (GO) term gene sets. This model converts the gene expression profiles to the functionome consisting of 1454 quantified GO functions, and then, the key functions involving the malignant transformation of EOAC can be extracted by a series of filters. Our results demonstrate that the deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response and cell-cell signaling play the key roles in the malignant transformation of EAOC. These results provide the evidence supporting the specific molecular pathways involved in the malignant transformation of EAOC. PMID:29113136

Developing a risk prediction model for the functional outcome after hip arthroscopy.

PubMed

Stephan, Patrick; Röling, Maarten A; Mathijssen, Nina M C; Hannink, Gerjon; Bloem, Rolf M

2018-04-19

Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors. Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule. The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17-0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21-1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20-1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99-4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22-0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38-4.18); p = < 0.01) were factors in the final prediction model of successful functional outcome 1 year after hip arthroscopy. The model's discriminating accuracy turned out to be fair, as 71% (95% CI: 64-80%) of the patients were classified correctly. The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.

Simple criteria to predict margin involvement after chemoradiotherapy and sphincter-sparing for low rectal cancer.

PubMed

Dumont, F; Tilly, C; Dartigues, P; Goéré, D; Honoré, C; Elias, D

2015-09-01

Low rectal cancers carry a high risk of circumferential margin involvement (CRM+). The anatomy of the lower part of the rectum and a long course of chemoradiotherapy (CRT) limit the accuracy of imaging to predict the CRM+. Additional criteria are required. Eighty six patients undergoing rectal resection with a sphincter-sparing procedure after CRT for low rectal cancer between 2000 and 2013 were retrospectively reviewed. Risk factors of CRM+ and the cut-off number of risk factors required to accurately predict the CRM+ were analyzed. The CRM+ rate was 9.3% and in the multivariate analysis, the significant risk factors were a tumor size exceeding 3 cm, poor response to CRT and a fixed tumor. The best cut-off to predict CRM+ was the presence of 2 risk factors. Patients with 0-1 and 2-3 risk factors had a CRM+ respectively in 1.3% and 50% of cases and a 3-year recurrence rate of 7% and 35% after a median follow-up of 50 months. Poor response, a residual tumor greater than 3 cm and a fixed tumor are predictive of CRM+. Sphincter sparing is an oncological safety procedure for patients with 0-1 criteria but not for patients with 2-3 criteria. Copyright © 2015 Elsevier Ltd. All rights reserved.

Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.

PubMed

Busk, P K; Pilgaard, B; Lezyk, M J; Meyer, A S; Lange, L

2017-04-12

Carbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy. We identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows. Hotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.

Functional identity and diversity of animals predict ecosystem functioning better than species-based indices

PubMed Central

Gagic, Vesna; Bartomeus, Ignasi; Jonsson, Tomas; Taylor, Astrid; Winqvist, Camilla; Fischer, Christina; Slade, Eleanor M.; Steffan-Dewenter, Ingolf; Emmerson, Mark; Potts, Simon G.; Tscharntke, Teja; Weisser, Wolfgang; Bommarco, Riccardo

2015-01-01

Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se. PMID:25567651

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Can Functional Movement Assessment Predict Football Head Impact Biomechanics?

PubMed

Ford, Julia M; Campbell, Kody R; Ford, Cassie B; Boyd, Kenneth E; Padua, Darin A; Mihalik, Jason P

2018-06-01

The purposes of this study was to determine functional movement assessments' ability to predict head impact biomechanics in college football players and to determine whether head impact biomechanics could explain preseason to postseason changes in functional movement performance. Participants (N = 44; mass, 109.0 ± 20.8 kg; age, 20.0 ± 1.3 yr) underwent two preseason and postseason functional movement assessment screenings: 1) Fusionetics Movement Efficiency Test and 2) Landing Error Scoring System (LESS). Fusionetics is scored 0 to 100, and participants were categorized into the following movement quality groups as previously published: good (≥75), moderate (50-75), and poor (<50). The LESS is scored 0 to 17, and participants were categorized into the following previously published movement quality groups: good (≤5 errors), moderate (6-7 errors), and poor (>7 errors). The Head Impact Telemetry (HIT) System measured head impact frequency and magnitude (linear acceleration and rotational acceleration). An encoder with six single-axis accelerometers was inserted between the padding of a commercially available Riddell football helmet. We used random intercepts general linear-mixed models to analyze our data. There were no effects of preseason movement assessment group on the two Head Impact Telemetry System impact outcomes: linear acceleration and rotational acceleration. Head impact frequency did not significantly predict preseason to postseason score changes obtained from the Fusionetics (F1,36 = 0.22, P = 0.643, R = 0.006) or the LESS (F1,36 < 0.01, P = 0.988, R < 0.001) assessments. Previous research has demonstrated an association between concussion and musculoskeletal injury, as well as functional movement assessment performance and musculoskeletal injury. The functional movement assessments chosen may not be sensitive enough to detect neurological and neuromuscular differences within the sample and subtle changes after sustaining head impacts.

Very Long Chain Fatty Acids Are Functionally Involved in Necroptosis.

PubMed

Parisi, Laura R; Li, Nasi; Atilla-Gokcumen, G Ekin

2017-12-21

Necroptosis is a form of regulated cell death that is linked to various human diseases. Distinct membrane-related, thus lipid-dependent, alterations take place during necroptosis. However, little is known about the roles of specific lipids in this process. We used an untargeted LC-MS-based approach to reveal that distinct lipid species are regulated at the molecular level during necroptosis. We found that ceramides and very long chain fatty acids accumulate during this process. Intrigued by the specificity of very long chain fatty acid accumulation, we focused on characterizing their involvement during necroptosis. Biochemical characterizations suggested that activated fatty acid biosynthesis and elongation could be responsible for these accumulations. We further showed that inhibition of fatty acid biosynthesis and depletion of very long chain fatty acids prevented loss of plasma membrane integrity and cell death, strongly suggesting that very long chain fatty acids are functionally involved in necroptosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Relationship between involvement and functional milk desserts intention to purchase. Influence on attitude towards packaging characteristics.

PubMed

Ares, Gastón; Besio, Mariángela; Giménez, Ana; Deliza, Rosires

2010-10-01

Consumers perceive functional foods as member of the particular food category to which they belong. In this context, apart from health and sensory characteristics, non-sensory factors such as packaging might have a key role on determining consumers' purchase decisions regarding functional foods. The aims of the present work were to study the influence of different package attributes on consumer willingness to purchase regular and functional chocolate milk desserts; and to assess if the influence of these attributes was affected by consumers' level of involvement with the product. A conjoint analysis task was carried out with 107 regular milk desserts consumers, who were asked to score their willingness to purchase of 16 milk dessert package concepts varying in five features of the package, and to complete a personal involvement inventory questionnaire. Consumers' level of involvement with the product affected their interest in the evaluated products and their reaction towards the considered conjoint variables, suggesting that it could be a useful segmentation tool during food development. Package colour and the presence of a picture on the label were the variables with the highest relative importance, regardless of consumers' involvement with the product. The importance of these variables was higher than the type of dessert indicating that packaging may play an important role in consumers' perception and purchase intention of functional foods.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

PubMed

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

FSPP: A Tool for Genome-Wide Prediction of smORF-Encoded Peptides and Their Functions

PubMed Central

Li, Hui; Xiao, Li; Zhang, Lili; Wu, Jiarui; Wei, Bin; Sun, Ninghui; Zhao, Yi

2018-01-01

smORFs are small open reading frames of less than 100 codons. Recent low throughput experiments showed a lot of smORF-encoded peptides (SEPs) played crucial rule in processes such as regulation of transcription or translation, transportation through membranes and the antimicrobial activity. In order to gather more functional SEPs, it is necessary to have access to genome-wide prediction tools to give profound directions for low throughput experiments. In this study, we put forward a functional smORF-encoded peptides predictor (FSPP) which tended to predict authentic SEPs and their functions in a high throughput method. FSPP used the overlap of detected SEPs from Ribo-seq and mass spectrometry as target objects. With the expression data on transcription and translation levels, FSPP built two co-expression networks. Combing co-location relations, FSPP constructed a compound network and then annotated SEPs with functions of adjacent nodes. Tested on 38 sequenced samples of 5 human cell lines, FSPP successfully predicted 856 out of 960 annotated proteins. Interestingly, FSPP also highlighted 568 functional SEPs from these samples. After comparison, the roles predicted by FSPP were consistent with known functions. These results suggest that FSPP is a reliable tool for the identification of functional small peptides. FSPP source code can be acquired at https://www.bioinfo.org/FSPP. PMID:29675032

Role of religious involvement and spirituality in functioning among African Americans with cancer: testing a mediational model

PubMed Central

Holt, Cheryl L.; Wang, Min Qi; Caplan, Lee; Schulz, Emily; Blake, Victor; Southward, Vivian L.

2013-01-01

The present study tested a mediational model of the role of religious involvement, spirituality, and physical/emotional functioning in a sample of African American men and women with cancer. Several mediators were proposed based on theory and previous research, including sense of meaning, positive and negative affect, and positive and negative religious coping. One hundred patients were recruited through oncologist offices, key community leaders and community organizations, and interviewed by telephone. Participants completed an established measure of religious involvement, the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-SP-12 version 4), the Positive and Negative Affect Schedule (PANAS), the Meaning in Life Scale, the Brief RCOPE, and the SF-12, which assesses physical and emotional functioning. Positive affect completely mediated the relationship between religious behaviors and emotional functioning. Though several other constructs showed relationships with study variables, evidence of mediation was not supported. Mediational models were not significant for the physical functioning outcome, nor were there significant main effects of religious involvement or spirituality for this outcome. Implications for cancer survivorship interventions are discussed. PMID:21222026

The Protein Interactome of Mycobacteriophage Giles Predicts Functions for Unknown Proteins.

PubMed

Mehla, Jitender; Dedrick, Rebekah M; Caufield, J Harry; Siefring, Rachel; Mair, Megan; Johnson, Allison; Hatfull, Graham F; Uetz, Peter

2015-08-01

Mycobacteriophages are viruses that infect mycobacterial hosts and are prevalent in the environment. Nearly 700 mycobacteriophage genomes have been completely sequenced, revealing considerable diversity and genetic novelty. Here, we have determined the protein complement of mycobacteriophage Giles by mass spectrometry and mapped its genome-wide protein interactome to help elucidate the roles of its 77 predicted proteins, 50% of which have no known function. About 22,000 individual yeast two-hybrid (Y2H) tests with four different Y2H vectors, followed by filtering and retest screens, resulted in 324 reproducible protein-protein interactions, including 171 (136 nonredundant) high-confidence interactions. The complete set of high-confidence interactions among Giles proteins reveals new mechanistic details and predicts functions for unknown proteins. The Giles interactome is the first for any mycobacteriophage and one of just five known phage interactomes so far. Our results will help in understanding mycobacteriophage biology and aid in development of new genetic and therapeutic tools to understand Mycobacterium tuberculosis. Mycobacterium tuberculosis causes over 9 million new cases of tuberculosis each year. Mycobacteriophages, viruses of mycobacterial hosts, hold considerable potential to understand phage diversity, evolution, and mycobacterial biology, aiding in the development of therapeutic tools to control mycobacterial infections. The mycobacteriophage Giles protein-protein interaction network allows us to predict functions for unknown proteins and shed light on major biological processes in phage biology. For example, Giles gp76, a protein of unknown function, is found to associate with phage packaging and maturation. The functions of mycobacteriophage-derived proteins may suggest novel therapeutic approaches for tuberculosis. Our ORFeome clone set of Giles proteins and the interactome data will be useful resources for phage interactomics. Copyright © 2015

Effect of ethylic alcohol on attentive functions involved in driving abilities.

PubMed

Bivona, Umberto; Garbarino, Sergio; Rigon, Jessica; Buzzi, Maria Gabriella; Onder, Graziano; Matteis, Maria; Catani, Sheila; Giustini, Marco; Mancardi, Giovanni Luigi; Formisano, Rita

2015-01-01

The burden of injuries due to drunk drivers has been estimated only indirectly. Indeed, alcohol is considered one of the most important contributing cause of car crash injuries and its effect on cognitive functions needs to be better elucidated. Aims of the study were i) to examine the effect of alcohol on attentive abilities involved while driving, and ii) to investigate whether Italian law limits for safe driving are sufficiently accurate to prevent risky behaviours and car crash risk while driving. We conducted a cross-over study at IRCCS Fondazione Santa Lucia Rehabilitation Hospital in Rome. Thirty-two healthy subjects were enrolled in this experiment. Participants were submitted to an attentive test battery assessing attention before taking Ethylic Alcohol (EA-) and after taking EA (EA+). In the EA+ condition subjects drank enough wine until the blood alcohol concentration, measured by means of Breath Analyzer, was equal to or higher than 0.5 g/l. Data analysis revealed that after alcohol assumption, tonic and phasic alertness, selective, divided attention and vigilance were significantly impaired when BAC level was at least 0.5 g/l. These data reveal that alcohol has a negative effect on attentive functions which are primarily involved in driving skills and that Italian law limits are adequate to prevent risky driving behaviour.

Similar patterns of neural activity predict memory function during encoding and retrieval.

PubMed

Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

2017-07-15

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

2013-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by

Predictive Design of Interfacial Functionality in Polymer Matrix Composites

DTIC Science & Technology

2017-05-24

structural design criteria. Due to the poor accessibility of interfaces by experimental means, little is known about the molecular definition, defect...is designed to allow for concurrent light scattering measurements, which establishes a unique experimental resource. We were able to leverage this...AFRL-AFOSR-VA-TR-2017-0103 Predictive Design of Interfacial Functionality in Polymer Matrix Composites John Kieffer UNIVERSITY OF MICHIGAN 503

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

PubMed Central

Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-01-01

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. PMID:28335492

Does subsequent criminal justice involvement predict foster care and termination of parental rights for children born to incarcerated women?

PubMed

Kubiak, Sheryl Pimlott; Kasiborski, Natalie; Karim, Nidal; Schmittel, Emily

2012-01-01

This longitudinal study of 83 incarcerated women, who gave birth during incarceration and retained their parental rights through brief sentences, examines the intersection between subsequent criminal justice involvement postrelease and child welfare outcomes. Ten years of multiple state-level administrative data sets are used to determine if arrest or conviction predict foster care and/or termination of parental rights. Findings indicate that only felony arrest is a significant predictor of foster care involvement. Additionally, 69% of mothers retained legal custody, despite subsequent criminal involvement for many, suggesting supportive parenting programs and resources need to be available to these women throughout and after incarceration.

Functional and proteomic analyses reveal that wxcB is involved in virulence, motility, detergent tolerance, and biofilm formation in Xanthomonas campestris pv. vesicatoria.

PubMed

Park, Hye-Jee; Jung, Ho Won; Han, Sang-Wook

2014-09-26

The bacterial envelope possesses diverse functions, including protection against environmental stress and virulence factors for host infection. Here, we report the function of wxcB in Xanthomonas campestris pv. vesicatoria (Xcv), a causal agent of bacterial leaf spot disease in tomato and pepper. To characterize roles of wxcB, we generated a knockout mutant (XcvΔwxcB) and found that the virulence of the mutant was weaker than that of the wild type in tomato plants. To predict the mechanism affected by wxcB, we compared protein expressions between the wild type and the mutant. Expression of 152 proteins showed a greater than 2-fold difference. Proteins involved in motility and cell wall/membrane were the most abundant. Through phenotypic assays, we further demonstrated that the mutant displayed reduced motility and tolerance to treatment, but it showed increased biofilm formation. Interestingly, the LPS profile was unchanged. These results lead to new insights into the functions of wxcB that is associated with cell wall/membrane functions, which contributes to pathogen virulence. Copyright © 2014 Elsevier Inc. All rights reserved.

Functions of Parental Involvement and Effects of School Climate on Bullying Behaviors among South Korean Middle School Students

ERIC Educational Resources Information Center

Lee, Chang-Hun; Song, Juyoung

2012-01-01

This study uses an ecological systems theory to understand bullying behavior. Emphasis is given to overcome limitations found in the literature, such as very little empirical research on functions of parental involvement and the impacts of school climate on bullying as an outcome variable. Two functions of parental involvement investigated are (a)…

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Diurnal rhythmicity in biological processes involved in bioavailability of functional food factors.

PubMed

Tsurusaki, Takashi; Sakakibara, Hiroyuki; Aoshima, Yoshiki; Yamazaki, Shunsuke; Sakono, Masanobu; Shimoi, Kayoko

2013-05-01

In the past few decades, many types of functional factors have been identified in dietary foods; for example, flavonoids are major groups widely distributed in the plant kingdom. However, the absorption rates of the functional food factors are usually low, and many of these are difficult to be absorbed in the intact forms because of metabolization by biological processes during absorption. To gain adequate beneficial effects, it is therefore mandatory to know whether functional food factors are absorbed in sufficient quantity, and then reach target organs while maintaining beneficial effects. These are the reasons why the bioavailability of functional food factors has been well investigated using rodent models. Recently, many of the biological processes have been reported to follow diurnal rhythms recurring every 24 h. Therefore, absorption and metabolism of functional food factors influenced by the biological processes may vary with time of day. Consequently, the evaluation of the bioavailability of functional food factors using rodent models should take into consideration the timing of consumption. In this review, we provide a perspective overview of the diurnal rhythm of biological processes involved in the bioavailability of functional food factors, particularly flavonoids.

Quantitative computed tomography for the prediction of pulmonary function after lung cancer surgery: a simple method using simulation software.

PubMed

Ueda, Kazuhiro; Tanaka, Toshiki; Li, Tao-Sheng; Tanaka, Nobuyuki; Hamano, Kimikazu

2009-03-01

The prediction of pulmonary functional reserve is mandatory in therapeutic decision-making for patients with resectable lung cancer, especially those with underlying lung disease. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative computed tomography (CT) helps to identify residual pulmonary function, although the utility of this modality needs investigation. The subjects of this prospective study were 30 patients with resectable lung cancer. A three-dimensional CT lung model was created with voxels representing normal lung attenuation (-600 to -910 Hounsfield units). Residual pulmonary function was predicted by drawing a boundary line between the lung to be preserved and that to be resected, directly on the lung model. The predicted values were correlated with the postoperative measured values. The predicted and measured values corresponded well (r=0.89, p<0.001). Although the predicted values corresponded with values predicted by simple calculation using a segment-counting method (r=0.98), there were two outliers whose pulmonary functional reserves were predicted more accurately by CT than by segment counting. The measured pulmonary functional reserves were significantly higher than the predicted values in patients with extensive emphysematous areas (<-910 Hounsfield units), but not in patients with chronic obstructive pulmonary disease. Quantitative CT yielded accurate prediction of functional reserve after lung cancer surgery and helped to identify patients whose functional reserves are likely to be underestimated. Hence, this modality should be utilized for patients with marginal pulmonary function.

Prediction of functional aerobic capacity without exercise testing

NASA Technical Reports Server (NTRS)

Jackson, A. S.; Blair, S. N.; Mahar, M. T.; Wier, L. T.; Ross, R. M.; Stuteville, J. E.

1990-01-01

The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS).

S100b and BNP predict functional neurological outcome after intracerebral hemorrhage

PubMed Central

James, Michael L.; Blessing, Robert; Phillips-Bute, Barbara G.; Bennett, Ellen; Laskowitz, Daniel T.

2009-01-01

Objective To determine the predictive value of S100b and brain natriuretic peptide (BNP) to accurately and quickly determine discharge prognosis after primary supratentorial intracerebral hemorrhage (ICH). Methods After IRB approval and informed consent, blood samples were obtained and analyzed from 28 adult patients consecutively admitted to the neuroscience intensive care unit with computed tomography-proven supratentorial ICH from June 2003 and December 2004 within the first 24 h after symptom onset for S100b and BNP. Functional outcomes on discharge were dichotomized to favorable (mRS<3) or unfavorable. Results BNP (a neurohormone) and S100b (a marker of glial activation) were found to be independently highly predictive of functional neurological outcome at the time of discharge as measured by modified Rankin Score (BNP:p<0.01, r=0.46; S100b: p<0.01, r=0.42) and Barthel Index (BNP:p<0.01, r=0.54; s100b:p<0.01, r=0.50). Although inclusion of either biomarker produced additive value when included with traditional clinical prognostic variables, such as the ICH Score (Barthel index: p<0.01, r=0.66; mRS:p<0.01, r=0.96), little predictive power is added with inclusion of both biomarkers in a regression model for neurological outcome. Conclusions Serum S100b and BNP levels in the first 24 h after injury accurately predict neurological function at discharge after supratentorial ICH. PMID:19505208

Predicting functional ability in mild cognitive impairment with the Dementia Rating Scale-2.

PubMed

Greenaway, Melanie C; Duncan, Noah L; Hanna, Sherrie; Smith, Glenn E

2012-06-01

We examined the utility of cognitive evaluation to predict instrumental activities of daily living (IADLs) and decisional ability in Mild Cognitive Impairment (MCI). Sixty-seven individuals with single-domain amnestic MCI were administered the Dementia Rating Scale-2 (DRS-2) as well as the Everyday Cognition assessment form to assess functional ability. The DRS-2 Total Scores and Initiation/Perseveration and Memory subscales were found to be predictive of IADLs, with Total Scores accounting for 19% of the variance in IADL performance on average. In addition, the DRS-2 Initiation/Perseveration and Total Scores were predictive of ability to understand information, and the DRS-2 Conceptualization helped predict ability to communicate with others, both key variables in decision-making ability. These findings suggest that performance on the DRS-2, and specific subscales related to executive function and memory, is significantly related to IADLs in individuals with MCI. These cognitive measures are also associated with decision-making-related abilities in MCI.

Protein side chain conformation predictions with an MMGBSA energy function.

PubMed

Gaillard, Thomas; Panel, Nicolas; Simonson, Thomas

2016-06-01

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Combining Spot Sign and Intracerebral Hemorrhage Score to Estimate Functional Outcome: Analysis From the PREDICT Cohort.

PubMed

Schneider, Hauke; Huynh, Thien J; Demchuk, Andrew M; Dowlatshahi, Dar; Rodriguez-Luna, David; Silva, Yolanda; Aviv, Richard; Dzialowski, Imanuel

2018-06-01

The intracerebral hemorrhage (ICH) score is the most commonly used grading scale for stratifying functional outcome in patients with acute ICH. We sought to determine whether a combination of the ICH score and the computed tomographic angiography spot sign may improve outcome prediction in the cohort of a prospective multicenter hemorrhage trial. Prospectively collected data from 241 patients from the observational PREDICT study (Prediction of Hematoma Growth and Outcome in Patients With Intracerebral Hemorrhage Using the CT-Angiography Spot Sign) were analyzed. Functional outcome at 3 months was dichotomized using the modified Rankin Scale (0-3 versus 4-6). Performance of (1) the ICH score and (2) the spot sign ICH score-a scoring scale combining ICH score and spot sign number-was tested. Multivariable analysis demonstrated that ICH score (odds ratio, 3.2; 95% confidence interval, 2.2-4.8) and spot sign number (n=1: odds ratio, 2.7; 95% confidence interval, 1.1-7.4; n>1: odds ratio, 3.8; 95% confidence interval, 1.2-17.1) were independently predictive of functional outcome at 3 months with similar odds ratios. Prediction of functional outcome was not significantly different using the spot sign ICH score compared with the ICH score alone (spot sign ICH score area under curve versus ICH score area under curve: P =0.14). In the PREDICT cohort, a prognostic score adding the computed tomographic angiography-based spot sign to the established ICH score did not improve functional outcome prediction compared with the ICH score. © 2018 American Heart Association, Inc.

Functional identity and diversity of animals predict ecosystem functioning better than species-based indices.

PubMed

Gagic, Vesna; Bartomeus, Ignasi; Jonsson, Tomas; Taylor, Astrid; Winqvist, Camilla; Fischer, Christina; Slade, Eleanor M; Steffan-Dewenter, Ingolf; Emmerson, Mark; Potts, Simon G; Tscharntke, Teja; Weisser, Wolfgang; Bommarco, Riccardo

2015-02-22

Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.

PubMed

Li, Bian; Mendenhall, Jeffrey L; Kroncke, Brett M; Taylor, Keenan C; Huang, Hui; Smith, Derek K; Vanoye, Carlos G; Blume, Jeffrey D; George, Alfred L; Sanders, Charles R; Meiler, Jens

2017-10-01

An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools. © 2017 American Heart Association, Inc.

An improved predictive functional control method with application to PMSM systems

NASA Astrophysics Data System (ADS)

Li, Shihua; Liu, Huixian; Fu, Wenshu

2017-01-01

In common design of prediction model-based control method, usually disturbances are not considered in the prediction model as well as the control design. For the control systems with large amplitude or strong disturbances, it is difficult to precisely predict the future outputs according to the conventional prediction model, and thus the desired optimal closed-loop performance will be degraded to some extent. To this end, an improved predictive functional control (PFC) method is developed in this paper by embedding disturbance information into the system model. Here, a composite prediction model is thus obtained by embedding the estimated value of disturbances, where disturbance observer (DOB) is employed to estimate the lumped disturbances. So the influence of disturbances on system is taken into account in optimisation procedure. Finally, considering the speed control problem for permanent magnet synchronous motor (PMSM) servo system, a control scheme based on the improved PFC method is designed to ensure an optimal closed-loop performance even in the presence of disturbances. Simulation and experimental results based on a hardware platform are provided to confirm the effectiveness of the proposed algorithm.

Assessment of the reliability of protein-protein interactions and protein function prediction.

PubMed

Deng, Minghua; Sun, Fengzhu; Chen, Ting

2003-01-01

As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex

Replication Protein A Presents Canonical Functions and Is Also Involved in the Differentiation Capacity of Trypanosoma cruzi.

PubMed

Pavani, Raphael Souza; da Silva, Marcelo Santos; Fernandes, Carlos Alexandre Henrique; Morini, Flavia Souza; Araujo, Christiane Bezerra; Fontes, Marcos Roberto de Mattos; Sant'Anna, Osvaldo Augusto; Machado, Carlos Renato; Cano, Maria Isabel; Fragoso, Stenio Perdigão; Elias, Maria Carolina

2016-12-01

Replication Protein A (RPA), the major single stranded DNA binding protein in eukaryotes, is composed of three subunits and is a fundamental player in DNA metabolism, participating in replication, transcription, repair, and the DNA damage response. In human pathogenic trypanosomatids, only limited studies have been performed on RPA-1 from Leishmania. Here, we performed in silico, in vitro and in vivo analysis of Trypanosoma cruzi RPA-1 and RPA-2 subunits. Although computational analysis suggests similarities in DNA binding and Ob-fold structures of RPA from T. cruzi compared with mammalian and fungi RPA, the predicted tridimensional structures of T. cruzi RPA-1 and RPA-2 indicated that these molecules present a more flexible tertiary structure, suggesting that T. cruzi RPA could be involved in additional responses. Here, we demonstrate experimentally that the T. cruzi RPA complex interacts with DNA via RPA-1 and is directly related to canonical functions, such as DNA replication and DNA damage response. Accordingly, a reduction of TcRPA-2 expression by generating heterozygous knockout cells impaired cell growth, slowing down S-phase progression. Moreover, heterozygous knockout cells presented a better efficiency in differentiation from epimastigote to metacyclic trypomastigote forms and metacyclic trypomastigote infection. Taken together, these findings indicate the involvement of TcRPA in the metacyclogenesis process and suggest that a delay in cell cycle progression could be linked with differentiation in T. cruzi.

Doppler echocardiographic myocardial stunning index predicts recovery of left ventricular systolic function after primary percutaneous coronary intervention.

PubMed

Sharif, Dawod; Matanis, Wisam; Sharif-Rasslan, Amal; Rosenschein, Uri

2016-10-01

Myocardial stunning is responsible for partially reversible left ventricular (LV) systolic dysfunction after successful primary percutaneous coronary intervention (PPCI) in patients with acute ST-elevation myocardial infarction (STEMI). To test the hypothesis that early coronary blood flow (CBF) to LV systolic function ratios, as an equivalent to LV stunning index (SI), predict recovery of LV systolic function after PPCI in patients with acute STEMI. Twenty-four patients with acute anterior STEMI who had successful PPCI were evaluated and compared to 96 control subjects. Transthoracic echocardiography with measurement of LV ejection fraction (EF), LV, and left anterior descending (LAD) coronary artery area wall-motion score index (WMSI) as well as Doppler sampling of LAD blood velocities, early after PPCI and 5 days later, were performed. SI was evaluated as the early ratio of CBF parameters in the LAD to LV systolic function parameters. Early SI-LVEF well predicted late LVEF (r=.51, P<.01) and the change in LVEF (r=.48, P<.017). Early SI-LVMSI predicted well late LVEF (r=.56, P<.006) and the change in LVEF (r=.46, P<.028). Early SI-LADWMSI predicted late LVEF (r=.44, P<.028). Other SI indices measured as other LAD-CBF to LV systolic function parameters were not predictive of late LV systolic function. LV stunning indices measured as early LAD flow to LVEF, LVWMSI, and LADWMSI ratios well predicted late LVEF and the change in LVEF. Thus, greater early coronary artery flow to LV systolic function parameter ratios predict a better improvement in late LV systolic function after PPCI. © 2016, Wiley Periodicals, Inc.

Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity.

PubMed

Khalsa, Sakh; Mayhew, Stephen D; Przezdzik, Izabela; Wilson, Rebecca; Hale, Joanne; Goldstone, Aimee; Bagary, Manny; Bagshaw, Andrew P

2016-01-01

We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN). Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age). cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST. This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive performance. �

Computational Identification and Functional Predictions of Long Noncoding RNA in Zea mays

PubMed Central

Boerner, Susan; McGinnis, Karen M.

2012-01-01

Background Computational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. In mammals, noncoding transcription is abundant, and often results in functional RNA molecules that do not appear to encode proteins. Many long noncoding RNAs (lncRNAs) appear to have epigenetic regulatory function in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants. Methodology/Principal Findings To explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python has been developed and applied to maize full length cDNA sequences to identify, classify, and localize potential lncRNAs. The pipeline was used in parallel with an SVM tool for identifying ncRNAs to identify the maximal number of ncRNAs in the dataset. Although the available library of sequences was small and potentially biased toward protein coding transcripts, 15% of the sequences were predicted to be noncoding. Approximately 60% of these sequences appear to act as precursors for small RNA molecules and may function to regulate gene expression via a small RNA dependent mechanism. ncRNAs were predicted to originate from both genic and intergenic loci. Of the lncRNAs that originated from genic loci, ∼20% were antisense to the host gene loci. Conclusions/Significance Consistent with similar studies in other organisms, noncoding transcription appears to be widespread in the maize genome. Computational predictions indicate that maize lncRNAs may function to regulate expression of other genes through multiple RNA mediated mechanisms. PMID:22916204

Improved fuzzy PID controller design using predictive functional control structure.

PubMed

Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

2017-11-01

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

PubMed

Xu, Yonghui; Min, Huaqing; Song, Hengjie; Wu, Qingyao

2016-08-01

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of novel families of enzymes involved in oxidative and other complex modifications of bases in nucleic acids.

PubMed

Iyer, Lakshminarayan M; Tahiliani, Mamta; Rao, Anjana; Aravind, L

2009-06-01

Modified bases in nucleic acids present a layer of information that directs biological function over and beyond the coding capacity of the conventional bases. While a large number of modified bases have been identified, many of the enzymes generating them still remain to be discovered. Recently, members of the 2-oxoglutarate- and iron(II)-dependent dioxygenase super-family, which modify diverse substrates from small molecules to biopolymers, were predicted and subsequently confirmed to catalyze oxidative modification of bases in nucleic acids. Of these, two distinct families, namely the AlkB and the kinetoplastid base J binding proteins (JBP) catalyze in situ hydroxylation of bases in nucleic acids. Using sensitive computational analysis of sequences, structures and contextual information from genomic structure and protein domain architectures, we report five distinct families of 2-oxoglutarate- and iron(II)-dependent dioxygenase that we predict to be involved in nucleic acid modifications. Among the DNA-modifying families, we show that the dioxygenase domains of the kinetoplastid base J-binding proteins belong to a larger family that includes the Tet proteins, prototyped by the human oncogene Tet1, and proteins from basidiomycete fungi, chlorophyte algae, heterolobosean amoeboflagellates and bacteriophages. We present evidence that some of these proteins are likely to be involved in oxidative modification of the 5-methyl group of cytosine leading to the formation of 5-hydroxymethylcytosine. The Tet/JBP homologs from basidiomycete fungi such as Laccaria and Coprinopsis show large lineage-specific expansions and a tight linkage with genes encoding a novel and distinct family of predicted transposases, and a member of the Maelstrom-like HMG family. We propose that these fungal members are part of a mobile transposon. To the best of our knowledge, this is the first report of a eukaryotic transposable element that encodes its own DNA-modification enzyme with a

Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

PubMed

Zitnik, Marinka; Zupan, Blaž

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.

Longitudinal prediction and concurrent functioning of adolescent girls demonstrating various profiles of dating violence and victimization.

PubMed

Chiodo, Debbie; Crooks, Claire V; Wolfe, David A; McIsaac, Caroline; Hughes, Ray; Jaffe, Peter G

2012-08-01

Adolescent girls are involved in physical dating violence as both perpetrators and victims, and there are negative consequences associated with each of these behaviors. This article used a prospective design with 519 girls dating in grade 9 to predict profiles of dating violence in grade 11 based on relationships with families of origin (child maltreatment experiences, harsh parenting), and peers (harassment, delinquency, relational aggression). In addition, dating violence profiles were compared on numerous indices of adjustment (school connectedness, grades, self-efficacy and community connectedness) and maladjustment (suicide attempts, distress, delinquency, sexual behavior) for descriptive purposes. The most common profile was no dating violence (n = 367) followed by mutual violence (n = 81). Smaller numbers of girls reported victimization or perpetration only (ns = 39 and 32, respectively). Predicting grade 11 dating violence profile membership from grade 9 relationships was limited, although delinquency, parental rejection, and sexual harassment perpetration predicted membership to the mutually violent group, and delinquency predicted the perpetrator-only group. Compared to the non-violent group, the mutually violent girls in grade 11 had lower grades, poorer self-efficacy, and lower school connectedness and community involvement. Furthermore, they had higher rates of peer aggression and delinquency, were less likely to use condoms and were much more likely to have considered suicide. There were fewer differences among the profiles for girls involved with dating violence. In addition, the victims-only group reported higher rates of sexual intercourse, comparable to the mutually violent group and those involved in nonviolent relationships. Implications for prevention and intervention are highlighted.

Nailfold capillaroscopy for prediction of novel future severe organ involvement in systemic sclerosis.

PubMed

Smith, Vanessa; Riccieri, Valeria; Pizzorni, Carmen; Decuman, Saskia; Deschepper, Ellen; Bonroy, Carolien; Sulli, Alberto; Piette, Yves; De Keyser, Filip; Cutolo, Maurizio

2013-12-01

Assessment of associations of nailfold videocapillaroscopy (NVC) scleroderma (systemic sclerosis; SSc) ("early," "active," and "late") with novel future severe clinical involvement in 2 independent cohorts. Sixty-six consecutive Belgian and 82 Italian patients with SSc underwent NVC at baseline. Images were blindly assessed and classified into normal, early, active, or late NVC pattern. Clinical evaluation was performed for 9 organ systems (general, peripheral vascular, skin, joint, muscle, gastrointestinal tract, lung, heart, and kidney) according to the Medsger disease severity scale (DSS) at baseline and in the future (18-24 months of followup). Severe clinical involvement was defined as category 2 to 4 per organ of the DSS. Logistic regression analysis (continuous NVC predictor variable) was performed. The OR to develop novel future severe organ involvement was stronger according to more severe NVC patterns and similar in both cohorts. In simple logistic regression analysis the OR in the Belgian/Italian cohort was 2.16 (95% CI 1.19-4.47, p = 0.010)/2.33 (95% CI 1.36-4.22, p = 0.002) for the early NVC SSc pattern, 4.68/5.42 for the active pattern, and 10.14/12.63 for the late pattern versus the normal pattern. In multiple logistic regression analysis, adjusting for disease duration, subset, and vasoactive medication, the OR was 2.99 (95% CI 1.31-8.82, p = 0.007)/1.88 (95% CI 1.00-3.71, p = 0.050) for the early NVC SSc pattern, 8.93/3.54 for the active pattern, and 26.69/6.66 for the late pattern versus the normal pattern. Capillaroscopy may be predictive of novel future severe organ involvement in SSc, as attested by 2 independent cohorts.

Predicting functional recovery after acute ankle sprain.

PubMed

O'Connor, Sean R; Bleakley, Chris M; Tully, Mark A; McDonough, Suzanne M

2013-01-01

Ankle sprains are among the most common acute musculoskeletal conditions presenting to primary care. Their clinical course is variable but there are limited recommendations on prognostic factors. Our primary aim was to identify clinical predictors of short and medium term functional recovery after ankle sprain. A secondary analysis of data from adult participants (N = 85) with an acute ankle sprain, enrolled in a randomized controlled trial was undertaken. The predictive value of variables (age, BMI, gender, injury mechanism, previous injury, weight-bearing status, medial joint line pain, pain during weight-bearing dorsiflexion and lateral hop test) recorded at baseline and at 4 weeks post injury were investigated for their prognostic ability. Recovery was determined from measures of subjective ankle function at short (4 weeks) and medium term (4 months) follow ups. Multivariate stepwise linear regression analyses were undertaken to evaluate the association between the aforementioned variables and functional recovery. Greater age, greater injury grade and weight-bearing status at baseline were associated with lower function at 4 weeks post injury (p<0.01; adjusted R square=0.34). Greater age, weight-bearing status at baseline and non-inversion injury mechanisms were associated with lower function at 4 months (p<0.01; adjusted R square=0.20). Pain on medial palpation and pain on dorsiflexion at 4 weeks were the most valuable prognostic indicators of function at 4 months (p< 0.01; adjusted R square=0.49). The results of the present study provide further evidence that ankle sprains have a variable clinical course. Age, injury grade, mechanism and weight-bearing status at baseline provide some prognostic information for short and medium term recovery. Clinical assessment variables at 4 weeks were the strongest predictors of recovery, explaining 50% of the variance in ankle function at 4 months. Further prospective research is required to highlight the factors that best

Biological interpretation of genome-wide association studies using predicted gene functions.

PubMed

Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

2015-01-19

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

PREOPERATIVE PREDICTION OF LUNG FUNCTION IN PNEUMONECTOMY BY SPIROMETRY AND LUNG PERFUSION SCINTIGRAPHY

PubMed Central

Cukic, Vesna

2012-01-01

Introduction: Nowadays an increasing number of lung resections are being done because of the rising prevalence of lung cancer that occurs mainly in patients with limited lung function, what is caused by common etiologic factor - smoking cigarettes. Loss of lung tissue in such patients can worsen much the postoperative pulmonary function. So it is necessary to asses the postoperative pulmonary function especially after maximal resection, i.e. pneumonectomy. Objective: To check over the accuracy of preoperative prognosis of postoperative lung function after pneumonectomy using spirometry and lung perfusion scinigraphy. Material and methods: The study was done on 17 patients operated at the Clinic for thoracic surgery, who were treated previously at the Clinic for Pulmonary Diseases “Podhrastovi” in the period from 01. 12. 2008. to 01. 06. 2011. Postoperative pulmonary function expressed as ppoFEV1 (predicted postoperative forced expiratory volume in one second) was prognosticated preoperatively using spirometry, i.e.. simple calculation according to the number of the pulmonary segments to be removed and perfusion lung scintigraphy. Results: There is no significant deviation of postoperative achieved values of FEV1 from predicted ones obtained by both methods, and there is no significant differences between predicted values (ppoFEV1) obtained by spirometry and perfusion scintigraphy. Conclusion: It is necessary to asses the postoperative pulmonary function before lung resection to avoid postoperative respiratory failure and other cardiopulmonary complications. It is absolutely necessary for pneumonectomy, i.e.. maximal pulmonary resection. It can be done with great possibility using spirometry or perfusion lung scintigraphy. PMID:23378687

Predicting protein function and other biomedical characteristics with heterogeneous ensembles

PubMed Central

Whalen, Sean; Pandey, Om Prakash

2015-01-01

Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink. PMID:26342255

Evaluation of the Three Parameter Weibull Distribution Function for Predicting Fracture Probability in Composite Materials

DTIC Science & Technology

1978-03-01

for the risk of rupture for a unidirectionally laminat - ed composite subjected to pure bending. (5D This equation can be simplified further by use of...C EVALUATION OF THE THREE PARAMETER WEIBULL DISTRIBUTION FUNCTION FOR PREDICTING FRACTURE PROBABILITY IN COMPOSITE MATERIALS. THESIS / AFIT/GAE...EVALUATION OF THE THREE PARAMETER WE1BULL DISTRIBUTION FUNCTION FOR PREDICTING FRACTURE PROBABILITY IN COMPOSITE MATERIALS THESIS Presented

Teacher Involvement in the Development of Function-Based Behaviour Intervention Plans for Students with Challenging Behaviour

ERIC Educational Resources Information Center

O'Neill, Sue; Stephenson, Jennifer

2009-01-01

This article examines literature published since 1997 on functional behaviour assessment (FBA) and behaviour intervention plans (BIPs), involving school-based personnel, for children identified as having or being at risk of emotional/behavioural disorder (E/BD) in school settings. Of interest was the level of involvement of school-based personnel…

Do Executive Function and Impulsivity Predict Adolescent Health Behaviour after Accounting for Intelligence? Findings from the ALSPAC Cohort.

PubMed

Stautz, Kaidy; Pechey, Rachel; Couturier, Dominique-Laurent; Deary, Ian J; Marteau, Theresa M

2016-01-01

Executive function, impulsivity, and intelligence are correlated markers of cognitive resource that predict health-related behaviours. It is unknown whether executive function and impulsivity are unique predictors of these behaviours after accounting for intelligence. Data from 6069 participants from the Avon Longitudinal Study of Parents and Children were analysed to investigate whether components of executive function (selective attention, attentional control, working memory, and response inhibition) and impulsivity (parent-rated) measured between ages 8 and 10, predicted having ever drunk alcohol, having ever smoked, fruit and vegetable consumption, physical activity, and overweight at age 13, after accounting for intelligence at age 8 and childhood socioeconomic characteristics. Higher intelligence predicted having drunk alcohol, not smoking, greater fruit and vegetable consumption, and not being overweight. After accounting for intelligence, impulsivity predicted alcohol use (odds ratio = 1.10; 99% confidence interval = 1.02, 1.19) and smoking (1.22; 1.11, 1.34). Working memory predicted not being overweight (0.90; 0.81, 0.99). After accounting for intelligence, executive function predicts overweight status but not health-related behaviours in early adolescence, whilst impulsivity predicts the onset of alcohol and cigarette use, all with small effects. This suggests overlap between executive function and intelligence as predictors of health behaviour in this cohort, with trait impulsivity accounting for additional variance.

Does early paternal involvement predict offspring developmental diagnoses?

PubMed

Jackson, Dylan B; Newsome, Jamie; Beaver, Kevin M

2016-12-01

A long line of research has illustrated that fathers play an important role in the development of their children. Few studies, however, have examined the impact of paternal involvement at the earliest stages of life on developmental diagnoses in childhood. The present study extends this line of research by exploring the possibility that paternal involvement prenatally, postnatally, and at the time of birth may influence offspring risk for various diagnoses in childhood. A quasi-experimental, propensity score matching design was used to create treatment and control groups to assess the relationship between paternal involvement at each stage of development and developmental diagnoses. Approximately 6000 children, and a subsample of fathers, who participated in the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B). Activity, attention and learning, speech or language, and other diagnoses in early childhood, and overall number of diagnoses at 4years of age. We find no consistent evidence that low paternal involvement prenatally or postnatally increases the risk of various developmental diagnoses by age 4. However, children whose fathers were absent at the time of their birth were at significantly greater risk of incurring various developmental diagnoses, as well as a significantly greater number of developmental diagnoses. The findings expand our understanding of exactly how early paternal influence begins and the specific dimensions of early father behaviors that are related to the risk of various developmental diagnoses. Ultimately, these results have important implications concerning father involvement during the earliest stages of the life course. Copyright © 2016. Published by Elsevier Ireland Ltd.

Functional involvement of human discs large tumor suppressor in cytokinesis

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

Unno, Kenji; Hanada, Toshihiko; Chishti, Athar H.

2008-10-15

Cytokinesis is the final step of cell division that completes the separation of two daughter cells. We found that the human discs large (hDlg) tumor suppressor homologue is functionally involved in cytokinesis. The guanylate kinase (GUK) domain of hDlg mediates the localization of hDlg to the midbody during cytokinesis, and over-expression of the GUK domain in U2OS and HeLa cells impaired cytokinesis. Mouse embryonic fibroblasts (MEFs) derived from dlg mutant mice contained an increased number of multinucleated cells and showed reduced proliferation in culture. A kinesin-like motor protein, GAKIN, which binds directly to the GUK domain of hDlg, exhibited amore » similar intracellular distribution pattern with hDlg throughout mitosis and localized to the midbody during cytokinesis. However, the targeting of hDlg and GAKIN to the midbody appeared to be independent of each other. The midbody localization of GAKIN required its functional kinesin-motor domain. Treatment of cells with the siRNA specific for hDlg and GAKIN caused formation of multinucleated cells and delayed cytokinesis. Together, these results suggest that hDlg and GAKIN play functional roles in the maintenance of midbody architecture during cytokinesis.« less

The Role of Family Involvement in Predicting Student-Teacher Relationships and Academic and Behavioral Outcomes for Children of Immigrants

ERIC Educational Resources Information Center

Ryce, Patrice

2012-01-01

Using a multi-ethnic, socioeconomically varied sample of children of immigrants attending Islamic and public schools from first through third grade, this dissertation examined the degree to which school-based family involvement predicted teacher perceptions of value differences with parents, teacher expectations, child externalizing behavioral…

Brain Natriuretic Hormone Predicts Stress Induced Alterations in Diastolic Function

PubMed Central

Choksy, Pratik; Davis, Harry C.; Januzzi, James; Thayer, Julian; Harshfield, Gregory; Robinson, Vincent JB; Kapuku, Gaston K.

2015-01-01

Background Mental stress (MS) reduces diastolic function (DF) and may lead to congestive heart failure with preserved systolic function. Whether brain natriuretic hormone (BNP) mediates the relationship of MS with DF is unknown. Method and Results 160 individuals aged 30 to 50 years underwent 2 hour protocol of 40 minutes rest, videogame stressor and recovery. Hemodynamics, pro-BNP samples and DF indices were obtained throughout the protocol. Separate regression analyses were conducted using rest and stress E/A, E’ and E/E’ as dependent variables. Predictor variables were entered into the stepwise regression models in a hierarchical fashion. At the first level age, sex, race, height, BMI, pro-BNP, and LVM were permitted to enter the models. The second level consisted of SBP, DBP and HR. The final level contained cross-product terms of race by SBP, DBP and HR. E/A ratio was lower during stress compared to rest, and recovery (p<0.01). Resting E/A ratio was predicted by a regression model of age (−.31), pro-BNP (.16), HR (−.40) and DBP (−.23) with an R2 = .33. Stress E/A ratio was predicted by age (−.24), pro-BNP (.08), HR (−.38), and SBP (−.21), total R2 = .22. Resting E’ model consisted of age (−.22), pro-BNP (.26), DBP (−.27) and LVM (−.15) with an R2 = .29. Stress E’ was predicted by age (−.18), pro-BNP (.35) and LVM (−.18) with an R2 = .18. Resting E/E’ was predicted by race (.17, B>W) and DBP (.24) with an R2 = .10. Stress E/E’ consisted of pro-BNP (−.36), height (−.26) and HR (−.21) with R2 = .15. Conclusion pro-BNP predicts both resting and stress DF suggesting that lower BNP during MS may be a maker of diastolic dysfunction in apparently healthy individuals. PMID:24841419

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks.

PubMed

Yao, Xiaojun; Zhang, Xiaoyun; Zhang, Ruisheng; Liu, Mancang; Hu, Zhide; Fan, Botao

2002-05-16

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks. To get the best prediction results, some strategies are also employed to optimize the topology and learning parameters of the RBFNNs. For the test set, a predictive correlation coefficient R=0.9910 and root mean squared error of 14.1 are obtained. Results show that radial basis function networks can give satisfactory prediction ability and its optimization is less-time consuming and easy to implement.

Clinical history and biologic age predicted falls better than objective functional tests.

PubMed

Gerdhem, Paul; Ringsberg, Karin A M; Akesson, Kristina; Obrant, Karl J

2005-03-01

Fall risk assessment is important because the consequences, such as a fracture, may be devastating. The objective of this study was to find the test or tests that best predicted falls in a population-based sample of elderly women. The fall-predictive ability of a questionnaire, a subjective estimate of biologic age and objective functional tests (gait, balance [Romberg and sway test], thigh muscle strength, and visual acuity) were compared in 984 randomly selected women, all 75 years of age. A recalled fall was the most important predictor for future falls. Only recalled falls and intake of psycho-active drugs independently predicted future falls. Women with at least five of the most important fall predictors (previous falls, conditions affecting the balance, tendency to fall, intake of psychoactive medication, inability to stand on one leg, high biologic age) had an odds ratio of 11.27 (95% confidence interval 4.61-27.60) for a fall (sensitivity 70%, specificity 79%). The more time-consuming objective functional tests were of limited importance for fall prediction. A simple clinical history, the inability to stand on one leg, and a subjective estimate of biologic age were more important as part of the fall risk assessment.

Biological interpretation of genome-wide association studies using predicted gene functions

PubMed Central

Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

2015-01-01

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

Requirements for Predictive Density Functional Theory Methods for Heavy Materials Equation of State

NASA Astrophysics Data System (ADS)

Mattsson, Ann E.; Wills, John M.

2012-02-01

The difficulties in experimentally determining the Equation of State of actinide and lanthanide materials has driven the development of many computational approaches with varying degree of empiricism and predictive power. While Density Functional Theory (DFT) based on the Schr"odinger Equation (possibly with relativistic corrections including the scalar relativistic approach) combined with local and semi-local functionals has proven to be a successful and predictive approach for many materials, it is not giving enough accuracy, or even is a complete failure, for the actinides. To remedy this failure both an improved fundamental description based on the Dirac Equation (DE) and improved functionals are needed. Based on results obtained using the appropriate fundamental approach of DFT based on the DE we discuss the performance of available semi-local functionals, the requirements for improved functionals for actinide/lanthanide materials, and the similarities in how functionals behave in transition metal oxides. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Utility functions predict variance and skewness risk preferences in monkeys

PubMed Central

Genest, Wilfried; Stauffer, William R.; Schultz, Wolfram

2016-01-01

Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals’ preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals’ preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys’ choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences. PMID:27402743

Functional traits help predict post-disturbance demography of tropical trees.

PubMed

Flores, Olivier; Hérault, Bruno; Delcamp, Matthieu; Garnier, Éric; Gourlet-Fleury, Sylvie

2014-01-01

How tropical tree species respond to disturbance is a central issue of forest ecology, conservation and resource management. We define a hierarchical model to investigate how functional traits measured in control plots relate to the population change rate and to demographic rates for recruitment and mortality after disturbance by logging operations. Population change and demographic rates were quantified on a 12-year period after disturbance and related to seven functional traits measured in control plots. The model was calibrated using a Bayesian Network approach on 53 species surveyed in permanent forest plots (37.5 ha) at Paracou in French Guiana. The network analysis allowed us to highlight both direct and indirect relationships among predictive variables. Overall, 89% of interspecific variability in the population change rate after disturbance were explained by the two demographic rates, the recruitment rate being the most explicative variable. Three direct drivers explained 45% of the variability in recruitment rates, including leaf phosphorus concentration, with a positive effect, and seed size and wood density with negative effects. Mortality rates were explained by interspecific variability in maximum diameter only (25%). Wood density, leaf nitrogen concentration, maximum diameter and seed size were not explained by variables in the analysis and thus appear as independent drivers of post-disturbance demography. Relationships between functional traits and demographic parameters were consistent with results found in undisturbed forests. Functional traits measured in control conditions can thus help predict the fate of tropical tree species after disturbance. Indirect relationships also suggest how different processes interact to mediate species demographic response.

Utility functions predict variance and skewness risk preferences in monkeys.

PubMed

Genest, Wilfried; Stauffer, William R; Schultz, Wolfram

2016-07-26

Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals' preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals' preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys' choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences.

Neurocognitive functioning predicts frailty index in HIV.

PubMed

Oppenheim, Hannah; Paolillo, Emily W; Moore, Raeanne C; Ellis, Ronald J; Letendre, Scott L; Jeste, Dilip V; Grant, Igor; Moore, David J

2018-06-06

To evaluate the association between a frailty index (i.e., scale of accumulated deficits) and neurocognitive functioning among persons living with HIV/AIDS (PLWHA). Observational, cross-sectional data were gathered from the University of California, San Diego, HIV Neurobehavioral Research Program from 2002 to 2016. Eight hundred eleven PLWHA aged 18 to 79 years completed comprehensive physical, neuropsychological, and neuromedical evaluations. The frailty index was composed of 26 general and HIV-specific health maintenance measures, and reflects the proportion of accumulated deficits from 0 (no deficits) to 1 (all 26 deficits). Multiple linear regression was used to examine the association between continuous frailty index scores and neurocognitive functioning. Participants had a mean age of 44.6 years (11.2), and were mostly male (86.9%) and white (60.2%) with a mean frailty index of 0.26 (0.11). Over the study period, prevalence of HIV-related components (e.g., low CD4) decreased, while non-HIV comorbidities (e.g., diabetes) increased. There were no changes in the frailty index by study year. Higher frailty index was associated with worse global neurocognitive functioning, even after adjusting for covariates (age, employment, and premorbid intellectual functioning; b = -0.007; 95% confidence interval [CI] = -0.0112 to -0.003; p < 0.001). The cognitive domains of verbal fluency (b = -0.004; 95% CI = -0.006 to -0.002), executive functioning (b = -0.004; 95% CI = -0.006 to -0.002), processing speed (b = -0.005; 95% CI = -0.007 to -0.003), and motor skills (b = -0.006; 95% CI = -0.007 to -0.005) also significantly predicted worse frailty index score ( p values <0.001). A frailty index can standardize how clinicians identify PLWHA who may be at higher risk of neurocognitive impairment. © 2018 American Academy of Neurology.

Expression and functional studies of genes involved in transport and metabolism of glycerol in Pachysolen tannophilus

PubMed Central

2013-01-01

Background Pachysolen tannophilus is a non-conventional yeast, which can metabolize many of the carbon sources found in low cost feedstocks including glycerol and xylose. The xylose utilisation pathways have been extensively studied in this organism. However, the mechanism behind glycerol metabolism is poorly understood. Using the recently published genome sequence of P. tannophilus CBS4044, we searched for genes with functions in glycerol transport and metabolism by performing a BLAST search using the sequences of the relevant genes from Saccharomyces cerevisiae as queries. Results Quantitative real-time PCR was performed to unveil the expression patterns of these genes during growth of P. tannophilus on glycerol and glucose as sole carbon sources. The genes predicted to be involved in glycerol transport in P. tannophilus were expressed in S. cerevisiae to validate their function. The S. cerevisiae strains transformed with heterologous genes showed improved growth and glycerol consumption rates with glycerol as the sole carbon source. Conclusions P. tannophilus has characteristics relevant for a microbial cell factory to be applied in a biorefinery setting, i.e. its ability to utilise the carbon sources such as xylose and glycerol. However, the strain is not currently amenable to genetic modification and transformation. Heterologous expression of the glycerol transporters from P. tannophilus, which has a relatively high growth rate on glycerol, could be used as an approach for improving the efficiency of glycerol assimilation in other well characterized and applied cell factories such as S. cerevisiae. PMID:23514356

Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions

PubMed Central

2016-01-01

Recent studies of children's tool innovation have revealed that there is variation in children's success in middle-childhood. In two individual differences studies, we sought to identify personal characteristics that might predict success on an innovation task. In Study 1, we found that although measures of divergent thinking were related to each other they did not predict innovation success. In Study 2, we measured executive functioning including: inhibition, working memory, attentional flexibility and ill-structured problem-solving. None of these measures predicted innovation, but, innovation was predicted by children's performance on a receptive vocabulary scale that may function as a proxy for general intelligence. We did not find evidence that children's innovation was predicted by specific personal characteristics. PMID:26926280

Perceived participation and autonomy: aspects of functioning and contextual factors predicting participation after stroke.

PubMed

Fallahpour, Mandana; Tham, Kerstin; Joghataei, Mohammad Taghi; Jonsson, Hans

2011-04-01

To describe perceived participation and autonomy among a sample of persons with stroke in Iran and to identify different aspects of functioning and contextual factors predicting participation after stroke. A cross-sectional study. A total of 102 persons, between 27 and 75 years of age, diagnosed with first-ever stroke. Participants were assessed for different aspects of functioning, contextual factors and health conditions. Participation was assessed using the Persian version of the Impact on Participation and Autonomy questionnaire. This study demonstrated that the majority of the study population perceived their participation and autonomy to be good to fair in the different domains of their participation, but not with respect to the autonomy outdoors domain. In addition, physical function was found to be the most important variable predicting performance-based participation, whereas mood state was the most important variable predicting social-based participation. The results emphasize the importance of physical function, mood state and access to caregiving services as predictors of participation in everyday life after stroke. Whilst there are two dimensions of participation in this Persian sample of persons with stroke, the factors explaining participation seem to be the same across the cultures.

Executive Function and Personality Predict Instrumental Activities of Daily Living in Alzheimer Disease.

PubMed

Roy, Shumita; Ficarro, Stephanie; Duberstein, Paul; Chapman, Benjamin P; Dubovsky, Steven; Paroski, Margaret; Szigeti, Kinga; Benedict, Ralph H B

2016-11-01

Previous research shows that executive function (EF) and personality independently predict functional decline. Our objective was to determine whether personality traits predict independence with instrumental activities of daily living (IADLs), after accounting for executive dysfunction, in a mixed sample of patients with amnestic mild cognitive impairment (MCI) and Alzheimer disease (AD). In a cross-sectional analysis at a university medical center, 63 healthy older adults (median age: 67.6 years; 71% women) and 119 patients (median age: 75.0 years; 58% women) with varying degrees of AD (probable AD: 85; possible AD: 3; amnestic MCI: 31) were studied. Standardized neuropsychological measures, NEO Five-Factor Inventory (NEO-FFI), and informant-report Lawton and Brody IADL scales were used. All participants underwent neuropsychological evaluation, including administration of self- and informant-report NEO-FFI. Patients additionally underwent neurologic examination, and their informants completed the Lawton and Brody IADL scale. When testing the association between EF and personality on IADLs in the patient sample, conceptual card sorting, informant-report Openness, and informant-report Conscientiousness all significantly predicted IADLs, after accounting for age, education, and depression. In addition, a significant interaction showed that low Conscientiousness and executive dysfunction, in combination, can predict impairment of IADLs. Personality has a unique association with IADLs in patients with AD pathology that is not explained by EF. The findings confirm prior speculation that personality, in addition to cognitive dysfunction, is a risk factor for functional decline. Early identification of vulnerable individuals may allow for intervention to prolong functional independence. Copyright © 2016 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Predicting functional communication ability in children with cerebral palsy at school entry.

PubMed

Coleman, Andrea; Weir, Kelly; Ware, Robert S; Boyd, Roslyn

2015-03-01

To explore the value of demographic, environmental, and early clinical characteristics in predicting functional communication in children with cerebral palsy (CP) at school entry. Data are from an Australian prospective longitudinal study of children with CP. Children assessed at 18 to 24 and 48 to 60 months corrected age were included in the study. Functional communication was classified at 48 to 60 months using the Communication Function Classification System (CFCS). Predictive variables included communication skills at 18 to 24 months, evaluated using the Communication and Symbolic Behavioural Scales Developmental Profile (CSBS-DP) Infant-Toddler Checklist. Early Gross Motor Function Classification System (GMFCS), Manual Ability Classification System, and motor type and distribution were evaluated by two physiotherapists. Demographic and comorbid variables were obtained through parent interview with a paediatrician or rehabilitation specialist. A total of 114 children (76 males, 38 females) were included in the study. At 18 to 24 months the mean CSBS-DP was 84.9 (SD 19.0). The CFCS distribution at 48 to 60 months was I=36(32%), II=25(22%), III=20(18%), IV=19(17%), and V=14(12%). In multivariable regression analysis, only CSBS-DP (p<0.01) and GMFCS (p<0.01) at 18 to 24 months were predictors of functional communication at school entry. Body structure and function and not environmental factors impact functional communication at school entry in children with CP. This provides valuable guidance for early screening, parent education, and future planning of intervention programs to improve functional communication. © 2014 Mac Keith Press.

Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics.

PubMed

Mahmood, Khalid; Jung, Chol-Hee; Philip, Gayle; Georgeson, Peter; Chung, Jessica; Pope, Bernard J; Park, Daniel J

2017-05-16

Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets. These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.

Predicting invasive species impacts: a community module functional response approach reveals context dependencies.

PubMed

Paterson, Rachel A; Dick, Jaimie T A; Pritchard, Daniel W; Ennis, Marilyn; Hatcher, Melanie J; Dunn, Alison M

2015-03-01

Predatory functional responses play integral roles in predator-prey dynamics, and their assessment promises greater understanding and prediction of the predatory impacts of invasive species. Other interspecific interactions, however, such as parasitism and higher-order predation, have the potential to modify predator-prey interactions and thus the predictive capability of the comparative functional response approach. We used a four-species community module (higher-order predator; focal native or invasive predators; parasites of focal predators; native prey) to compare the predatory functional responses of native Gammarus duebeni celticus and invasive Gammarus pulex amphipods towards three invertebrate prey species (Asellus aquaticus, Simulium spp., Baetis rhodani), thus, quantifying the context dependencies of parasitism and a higher-order fish predator on these functional responses. Our functional response experiments demonstrated that the invasive amphipod had a higher predatory impact (lower handling time) on two of three prey species, which reflects patterns of impact observed in the field. The community module also revealed that parasitism had context-dependent influences, for one prey species, with the potential to further reduce the predatory impact of the invasive amphipod or increase the predatory impact of the native amphipod in the presence of a higher-order fish predator. Partial consumption of prey was similar for both predators and occurred increasingly in the order A. aquaticus, Simulium spp. and B. rhodani. This was associated with increasing prey densities, but showed no context dependencies with parasitism or higher-order fish predator. This study supports the applicability of comparative functional responses as a tool to predict and assess invasive species impacts incorporating multiple context dependencies. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.

PubMed

Xu, Weijun; Lucke, Andrew J; Fairlie, David P

2015-04-01

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches. Copyright © 2015 Elsevier Inc. All rights reserved.

Do Executive Function and Impulsivity Predict Adolescent Health Behaviour after Accounting for Intelligence? Findings from the ALSPAC Cohort

PubMed Central

Pechey, Rachel; Couturier, Dominique-Laurent; Deary, Ian J.; Marteau, Theresa M.

2016-01-01

Objective Executive function, impulsivity, and intelligence are correlated markers of cognitive resource that predict health-related behaviours. It is unknown whether executive function and impulsivity are unique predictors of these behaviours after accounting for intelligence. Methods Data from 6069 participants from the Avon Longitudinal Study of Parents and Children were analysed to investigate whether components of executive function (selective attention, attentional control, working memory, and response inhibition) and impulsivity (parent-rated) measured between ages 8 and 10, predicted having ever drunk alcohol, having ever smoked, fruit and vegetable consumption, physical activity, and overweight at age 13, after accounting for intelligence at age 8 and childhood socioeconomic characteristics. Results Higher intelligence predicted having drunk alcohol, not smoking, greater fruit and vegetable consumption, and not being overweight. After accounting for intelligence, impulsivity predicted alcohol use (odds ratio = 1.10; 99% confidence interval = 1.02, 1.19) and smoking (1.22; 1.11, 1.34). Working memory predicted not being overweight (0.90; 0.81, 0.99). Conclusions After accounting for intelligence, executive function predicts overweight status but not health-related behaviours in early adolescence, whilst impulsivity predicts the onset of alcohol and cigarette use, all with small effects. This suggests overlap between executive function and intelligence as predictors of health behaviour in this cohort, with trait impulsivity accounting for additional variance. PMID:27479488

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

PubMed

Sartori, Juliana M; Reckziegel, Ramiro; Passos, Ives Cavalcante; Czepielewski, Leticia S; Fijtman, Adam; Sodré, Leonardo A; Massuda, Raffael; Goi, Pedro D; Vianna-Sulzbach, Miréia; Cardoso, Taiane de Azevedo; Kapczinski, Flávio; Mwangi, Benson; Gama, Clarissa S

2018-08-01

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment. Copyright © 2018 Elsevier Ltd. All rights reserved.

A selective involvement of putamen functional connectivity in youth with internet gaming disorder.

PubMed

Hong, Soon-Beom; Harrison, Ben J; Dandash, Orwa; Choi, Eun-Jung; Kim, Seong-Chan; Kim, Ho-Hyun; Shim, Do-Hyun; Kim, Chang-Dai; Kim, Jae-Won; Yi, Soon-Hyung

2015-03-30

Brain cortico-striatal circuits have consistently been implicated in the pathology of addiction related disorders. We applied a reliable seed-based analysis of the resting-state brain activity to comprehensively delineate the subdivisions of striatal functional connectivity implicated in internet gaming disorder. Among twelve right-handed male adolescents with internet gaming disorder and 11 right-handed and gender-matched healthy controls, we examined group differences in the functional connectivity of dorsal and ventral subdivisions of the caudate nucleus and putamen, as well as the association of these connectivity indices with behavioral measures of internet use. Adolescents with internet gaming disorder showed significantly reduced dorsal putamen functional connectivity with the posterior insula-parietal operculum. More time spent playing online games predicted significantly greater functional connectivity between the dorsal putamen and bilateral primary somatosensory cortices in adolescents with internet gaming disorder, and significantly lower functional connectivity between the dorsal putamen and bilateral sensorimotor cortices in healthy controls. The dorsal putamen functional connectivity was significantly and specifically different in adolescents with internet gaming disorder. The findings suggest a possible biomarker of internet gaming disorder. Copyright © 2015. Published by Elsevier B.V.

Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula

PubMed Central

Geuter, Stephan; Boll, Sabrina; Eippert, Falk; Büchel, Christian

2017-01-01

The computational principles by which the brain creates a painful experience from nociception are still unknown. Classic theories suggest that cortical regions either reflect stimulus intensity or additive effects of intensity and expectations, respectively. By contrast, predictive coding theories provide a unified framework explaining how perception is shaped by the integration of beliefs about the world with mismatches resulting from the comparison of these beliefs against sensory input. Using functional magnetic resonance imaging during a probabilistic heat pain paradigm, we investigated which computations underlie pain perception. Skin conductance, pupil dilation, and anterior insula responses to cued pain stimuli strictly followed the response patterns hypothesized by the predictive coding model, whereas posterior insula encoded stimulus intensity. This novel functional dissociation of pain processing within the insula together with previously observed alterations in chronic pain offer a novel interpretation of aberrant pain processing as disturbed weighting of predictions and prediction errors. DOI: http://dx.doi.org/10.7554/eLife.24770.001 PMID:28524817

A Noise Level Prediction Method Based on Electro-Mechanical Frequency Response Function for Capacitors

PubMed Central

Zhu, Lingyu; Ji, Shengchang; Shen, Qi; Liu, Yuan; Li, Jinyu; Liu, Hao

2013-01-01

The capacitors in high-voltage direct-current (HVDC) converter stations radiate a lot of audible noise which can reach higher than 100 dB. The existing noise level prediction methods are not satisfying enough. In this paper, a new noise level prediction method is proposed based on a frequency response function considering both electrical and mechanical characteristics of capacitors. The electro-mechanical frequency response function (EMFRF) is defined as the frequency domain quotient of the vibration response and the squared capacitor voltage, and it is obtained from impulse current experiment. Under given excitations, the vibration response of the capacitor tank is the product of EMFRF and the square of the given capacitor voltage in frequency domain, and the radiated audible noise is calculated by structure acoustic coupling formulas. The noise level under the same excitations is also measured in laboratory, and the results are compared with the prediction. The comparison proves that the noise prediction method is effective. PMID:24349105

A noise level prediction method based on electro-mechanical frequency response function for capacitors.

PubMed

Zhu, Lingyu; Ji, Shengchang; Shen, Qi; Liu, Yuan; Li, Jinyu; Liu, Hao

2013-01-01

The capacitors in high-voltage direct-current (HVDC) converter stations radiate a lot of audible noise which can reach higher than 100 dB. The existing noise level prediction methods are not satisfying enough. In this paper, a new noise level prediction method is proposed based on a frequency response function considering both electrical and mechanical characteristics of capacitors. The electro-mechanical frequency response function (EMFRF) is defined as the frequency domain quotient of the vibration response and the squared capacitor voltage, and it is obtained from impulse current experiment. Under given excitations, the vibration response of the capacitor tank is the product of EMFRF and the square of the given capacitor voltage in frequency domain, and the radiated audible noise is calculated by structure acoustic coupling formulas. The noise level under the same excitations is also measured in laboratory, and the results are compared with the prediction. The comparison proves that the noise prediction method is effective.

Short Term Single Station GNSS TEC Prediction Using Radial Basis Function Neural Network

NASA Astrophysics Data System (ADS)

Muslim, Buldan; Husin, Asnawi; Efendy, Joni

2018-04-01

TEC prediction models for 24 hours ahead have been developed from JOG2 GPS TEC data during 2016. Eleven month of TEC data were used as a training model of the radial basis function neural network (RBFNN) and 1 month of last data (December 2016) is used for the RBFNN model testing. The RBFNN inputs are the previous 24 hour TEC data and the minimum of Dst index during the previous 24 hours. Outputs of the model are 24 ahead TEC prediction. Comparison of model prediction show that the RBFNN model is able to predict the next 24 hours TEC is more accurate than the TEC GIM model.

Sustained prediction ability of net analyte preprocessing methods using reduced calibration sets. Theoretical and experimental study involving the spectrophotometric analysis of multicomponent mixtures.

PubMed

Goicoechea, H C; Olivieri, A C

2001-07-01

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evaluated and compared with that of partial least-squares (PLS-1). Minimisation of the calibration predicted error sum of squares (PRESS) as a function of a moving spectral window helped to select appropriate working spectral ranges for both methods. The comparison of NAP and PLS results was carried out using two tests: (1) the elliptical joint confidence region for the slope and intercept of a predicted versus actual concentrations plot for a large validation set of samples and (2) the D-optimality criterion concerning the information content of the calibration data matrix. Extensive simulations and experimental validation showed that, unlike PLS, the NAP method is able to furnish highly satisfactory results when the calibration set is reduced from a full four-component central composite to a fractional central composite, as expected from the modelling requirements of net analyte based methods.

Replication Protein A Presents Canonical Functions and Is Also Involved in the Differentiation Capacity of Trypanosoma cruzi

PubMed Central

Pavani, Raphael Souza; da Silva, Marcelo Santos; Fernandes, Carlos Alexandre Henrique; Morini, Flavia Souza; Araujo, Christiane Bezerra; Fontes, Marcos Roberto de Mattos; Sant’Anna, Osvaldo Augusto; Machado, Carlos Renato; Cano, Maria Isabel; Fragoso, Stenio Perdigão; Elias, Maria Carolina

2016-01-01

Replication Protein A (RPA), the major single stranded DNA binding protein in eukaryotes, is composed of three subunits and is a fundamental player in DNA metabolism, participating in replication, transcription, repair, and the DNA damage response. In human pathogenic trypanosomatids, only limited studies have been performed on RPA-1 from Leishmania. Here, we performed in silico, in vitro and in vivo analysis of Trypanosoma cruzi RPA-1 and RPA-2 subunits. Although computational analysis suggests similarities in DNA binding and Ob-fold structures of RPA from T. cruzi compared with mammalian and fungi RPA, the predicted tridimensional structures of T. cruzi RPA-1 and RPA-2 indicated that these molecules present a more flexible tertiary structure, suggesting that T. cruzi RPA could be involved in additional responses. Here, we demonstrate experimentally that the T. cruzi RPA complex interacts with DNA via RPA-1 and is directly related to canonical functions, such as DNA replication and DNA damage response. Accordingly, a reduction of TcRPA-2 expression by generating heterozygous knockout cells impaired cell growth, slowing down S-phase progression. Moreover, heterozygous knockout cells presented a better efficiency in differentiation from epimastigote to metacyclic trypomastigote forms and metacyclic trypomastigote infection. Taken together, these findings indicate the involvement of TcRPA in the metacyclogenesis process and suggest that a delay in cell cycle progression could be linked with differentiation in T. cruzi. PMID:27984589

A meta-analysis of perceptual and cognitive functions involved in useful-field-of-view test performance.

PubMed

Woutersen, Karlijn; Guadron, Leslie; van den Berg, Albert V; Boonstra, F Nienke; Theelen, Thomas; Goossens, Jeroen

2017-12-01

The useful-field-of-view (UFOV) test measures the amount of information someone can extract from a visual scene in one glance. Its scores show relatively strong relationships with everyday activities. The UFOV test consists of three computer tests, suggested to measure processing speed and central vision, divided attention, and selective attention. However, other functions seem to be involved as well. In order to investigate the contribution of these suggested and other perceptual and cognitive functions, we performed a meta-analysis of 116 Pearson's correlation coefficients between UFOV scores and other test scores reported in 18 peer-reviewed articles. We divided these correlations into nine domains: attention, executive functioning, general cognition, memory, spatial ability, visual closure, contrast sensitivity, visual processing speed, and visual acuity. A multivariate mixed-effects model analysis revealed that each domain correlated significantly with each of the UFOV subtest scores. These correlations were stronger for Subtests 2 and 3 than for Subtest 1. Furthermore, some domains were more strongly correlated to the UFOV than others across subtests. We did not find interaction effects between subtest and domain, indicating that none of the UFOV subtests is more selectively sensitive to a particular domain than the others. Thus, none of the three UFOV subtests seem to measure one clear construct. Instead, a range of visual and cognitive functions is involved. Perhaps this is the reason for the UFOV's high ecological validity, as it involves many functions at once, making it harder to compensate if one of them fails.

Visual prediction and perceptual expertise

PubMed Central

Cheung, Olivia S.; Bar, Moshe

2012-01-01

Making accurate predictions about what may happen in the environment requires analogies between perceptual input and associations in memory. These elements of predictions are based on cortical representations, but little is known about how these processes can be enhanced by experience and training. On the other hand, studies on perceptual expertise have revealed that the acquisition of expertise leads to strengthened associative processing among features or objects, suggesting that predictions and expertise may be tightly connected. Here we review the behavioral and neural findings regarding the mechanisms involving prediction and expert processing, and highlight important possible overlaps between them. Future investigation should examine the relations among perception, memory and prediction skills as a function of expertise. The knowledge gained by this line of research will have implications for visual cognition research, and will advance our understanding of how the human brain can improve its ability to predict by learning from experience. PMID:22123523

Depression Predicts Functional Outcome in Geriatric Inpatient Rehabilitation.

PubMed

Shahab, Saba; Nicolici, Diana-Felicia; Tang, Alva; Katz, Paul; Mah, Linda

2017-03-01

To evaluate the effect of depression on functional recovery in geriatric patients who have completed an inpatient rehabilitation program. Prospective cohort study. Inpatient rehabilitation unit of a university-affiliated geriatric hospital. Convenience sample of patients (N=65; mean age, 81.6y; 25 men) admitted to rehabilitation over a 10-month period. Patients >60 years of age who were proficient in English and capable of providing informed consent were eligible to participate in the study. Depression was assessed using both the Geriatric Depression Scale-short form (GDS-15) and the Patient Health Questionnaire (9-item screen for depression) (PHQ-9). Measures of well-established predictors of rehabilitation outcome, which may interact with depression, were also obtained, and multiple regression linear modeling was used to evaluate the relation between depression and functional outcome over and above the contribution of these other factors. FIM (Functional Independence Measure) at discharge from the rehabilitation program. Depression, as assessed by the GDS-15, but not the PHQ-9, was predictive of functional outcome (standardized beta=-.151, P=.030) after controlling for other significant predictors, which included baseline disability, pain, cognition, and educational level. Participation in recreational, but not physio- or occupational, therapy additionally contributed to a small amount of variance in the functional outcome. Our findings suggest that self-report of depression is an independent predictor of functional outcome in high-tolerance, short-duration geriatric rehabilitation. Routine assessment of depressive symptoms in older adults using an instrument (eg, GDS-15) may help identify those at risk for poorer outcomes in rehabilitation. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Salience network integrity predicts default mode network function after traumatic brain injury

PubMed Central

Bonnelle, Valerie; Ham, Timothy E.; Leech, Robert; Kinnunen, Kirsi M.; Mehta, Mitul A.; Greenwood, Richard J.; Sharp, David J.

2012-01-01

Efficient behavior involves the coordinated activity of large-scale brain networks, but the way in which these networks interact is uncertain. One theory is that the salience network (SN)—which includes the anterior cingulate cortex, presupplementary motor area, and anterior insulae—regulates dynamic changes in other networks. If this is the case, then damage to the structural connectivity of the SN should disrupt the regulation of associated networks. To investigate this hypothesis, we studied a group of 57 patients with cognitive impairments following traumatic brain injury (TBI) and 25 control subjects using the stop-signal task. The pattern of brain activity associated with stop-signal task performance was studied by using functional MRI, and the structural integrity of network connections was quantified by using diffusion tensor imaging. Efficient inhibitory control was associated with rapid deactivation within parts of the default mode network (DMN), including the precuneus and posterior cingulate cortex. TBI patients showed a failure of DMN deactivation, which was associated with an impairment of inhibitory control. TBI frequently results in traumatic axonal injury, which can disconnect brain networks by damaging white matter tracts. The abnormality of DMN function was specifically predicted by the amount of white matter damage in the SN tract connecting the right anterior insulae to the presupplementary motor area and dorsal anterior cingulate cortex. The results provide evidence that structural integrity of the SN is necessary for the efficient regulation of activity in the DMN, and that a failure of this regulation leads to inefficient cognitive control. PMID:22393019

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.

PubMed

Backenroth, Daniel; He, Zihuai; Kiryluk, Krzysztof; Boeva, Valentina; Pethukova, Lynn; Khurana, Ekta; Christiano, Angela; Buxbaum, Joseph D; Ionita-Laza, Iuliana

2018-05-03

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources). Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2017-02-01

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

Emotion Perception Mediates the Predictive Relationship between Verbal Ability and Functional Outcome in High-Functioning Adults with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Otsuka, Sadao; Uono, Shota; Yoshimura, Sayaka; Zhao, Shuo; Toichi, Motomi

2017-01-01

The aim of this study was to identify specific cognitive abilities that predict functional outcome in high-functioning adults with autism spectrum disorder (ASD), and to clarify the contribution of those abilities and their relationships. In total, 41 adults with ASD performed cognitive tasks in a broad range of neuro- and social cognitive…

Nonseparable exchange–correlation functional for molecules, including homogeneous catalysis involving transition metals

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

Yu, Haoyu S.; Zhang, Wenjing; Verma, Pragya

2015-01-01

The goal of this work is to develop a gradient approximation to the exchange–correlation functional of Kohn–Sham density functional theory for treating molecular problems with a special emphasis on the prediction of quantities important for homogeneous catalysis and other molecular energetics. Our training and validation of exchange–correlation functionals is organized in terms of databases and subdatabases. The key properties required for homogeneous catalysis are main group bond energies (database MGBE137), transition metal bond energies (database TMBE32), reaction barrier heights (database BH76), and molecular structures (database MS10). We also consider 26 other databases, most of which are subdatabases of a newlymore » extended broad database called Database 2015, which is presented in the present article and in its ESI. Based on the mathematical form of a nonseparable gradient approximation (NGA), as first employed in the N12 functional, we design a new functional by using Database 2015 and by adding smoothness constraints to the optimization of the functional. The resulting functional is called the gradient approximation for molecules, or GAM. The GAM functional gives better results for MGBE137, TMBE32, and BH76 than any available generalized gradient approximation (GGA) or than N12. The GAM functional also gives reasonable results for MS10 with an MUE of 0.018 Å. The GAM functional provides good results both within the training sets and outside the training sets. The convergence tests and the smooth curves of exchange–correlation enhancement factor as a function of the reduced density gradient show that the GAM functional is a smooth functional that should not lead to extra expense or instability in optimizations. NGAs, like GGAs, have the advantage over meta-GGAs and hybrid GGAs of respectively smaller grid-size requirements for integrations and lower costs for extended systems. These computational advantages combined with the relatively high

Predictive Value of Upper Limb Muscles and Grasp Patterns on Functional Outcome in Cervical Spinal Cord Injury.

PubMed

Velstra, Inge-Marie; Bolliger, Marc; Krebs, Jörg; Rietman, Johan S; Curt, Armin

2016-05-01

To determine which single or combined upper limb muscles as defined by the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI); upper extremity motor score (UEMS) and the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), best predict upper limb function and independence in activities of daily living (ADLs) and to assess the predictive value of qualitative grasp movements (QlG) on upper limb function in individuals with acute tetraplegia. As part of a Europe-wide, prospective, longitudinal, multicenter study ISNCSCI, GRASSP, and Spinal Cord Independence Measure (SCIM III) scores were recorded at 1 and 6 months after SCI. For prediction of upper limb function and ADLs, a logistic regression model and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used. Results: Logistic regression and URP-CTREE revealed that a combination of ISNCSCI and GRASSP muscles (to a maximum of 4) demonstrated the best prediction (specificity and sensitivity ranged from 81.8% to 96.0%) of upper limb function and identified homogenous outcome cohorts at 6 months. The URP-CTREE model with the QlG predictors for upper limb function showed similar results. Prediction of upper limb function can be achieved through a combination of defined, specific upper limb muscles assessed in the ISNCSCI and GRASSP. A combination of a limited number of proximal and distal muscles along with an assessment of grasping movements can be applied for clinical decision making for rehabilitation interventions and clinical trials. © The Author(s) 2015.

Predicting functional divergence in protein evolution by site-specific rate shifts

NASA Technical Reports Server (NTRS)

Gaucher, Eric A.; Gu, Xun; Miyamoto, Michael M.; Benner, Steven A.

2002-01-01

Most modern tools that analyze protein evolution allow individual sites to mutate at constant rates over the history of the protein family. However, Walter Fitch observed in the 1970s that, if a protein changes its function, the mutability of individual sites might also change. This observation is captured in the "non-homogeneous gamma model", which extracts functional information from gene families by examining the different rates at which individual sites evolve. This model has recently been coupled with structural and molecular biology to identify sites that are likely to be involved in changing function within the gene family. Applying this to multiple gene families highlights the widespread divergence of functional behavior among proteins to generate paralogs and orthologs.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PubMed

Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

2016-01-01

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Lifestyle Markers Predict Cognitive Function.

PubMed

Masley, Steven C; Roetzheim, Richard; Clayton, Gwendolyn; Presby, Angela; Sundberg, Kelley; Masley, Lucas V

2017-01-01

Rates of mild cognitive impairment and Alzheimer's disease are increasing rapidly. None of the current treatment regimens for Alzheimer's disease are effective in arresting progression. Lifestyle choices may prevent cognitive decline. This study aims to clarify which factors best predict cognitive function. This was a prospective cross-sectional analysis of 799 men and women undergoing health and cognitive testing every 1 to 3 years at an outpatient center. This study utilizes data collected from the first patient visit. Participant ages were 18 to 88 (mean = 50.7) years and the sample was 26.6% female and 73.4% male. Measurements were made of body composition, fasting laboratory and anthropometric measures, strength and aerobic fitness, nutrient and dietary intake, and carotid intimal media thickness (IMT). Each participant was tested with a computerized neurocognitive test battery. Cognitive outcomes were assessed in bivariate analyses using t-tests and correlation coefficients and in multivariable analysis (controlling for age) using multiple linear regression. The initial bivariate analyses showed better Neurocognitive Index (NCI) scores with lower age, greater fitness scores (push-up strength, VO 2 max, and exercise duration during treadmill testing), and lower fasting glucose levels. Better cognitive flexibility scores were also noted with younger age, lower systolic blood pressure, lower body fat, lower carotid IMT scores, greater fitness, and higher alcohol intake. After controlling for age, factors that remained associated with better NCI scores include no tobacco use, lower fasting glucose levels, and better fitness (aerobic and strength). Higher cognitive flexibility scores remained associated with greater aerobic and strength fitness, lower body fat, and higher intake of alcohol. Modifiable biomarkers that impact cognitive performance favorably include greater aerobic fitness and strength, lower blood sugar levels, greater alcohol intake, lower body

Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury

PubMed Central

Field-Fote, Edelle C.; Yang, Jaynie F.; Basso, D. Michele; Gorassini, Monica A.

2017-01-01

Abstract Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans. PMID:27673569

Prediction of postoperative outcome after hepatectomy with a new bedside test for maximal liver function capacity.

PubMed

Stockmann, Martin; Lock, Johan F; Riecke, Björn; Heyne, Karsten; Martus, Peter; Fricke, Michael; Lehmann, Sina; Niehues, Stefan M; Schwabe, Michael; Lemke, Arne-Jörn; Neuhaus, Peter

2009-07-01

To validate the LiMAx test, a new bedside test for the determination of maximal liver function capacity based on C-methacetin kinetics. To investigate the diagnostic performance of different liver function tests and scores including the LiMAx test for the prediction of postoperative outcome after hepatectomy. Liver failure is a major cause of mortality after hepatectomy. Preoperative prediction of residual liver function has been limited so far. Sixty-four patients undergoing hepatectomy were analyzed in a prospective observational study. Volumetric analysis of the liver was carried out using preoperative computed tomography and intraoperative measurements. Perioperative factors associated with morbidity and mortality were analyzed. Cutoff values of the LiMAx test were evaluated by receiver operating characteristic. Residual LiMAx demonstrated an excellent linear correlation with residual liver volume (r = 0.94, P < 0.001) after hepatectomy. The multivariate analysis revealed LiMAx on postoperative day 1 as the only predictor of liver failure (P = 0.003) and mortality (P = 0.004). AUROC for the prediction of liver failure and liver failure related death by the LiMAx test was both 0.99. Preoperative volume/function analysis combining CT volumetry and LiMAx allowed an accurate calculation of the remnant liver function capacity prior to surgery (r = 0.85, P < 0.001). Residual liver function is the major factor influencing the outcome of patients after hepatectomy and can be predicted preoperatively by a combination of LiMAx and CT volumetry.

Extensions of Island Biogeography Theory predict the scaling of functional trait composition with habitat area and isolation.

PubMed

Jacquet, Claire; Mouillot, David; Kulbicki, Michel; Gravel, Dominique

2017-02-01

The Theory of Island Biogeography (TIB) predicts how area and isolation influence species richness equilibrium on insular habitats. However, the TIB remains silent about functional trait composition and provides no information on the scaling of functional diversity with area, an observation that is now documented in many systems. To fill this gap, we develop a probabilistic approach to predict the distribution of a trait as a function of habitat area and isolation, extending the TIB beyond the traditional species-area relationship. We compare model predictions to the body-size distribution of piscivorous and herbivorous fishes found on tropical reefs worldwide. We find that small and isolated reefs have a higher proportion of large-sized species than large and connected reefs. We also find that knowledge of species body-size and trophic position improves the predictions of fish occupancy on tropical reefs, supporting both the allometric and trophic theory of island biogeography. The integration of functional ecology to island biogeography is broadly applicable to any functional traits and provides a general probabilistic approach to study the scaling of trait distribution with habitat area and isolation. © 2016 John Wiley & Sons Ltd/CNRS.

Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.

PubMed

Maciejewski, Matthew L; Liu, Chuan-Fen; Fihn, Stephan D

2009-01-01

To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.

Predicting vapor liquid equilibria using density functional theory: A case study of argon

NASA Astrophysics Data System (ADS)

Goel, Himanshu; Ling, Sanliang; Ellis, Breanna Nicole; Taconi, Anna; Slater, Ben; Rai, Neeraj

2018-06-01

Predicting vapor liquid equilibria (VLE) of molecules governed by weak van der Waals (vdW) interactions using the first principles approach is a significant challenge. Due to the poor scaling of the post Hartree-Fock wave function theory with system size/basis functions, the Kohn-Sham density functional theory (DFT) is preferred for systems with a large number of molecules. However, traditional DFT cannot adequately account for medium to long range correlations which are necessary for modeling vdW interactions. Recent developments in DFT such as dispersion corrected models and nonlocal van der Waals functionals have attempted to address this weakness with a varying degree of success. In this work, we predict the VLE of argon and assess the performance of several density functionals and the second order Møller-Plesset perturbation theory (MP2) by determining critical and structural properties via first principles Monte Carlo simulations. PBE-D3, BLYP-D3, and rVV10 functionals were used to compute vapor liquid coexistence curves, while PBE0-D3, M06-2X-D3, and MP2 were used for computing liquid density at a single state point. The performance of the PBE-D3 functional for VLE is superior to other functionals (BLYP-D3 and rVV10). At T = 85 K and P = 1 bar, MP2 performs well for the density and structural features of the first solvation shell in the liquid phase.

Academic Achievement and School Functioning among Nonincarcerated Youth Involved with the Juvenile Justice System

ERIC Educational Resources Information Center

Brown, Jonathan D.; Riley, Anne W.; Walrath, Christine M.; Leaf, Philip J.; Valdez, Carmen

2008-01-01

The relationship between academic problems and delinquency is well documented among incarcerated populations but has not been examined among nonincarcerated youth involved with the juvenile justice system. This research examined the school functioning and academic achievement of 157 youth who had brief contact with a state department of juvenile…

How Homes Influence Schools: Early Parenting Predicts African American Children's Classroom Social-Emotional Functioning

ERIC Educational Resources Information Center

Baker, Claire E.; Rimm-Kaufman, Sara E.

2014-01-01

Data from the Early Childhood Longitudinal Study, Kindergarten Cohort were used to examine the extent to which early parenting predicted African American children's kindergarten social-emotional functioning. Teachers rated children's classroom social-emotional functioning in four areas (i.e., approaches to learning, self-control, interpersonal…

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

NASA Astrophysics Data System (ADS)

Roos, Katarina; Hogner, Anders; Ogg, Derek; Packer, Martin J.; Hansson, Eva; Granberg, Kenneth L.; Evertsson, Emma; Nordqvist, Anneli

2015-12-01

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R2 = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R2 = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function

PubMed Central

Tian, Weidong; Zhang, Lan V; Taşan, Murat; Gibbons, Francis D; King, Oliver D; Park, Julie; Wunderlich, Zeba; Cherry, J Michael; Roth, Frederick P

2008-01-01

Background: Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between function and other gene characteristics; and 'guilt-by-association', which transfers function from one gene to another via biological relationships. Results: We have developed a strategy ('Funckenstein') that performs guilt-by-profiling and guilt-by-association and combines the results. Using a benchmark set of functional categories and input data for protein-coding genes in Saccharomyces cerevisiae, Funckenstein was compared with a previous combined strategy. Subsequently, we applied Funckenstein to 2,455 Gene Ontology terms. In the process, we developed 2,455 guilt-by-profiling classifiers based on 8,848 gene characteristics and 12 functional linkage graphs based on 23 biological relationships. Conclusion: Funckenstein outperforms a previous combined strategy using a common benchmark dataset. The combination of 'guilt-by-profiling' and 'guilt-by-association' gave significant improvement over the component classifiers, showing the greatest synergy for the most specific functions. Performance was evaluated by cross-validation and by literature examination of the top-scoring novel predictions. These quantitative predictions should help prioritize experimental study of yeast gene functions. PMID:18613951

Nuclear pore proteins are involved in the biogenesis of functional tRNA.

PubMed

Simos, G; Tekotte, H; Grosjean, H; Segref, A; Sharma, K; Tollervey, D; Hurt, E C

1996-05-01

Los1p and Pus1p, which are involved in tRNA biogenesis, were found in a genetic screen for components interacting with the nuclear pore protein Nsp1p. LOS1, PUS1 and NSP1 interact functionally, since the combination of mutations in the three genes causes synthetic lethality. Pus1p is an intranuclear protein which exhibits a nucleotide-specific and intron-dependent tRNA pseudouridine synthase activity. Los1p was shown previously to be required for efficient pre-tRNA splicing; we report here that Los1p localizes to the nuclear pores and is linked functionally to several components of the tRNA biogenesis machinery including Pus1p and Tfc4p. When the formation of functional tRNA was analyzed by an in vivo assay, the los1(-) pus1(-) double mutant, as well as several thermosensitive nucleoporin mutants including nsp1, nup116, nup133 and nup85, exhibited loss of suppressor tRNA activity even at permissive temperatures. These data suggest that nuclear pore proteins are required for the biogenesis of functional tRNA.

Nuclear pore proteins are involved in the biogenesis of functional tRNA.

PubMed Central

Simos, G; Tekotte, H; Grosjean, H; Segref, A; Sharma, K; Tollervey, D; Hurt, E C

1996-01-01

Los1p and Pus1p, which are involved in tRNA biogenesis, were found in a genetic screen for components interacting with the nuclear pore protein Nsp1p. LOS1, PUS1 and NSP1 interact functionally, since the combination of mutations in the three genes causes synthetic lethality. Pus1p is an intranuclear protein which exhibits a nucleotide-specific and intron-dependent tRNA pseudouridine synthase activity. Los1p was shown previously to be required for efficient pre-tRNA splicing; we report here that Los1p localizes to the nuclear pores and is linked functionally to several components of the tRNA biogenesis machinery including Pus1p and Tfc4p. When the formation of functional tRNA was analyzed by an in vivo assay, the los1(-) pus1(-) double mutant, as well as several thermosensitive nucleoporin mutants including nsp1, nup116, nup133 and nup85, exhibited loss of suppressor tRNA activity even at permissive temperatures. These data suggest that nuclear pore proteins are required for the biogenesis of functional tRNA. Images PMID:8641292

Parents' Involvement in Children's Learning in the United States and China: Implications for Children's Academic and Emotional Adjustment

PubMed Central

Cheung, Cecilia Sin-Sze; Pomerantz, Eva M.

2011-01-01

This research examined parents' involvement in children's learning in the United States and China. Beginning in seventh grade, 825 American and Chinese children (mean age = 12.74 years) reported on their parents' involvement in their learning as well as their parents' psychological control and autonomy support every six months until the end of eighth grade. Information on children's academic and emotional adjustment was obtained. American (vs. Chinese) parents' involvement was associated less with their control and more with their autonomy support. Despite these different associations, parents' heightened involvement predicted children's enhanced engagement and achievement similarly in the United States and China. However, it predicted enhanced perceptions of competence and positive emotional functioning more strongly in the United States than China. PMID:21418057

Genetic interaction networks: better understand to better predict

PubMed Central

Boucher, Benjamin; Jenna, Sarah

2013-01-01

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering.

PubMed

Manoharan, Sujatha C; Ramakrishnan, Swaminathan

2009-10-01

In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

The incremental value of self-reported mental health measures in predicting functional outcomes of veterans.

PubMed

Eisen, Susan V; Bottonari, Kathryn A; Glickman, Mark E; Spiro, Avron; Schultz, Mark R; Herz, Lawrence; Rosenheck, Robert; Rofman, Ethan S

2011-04-01

Research on patient-centered care supports use of patient/consumer self-report measures in monitoring health outcomes. This study examined the incremental value of self-report mental health measures relative to a clinician-rated measure in predicting functional outcomes among mental health service recipients. Participants (n = 446) completed the Behavior and Symptom Identification Scale, the Brief Symptom Inventory, and the Veterans/Rand Short Form-36 at enrollment in the study (T1) and 3 months later (T2). Global Assessment of Functioning (GAF) ratings, mental health service utilization, and psychiatric diagnoses were obtained from administrative data files. Controlling for demographic and clinical variables, results indicated that improvement based on the self-report measures significantly predicted one or more functional outcomes (i.e., decreased likelihood of post-enrollment psychiatric hospitalization and increased likelihood of paid employment), above and beyond the predictive value of the GAF. Inclusion of self-report measures may be a useful addition to performance measurement efforts.

The developmental costs and benefits of children's involvement in interparental conflict.

PubMed

Davies, Patrick T; Coe, Jesse L; Martin, Meredith J; Sturge-Apple, Melissa L; Cummings, E Mark

2015-08-01

Building on empirical documentation of children's involvement in interparental conflicts as a weak predictor of psychopathology, we tested the hypothesis that involvement in conflict more consistently serves as a moderator of associations between children's emotional reactivity to interparental conflict and their psychological problems. In Study 1, 263 early adolescents (M age = 12.62 years), mothers, and fathers completed surveys of family and child functioning at 2 measurement occasions spaced 2 years apart. In Study 2, 243 preschool children (M age = 4.60 years) participated in a multimethod (i.e., observations, structured interview, surveys) measurement battery to assess family functioning, children's reactivity to interparental conflict, and their psychological adjustment. Across both studies, latent difference score analyses revealed that involvement moderated associations between emotional reactivity and children's increases in psychological (i.e., internalizing and externalizing) problems. Children's emotional reactivity to interparental conflict was a significantly stronger predictor of their psychological maladjustment when they were highly involved in the conflicts. In addition, the developmental benefits and costs of involvement varied as a function of emotional reactivity. Involvement in interparental conflict predicted increases in psychological problems for children experiencing high emotional reactivity and decreases in psychological problems when they exhibited low emotional reactivity. We interpret the results in the context of the new formulation of emotional security theory (e.g., Davies & Martin, 2013) and family systems models of children's parentification (e.g., Byng-Hall, 2002). (c) 2015 APA, all rights reserved).

Predicting functional remission in patients with schizophrenia: a cross-sectional study of symptomatic remission, psychosocial remission, functioning, and clinical outcome

PubMed Central

Valencia, Marcelo; Fresán, Ana; Barak, Yoram; Juárez, Francisco; Escamilla, Raul; Saracco, Ricardo

2015-01-01

Background New approaches to assess outcome in schizophrenia include multidimensional measures such as remission, cognition, psychosocial functioning, and quality of life. Clinical and psychosocial measures have been recently introduced to assess functional outcome. Objective The study presented here was designed to examine the rates of symptomatic remission, psychosocial remission, global functioning, and clinical global impressions in a sample of schizophrenia outpatients in order to assess functional remission and to identify predictive factors for functional remission. Methods A total of 168 consecutive Mexican outpatients receiving pharmacological treatment at the National Institute of Psychiatry in Mexico City were enrolled in a cross-sectional study. Symptomatic remission was assessed according to the definition and criteria proposed by the Remission in Schizophrenia Working Group using the Positive and Negative Symptom Scale. Psychosocial remission was assessed according to Barak criteria using the Psychosocial Remission in Schizophrenia scale. Functioning was measured with the Global Assessment of Functioning, and clinical outcome with the Clinical Global Impressions (CGI) Scale. Results Findings showed that 45.2% of patients fulfilled the symptomatic remission criteria, 32.1% achieved psychosocial remission, and 53% reported adequate functioning. However, the combination of these three outcome criteria – symptomatic, psychosocial remission, and functioning – indicated that 14.9% of the patients achieved our predefined functional remission outcome. The logistic regression model included five predictive variables for functional remission: (1) being employed, (2) use of atypical antipsychotics, (3) lower number of medications, (4) lower negative symptom severity, and (5) lower excitement symptom severity. Conclusion The study demonstrated that symptomatic remission, psychosocial remission, and functioning could be achievable goals for a considerable

Functional status predicts acute care readmission in the traumatic spinal cord injury population.

PubMed

Huang, Donna; Slocum, Chloe; Silver, Julie K; Morgan, James W; Goldstein, Richard; Zafonte, Ross; Schneider, Jeffrey C

2018-03-29

Context/objective Acute care readmission has been identified as an important marker of healthcare quality. Most previous models assessing risk prediction of readmission incorporate variables for medical comorbidity. We hypothesized that functional status is a more robust predictor of readmission in the spinal cord injury population than medical comorbidities. Design Retrospective cross-sectional analysis. Setting Inpatient rehabilitation facilities, Uniform Data System for Medical Rehabilitation data from 2002 to 2012 Participants traumatic spinal cord injury patients. Outcome measures A logistic regression model for predicting acute care readmission based on demographic variables and functional status (Functional Model) was compared with models incorporating demographics, functional status, and medical comorbidities (Functional-Plus) or models including demographics and medical comorbidities (Demographic-Comorbidity). The primary outcomes were 3- and 30-day readmission, and the primary measure of model performance was the c-statistic. Results There were a total of 68,395 patients with 1,469 (2.15%) readmitted at 3 days and 7,081 (10.35%) readmitted at 30 days. The c-statistics for the Functional Model were 0.703 and 0.654 for 3 and 30 days. The Functional Model outperformed Demographic-Comorbidity models at 3 days (c-statistic difference: 0.066-0.096) and outperformed two of the three Demographic-Comorbidity models at 30 days (c-statistic difference: 0.029-0.056). The Functional-Plus models exhibited negligible improvements (0.002-0.010) in model performance compared to the Functional models. Conclusion Readmissions are used as a marker of hospital performance. Function-based readmission models in the spinal cord injury population outperform models incorporating medical comorbidities. Readmission risk models for this population would benefit from the inclusion of functional status.

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

PubMed Central

Sprint, Gina; Cook, Diane J.; Weeks, Douglas L.; Borisov, Vladimir

2016-01-01

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported. PMID:27054054

Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification.

PubMed

Wang, Xindi; Lin, Qixiang; Xia, Mingrui; He, Yong

2018-04-01

Very little is known regarding whether structural hubs of human brain networks that enable efficient information communication may be classified into different categories. Using three multimodal neuroimaging data sets, we construct individual structural brain networks and further identify hub regions based on eight widely used graph-nodal metrics, followed by comprehensive characteristics and reproducibility analyses. We show the three categories of structural hubs in the brain network, namely, aggregated, distributed, and connector hubs. Spatially, these distinct categories of hubs are primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. These categorized hubs exhibit various distinct characteristics to support their differentiated roles, involving microstructural organization, wiring costs, topological vulnerability, functional modular integration, and cognitive flexibility; moreover, these characteristics are better in the hubs than nonhubs. Finally, all three categories of hubs display high across-session spatial similarities and act as structural fingerprints with high predictive rates (100%, 100%, and 84.2%) for individual identification. Collectively, we highlight three categories of brain hubs with differential microstructural, functional and, cognitive associations, which shed light on topological mechanisms of the human connectome. © 2018 Wiley Periodicals, Inc.

Mother Involvement as an Influence on Father Involvement with Early Adolescents

PubMed Central

Pleck, Joseph H.; Hofferth, Sandra L.

2009-01-01

This study hypothesized that father involvement is influenced by mothers' level of involvement as well as by marital conflict, mothers' work hours, and fathers' status as biological or step father. The analysis also tested hypotheses about mother involvement as a potential mediator of the effects of marital conflict and maternal work hours on father involvement, and hypotheses about factors influencing mother involvement. Children aged 10-14 from the NLSY79 who resided with their biological or step father and with their mother reported on each parent's involvement with them. As hypothesized, father involvement was predicted by mother involvement, and the reciprocal influence was not significant. Father involvement was associated with low marital conflict and being a biological father. Mothers' involvement partially mediated the effects of marital conflict on father involvement. If the mediating role of maternal involvement is not taken into account, the effect of marital conflict on father involvement is overestimated. PMID:21776195

Prediction of lymph node involvement in patients with breast tumors measuring 3-5 cm in a middle-income setting: the role of CancerMath.

PubMed