A Single-blinded, Randomized Clinical Trial of How to Implement an Evidence-based Treatment for Generalized Anxiety Disorder [IMPLEMENT] — Effects of Three Different Strategies of Implementation

BACKGROUND: Despite long-standing calls to disseminate evidence-based treatments for generalized anxiety (GAD), modest progress has been made in the study of how such treatments should be implemented. The primary objective of this study was to test three competing strategies on how to implement a cognitive behavioral treatment (CBT) for out-patients with GAD (i.e., comparison of one compensation vs. two capitalization models). METHODS: For our three-arm, single-blinded, randomized controlled trial (implementation of CBT for GAD [IMPLEMENT]), we recruited adults with GAD using advertisements in high-circulation newspapers to participate in a 14-session cognitive behavioral treatment (Mastery of your Anxiety and Worry, MAW-packet). We randomly assigned eligible patients using a full randomization procedure (1:1:1) to three different conditions of implementation: adherence priming (compensation model), which had a systematized focus on patients' individual GAD symptoms and how to compensate for these symptoms within the MAW-packet, and resource priming and supportive resource priming (capitalization model), which had systematized focuses on patients' strengths and abilities and how these strengths can be capitalized within the same packet. In the intention-to-treat population an outcome composite of primary and secondary symptoms-related self-report questionnaires was analyzed based on a hierarchical linear growth model from intake to 6-month follow-up assessment. This trial is registered at ClinicalTrials.gov (identifier: NCT02039193) and is closed to new participants. FINDINGS: From June 2012 to Nov. 2014, from 411 participants that were screened, 57 eligible participants were recruited and randomly assigned to three conditions. Forty-nine patients (86%) provided outcome data at post-assessment (14% dropout rate). All three conditions showed a highly significant reduction of symptoms over time. However, compared with the adherence priming condition, both resource priming conditions indicated faster symptom reduction. The observer ratings of a sub-sample of recorded videos (n = 100) showed that the therapists in the resource priming conditions conducted more strength-oriented interventions in comparison with the adherence priming condition. No patients died or attempted suicide. INTERPRETATION: To our knowledge, this is the first trial that focuses on capitalization and compensation models during the implementation of one prescriptive treatment packet for GAD. We have shown that GAD related symptoms were significantly faster reduced by the resource priming conditions, although the limitations of our study included a well-educated population. If replicated, our results suggest that therapists who implement a mental health treatment for GAD might profit from a systematized focus on capitalization models. FUNDING: Swiss Science National Foundation (SNSF-Nr. PZ00P1_136937/1) awarded to CF. KEYWORDS: Cognitive behavioral therapy; Evidence-based treatment; Implementation strategies; Randomized controlled trial

Cost-effectiveness analysis of stereotactic body radiotherapy and radiofrequency ablation for medically inoperable liver metastases from colorectal cancer

This report describes the development of a Markov model for comparing percutaneous radiofrequency ablation (RFA) and stereotactic body radiation therapy (SBRT) in terms of their cost-utility in treating isolated liver metastases from colorectal cancer. The model is based on data from multiple retrospective and prospective studies, available data on different utility states associated with treatment and complications, as well as publicly available Medicare costs. The purpose of this report is to establish a well-justified model for clinical management decisions. In comparison with SBRT, RFA is the most cost-effective treatment for this patient population. From the societal perspective, SBRT may be an acceptable alternative with an ICER of $28,673/QALY. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.

Propuesta, implementación y evaluación de la biometría de firma en el aire como sistema de verificación en teléfonos móviles

En esta tesis doctoral se propone una técnica biométrica de verificación en teléfonos móviles consistente en realizar una firma en el aire con la mano que sujeta el teléfono móvil. Los acelerómetros integrados en el dispositivo muestrean las aceleraciones del movimiento de la firma en el aire, generando tres señales temporales que pueden utilizarse para la verificación del usuario. Se proponen varios enfoques para la implementación del sistema de verificación, a partir de los enfoques más utilizados en biometría de firma manuscrita: correspondencia de patrones, con variantes de los algoritmos de Needleman-Wusch (NW) y Dynamic Time Warping (DTW), modelos ocultos de Markov (HMM) y clasificador estadístico basado en Máquinas de Vector Soporte (SVM). Al no existir bases de datos públicas de firmas en el aire y con el fin de evaluar los métodos propuestos en esta tesis doctoral, se han capturado dos con distintas características; una con falsificaciones reales a partir del estudio de las grabaciones de usuarios auténticos y otra con muestras de usuarios obtenidas en diferentes sesiones a lo largo del tiempo. Utilizando estas bases de datos se han evaluado una gran cantidad de algoritmos para implementar un sistema de verificación basado en firma en el aire. Esta evaluación se ha realizado de acuerdo con el estándar ISO/IEC 19795, añadiendo el caso de verificación en mundo abierto no incluido en la norma. Además, se han analizado las características que hacen que una firma sea suficientemente segura. Por otro lado, se ha estudiado la permanencia de las firmas en el aire a lo largo del tiempo, proponiendo distintos métodos de actualización, basados en una adaptación dinámica del patrón, para mejorar su rendimiento. Finalmente, se ha implementado un prototipo de la técnica de firma en el aire para teléfonos Android e iOS. Los resultados de esta tesis doctoral han tenido un gran impacto, generando varias publicaciones en revistas internacionales, congresos y libros. La firma en el aire ha sido nombrada también en varias revistas de divulgación, portales de noticias Web y televisión. Además, se han obtenido varios premios en competiciones de ideas innovadoras y se ha firmado un acuerdo de explotación de la tecnología con una empresa extranjera. ABSTRACT This thesis proposes a biometric verification technique on mobile phones consisting on making a signature in the air with the hand holding a mobile phone. The accelerometers integrated in the device capture the movement accelerations, generating three temporal signals that can be used for verification. This thesis suggests several approaches for implementing the verification system, based on the most widely used approaches in handwritten signature biometrics: template matching, with a lot of variations of the Needleman- Wusch (NW) and Dynamic Time Warping (DTW) algorithms, Hidden Markov Models (HMM) and Supported Vector Machines (SVM). As there are no public databases of in-air signatures and with the aim of assessing the proposed methods, there have been captured two databases; one. with real falsification attempts from the study of recordings captured when genuine users made their signatures in front of a camera, and other, with samples obtained in different sessions over a long period of time. These databases have been used to evaluate a lot of algorithms in order to implement a verification system based on in-air signatures. This evaluation has been conducted according to the standard ISO/IEC 19795, adding the open-set verification scenario not included in the norm. In addition, the characteristics of a secure signature are also investigated, as well as the permanence of in-air signatures over time, proposing several updating strategies to improve its performance. Finally, a prototype of in-air signature has been developed for iOS and Android phones. The results of this thesis have achieved a high impact, publishing several articles in SCI journals, conferences and books. The in-air signature deployed in this thesis has been also referred in numerous media. Additionally, this technique has won several awards in the entrepreneurship field and also an exploitation agreement has been signed with a foreign company.

Identification of a human nuclear receptor defines a new signaling pathway for CYP3A induction

Nuclear receptors regulate metabolic pathways in response to changes in the environment by appropriate alterations in gene expression of key metabolic enzymes. Here, a computational search approach based on iteratively built hidden Markov models of nuclear receptors was used to identify a human nuclear receptor, termed hPAR, that is expressed in liver and intestines. hPAR was found to be efficiently activated by pregnanes and by clinically used drugs including rifampicin, an antibiotic known to selectively induce human but not murine CYP3A expression. The CYP3A drug-metabolizing enzymes are expressed in gut and liver in response to environmental chemicals and clinically used drugs. Interestingly, hPAR is not activated by pregnenolone 16α-carbonitrile, which is a potent inducer of murine CYP3A genes and an activator of the mouse receptor PXR.1. Furthermore, hPAR was found to bind to and trans-activate through a conserved regulatory sequence present in human but not murine CYP3A genes. These results provide evidence that hPAR and PXR.1 may represent orthologous genes from different species that have evolved to regulate overlapping target genes in response to pharmacologically distinct CYP3A activators, and have potential implications for the in vitro identification of drug interactions important to humans.

The InterPro database, an integrated documentation resource for protein families, domains and functional sites

Signature databases are vital tools for identifying distant relationships in novel sequences and hence for inferring protein function. InterPro is an integrated documentation resource for protein families, domains and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. Each InterPro entry includes a functional description, annotation, literature references and links back to the relevant member database(s). Release 2.0 of InterPro (October 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification encoded by a total of 6804 different regular expressions, profiles, fingerprints and Hidden Markov Models. Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1 000 000 hits from 462 500 proteins in SWISS-PROT and TrEMBL). The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to interhelp@ebi.ac.uk.

TIGRFAMs: a protein family resource for the functional identification of proteins

TIGRFAMs is a collection of protein families featuring curated multiple sequence alignments, hidden Markov models and associated information designed to support the automated functional identification of proteins by sequence homology. We introduce the term ‘equivalog’ to describe members of a set of homologous proteins that are conserved with respect to function since their last common ancestor. Related proteins are grouped into equivalog families where possible, and otherwise into protein families with other hierarchically defined homology types. TIGRFAMs currently contains over 800 protein families, available for searching or downloading at www.tigr.org/TIGRFAMs. Classification by equivalog family, where achievable, complements classification by orthology, superfamily, domain or motif. It provides the information best suited for automatic assignment of specific functions to proteins from large-scale genome sequencing projects.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Improved Techniques for Video Compression and Communication

Thesis (Ph.D.)--University of Washington, 2016-06

Cost-effectiveness of combination peginterferon alpha-2a and ribavirin compared with interferon alpha-2b and ribavirin in patients with chronic hepatitis C

BACKGROUND: Sustained virological response (SVR) is the primary objective in the treatment of chronic hepatitis C (CHC). Results from a recent clinical trial of patients with previously untreated CHC demonstrate that the combination of peginterferon alpha-2a and ribavirin produces a greater SVR than interferon alpha-2b and ribavirin combination therapy. However, the cost-effectiveness of peginterferon alpha-2a plus ribavirin in the U.S. setting has not been investigated. METHODS: A Markov model was developed to investigate cost-effectiveness in patients with CHC using genotype to guide treatment duration. SVR and disease progression parameters were derived from the clinical trials and epidemiologic studies. The impact of treatment on life expectancy and costs were projected for a lifetime. Patients who had an SVR were assumed to remain virus-free for the rest of their lives. In genotype 1 patients, the SVRs were 46% for peginterferon alpha-2a plus ribavirin and 36% for interferon alpha-2b plus ribavirin. In genotype 2/3 patients, the SVRs were 76% for peginterferon alpha-2a plus ribavirin and 61% for interferon alpha-2b plus ribavirin. Quality of life and costs were based on estimates from the literature. All costs were based on published U.S. medical care costs and were adjusted to 2003 U.S. dollars. Costs and benefits beyond the first year were discounted at 3%. RESULTS: In genotype 1, peginterferon alpha-2a plus ribavirin increases quality-adjusted life expectancy (QALY) by 0.70 yr compared to interferon alpha-2b plus ribavirin, producing a cost-effectiveness ratio of $2,600 per QALY gained. In genotype 2/3 patients, peginterferon alpha-2a plus ribavirin increases QALY by 1.05 yr in comparison to interferon alpha-2b plus ribavirin. Peginterferon alpha-2a combination therapy in patients with HCV genotype 2 or 3 is dominant (more effective and cost saving) compared to interferon alpha-2b plus ribavirin. Results weighted by genotype prevalence (75% genotype 1; 25% genotype 2 or 3) also show that peginterferon alpha-2a plus ribavirin is dominant. Peginterferon alpha-2a and ribavirin remained cost-effective (below $16,500 per QALY gained) under sensitivity analyses on key clinical and cost parameters. CONCLUSION: Peginterferon alpha-2a in combination with ribavirin with duration of therapy based on genotype, is cost-effective compared with conventional interferon alpha-2b in combination with ribavirin when given to treatment-naive adults with CHC.

The effects of habitat fragmentation due to forestry plantation establishment on the demography and genetic variation of a marsupial carnivore, Antechinus agilis

We conducted a demographic and genetic study to investigate the effects of fragmentation due to the establishment of an exotic softwood plantation on populations of a small marsupial carnivore, the agile antechinus (Antechinus agilis), and the factors influencing the persistence of those populations in the fragmented habitat. The first aspect of the study was a descriptive analysis of patch occupancy and population size, in which we found a patch occupancy rate of 70% among 23 sites in the fragmented habitat compared to 100% among 48 sites with the same habitat characteristics in unfragmented habitat. Mark-recapture analyses yielded most-likely population size estimates of between 3 and 85 among the 16 occupied patches in the fragmented habitat. Hierarchical partitioning and model selection were used to identify geographic and habitat-related characteristics that influence patch occupancy and population size. Patch occupancy was primarily influenced by geographic isolation and habitat quality (vegetation basal area). The variance in population size among occupied sites was influenced primarily by forest type (dominant Eucalyptus species) and, to a lesser extent, by patch area and topographic context (gully sites had larger populations). A comparison of the sex ratios between the samples from the two habitat contexts revealed a significant deficiency of males in the fragmented habitat. We hypothesise that this is due to male-biased dispersal in an environment with increased dispersal-associated mortality. The population size and sex ratio data were incorporated into a simulation study to estimate the proportion of genetic diversity that would have been lost over the known timescale since fragmentation if the patch populations had been totally isolated. The observed difference in genetic diversity (gene diversity and allelic richness at microsatellite and mitochondrial markers) between 16 fragmented and 12 unfragmented sites was extremely low and inconsistent with the isolation of the patch populations. Our results show that although the remnant habitat patches comprise approximately 2% of the study area, they can support non-isolated populations. However, the distribution of agile antechinus populations in the fragmented system is dependent on habitat quality and patch connectivity. (C) 2004 Elsevier Ltd. All rights reserved.

A cost-effectiveness analysis of fluvastatin in patients with diabetes after successful percutaneous coronary intervention

Background: The Lescol Intervention Prevention Study (LIPS) was a multinational randomized controlled trial that showed a 47% reduction in the relative risk of cardiac death and a 22% reduction in major adverse cardiac events (MACEs) from the routine use of fluvastatin, compared with controls, in patients undergoing percutaneous coronary intervention (PCI, defined as angioplasty with or without stents). In this study, MACEs included cardiac death, nonfatal myocardial infarction, and subsequent PCI and coronary artery bypass graft. Diabetes was the greatest risk factor for MACEs. Objective: This study estimated the cost-effectiveness of fluvastatin when used for secondary prevention of MACEs after PCI in people with diabetes. Methods: A post hoc subgroup analysis of patients with diabetes from the LIPS was used to estimate the effectiveness of fluvastatin in reducing myocardial infarction, revascularization, and cardiac death. A probabilistic Markov model was developed using United Kingdom resource and cost data to estimate the additional costs and quality-adjusted life-years (QALYs) gained over 10 years from the perspective of the British National Health Service. The model contained 6 health states, and the transition probabilities were derived from the LIPS data. Crossover from fluvastatin to other lipid-lowering drugs, withdrawal from fluvastatin, and the use of lipid-lowering drugs in the control group were included. Results: In the subgroup of 202 patients with diabetes in the LIPS trial, 18 (15.0%) of 120 fluvastatin patients and 21 (25.6%) of 82 control participants were insulin dependent (P = NS). Compared with the control group, patients treated with fluvastatin can expect to gain an additional mean (SD) of 0.196 (0.139) QALY per patient over 10 years (P < 0.001) and will cost the health service an additional mean (SD) of 10 (E448) (P = NS) (mean [SD] US $16 [$689]). The additional cost per QALY gained was;(51 (US $78). The key determinants of cost-effectiveness included the probabilities of repeat interventions, cardiac death, the cost of fluvastatin, and the time horizon used for the evaluation. Conclusion: Fluvastatin was an economically efficient treatment to prevent MACEs in these patients with diabetes undergoing PCI.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.