Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation

PubMed Central

Pujar, Shashikant; O’Leary, Nuala A; Farrell, Catherine M; Mudge, Jonathan M; Wallin, Craig; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bult, Carol J; Frankish, Adam; Pruitt, Kim D

2018-01-01

Abstract The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. PMID:29126148

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

PubMed

Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

2005-08-15

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

PubMed

Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

2016-01-15

Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

Genetic Contributors to Intergenerational CAG Repeat Instability in Huntington’s Disease Knock-In Mice

PubMed Central

Neto, João Luís; Lee, Jong-Min; Afridi, Ali; Gillis, Tammy; Guide, Jolene R.; Dempsey, Stephani; Lager, Brenda; Alonso, Isabel; Wheeler, Vanessa C.; Pinto, Ricardo Mouro

2017-01-01

Huntington’s disease (HD) is a neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in exon 1 of the HTT gene. Longer repeat sizes are associated with increased disease penetrance and earlier ages of onset. Intergenerationally unstable transmissions are common in HD families, partly underlying the genetic anticipation seen in this disorder. HD CAG knock-in mouse models also exhibit a propensity for intergenerational repeat size changes. In this work, we examine intergenerational instability of the CAG repeat in over 20,000 transmissions in the largest HD knock-in mouse model breeding datasets reported to date. We confirmed previous observations that parental sex drives the relative ratio of expansions and contractions. The large datasets further allowed us to distinguish effects of paternal CAG repeat length on the magnitude and frequency of expansions and contractions, as well as the identification of large repeat size jumps in the knock-in models. Distinct degrees of intergenerational instability were observed between knock-in mice of six background strains, indicating the occurrence of trans-acting genetic modifiers. We also found that lines harboring a neomycin resistance cassette upstream of Htt showed reduced expansion frequency, indicative of a contributing role for sequences in cis, with the expanded repeat as modifiers of intergenerational instability. These results provide a basis for further understanding of the mechanisms underlying intergenerational repeat instability. PMID:27913616

Genetic Contributors to Intergenerational CAG Repeat Instability in Huntington's Disease Knock-In Mice.

PubMed

Neto, João Luís; Lee, Jong-Min; Afridi, Ali; Gillis, Tammy; Guide, Jolene R; Dempsey, Stephani; Lager, Brenda; Alonso, Isabel; Wheeler, Vanessa C; Pinto, Ricardo Mouro

2017-02-01

Huntington's disease (HD) is a neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in exon 1 of the HTT gene. Longer repeat sizes are associated with increased disease penetrance and earlier ages of onset. Intergenerationally unstable transmissions are common in HD families, partly underlying the genetic anticipation seen in this disorder. HD CAG knock-in mouse models also exhibit a propensity for intergenerational repeat size changes. In this work, we examine intergenerational instability of the CAG repeat in over 20,000 transmissions in the largest HD knock-in mouse model breeding datasets reported to date. We confirmed previous observations that parental sex drives the relative ratio of expansions and contractions. The large datasets further allowed us to distinguish effects of paternal CAG repeat length on the magnitude and frequency of expansions and contractions, as well as the identification of large repeat size jumps in the knock-in models. Distinct degrees of intergenerational instability were observed between knock-in mice of six background strains, indicating the occurrence of trans-acting genetic modifiers. We also found that lines harboring a neomycin resistance cassette upstream of Htt showed reduced expansion frequency, indicative of a contributing role for sequences in cis, with the expanded repeat as modifiers of intergenerational instability. These results provide a basis for further understanding of the mechanisms underlying intergenerational repeat instability. Copyright © 2017 by the Genetics Society of America.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

PubMed Central

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-01-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist. PMID:29651416

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

NASA Astrophysics Data System (ADS)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-03-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation.

PubMed

Pujar, Shashikant; O'Leary, Nuala A; Farrell, Catherine M; Loveland, Jane E; Mudge, Jonathan M; Wallin, Craig; Girón, Carlos G; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; Martin, Fergal J; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Suner, Marie-Marthe; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bruford, Elspeth A; Bult, Carol J; Frankish, Adam; Murphy, Terence; Pruitt, Kim D

2018-01-04

The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. Published by Oxford University Press on behalf of Nucleic Acids Research 2017.

SCPortalen: human and mouse single-cell centric database

PubMed Central

Noguchi, Shuhei; Böttcher, Michael; Hasegawa, Akira; Kouno, Tsukasa; Kato, Sachi; Tada, Yuhki; Ura, Hiroki; Abe, Kuniya; Shin, Jay W; Plessy, Charles; Carninci, Piero

2018-01-01

Abstract Published single-cell datasets are rich resources for investigators who want to address questions not originally asked by the creators of the datasets. The single-cell datasets might be obtained by different protocols and diverse analysis strategies. The main challenge in utilizing such single-cell data is how we can make the various large-scale datasets to be comparable and reusable in a different context. To challenge this issue, we developed the single-cell centric database ‘SCPortalen’ (http://single-cell.clst.riken.jp/). The current version of the database covers human and mouse single-cell transcriptomics datasets that are publicly available from the INSDC sites. The original metadata was manually curated and single-cell samples were annotated with standard ontology terms. Following that, common quality assessment procedures were conducted to check the quality of the raw sequence. Furthermore, primary data processing of the raw data followed by advanced analyses and interpretation have been performed from scratch using our pipeline. In addition to the transcriptomics data, SCPortalen provides access to single-cell image files whenever available. The target users of SCPortalen are all researchers interested in specific cell types or population heterogeneity. Through the web interface of SCPortalen users are easily able to search, explore and download the single-cell datasets of their interests. PMID:29045713

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

The Cambridge MRI database for animal models of Huntington disease.

PubMed

Sawiak, Stephen J; Morton, A Jennifer

2016-01-01

We describe the Cambridge animal brain magnetic resonance imaging repository comprising 400 datasets to date from mouse models of Huntington disease. The data include raw images as well as segmented grey and white matter images with maps of cortical thickness. All images and phenotypic data for each subject are freely-available without restriction from (http://www.dspace.cam.ac.uk/handle/1810/243361/). Software and anatomical population templates optimised for animal brain analysis with MRI are also available from this site. Copyright © 2015. Published by Elsevier Inc.

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

PubMed

Bhhatarai, Barun; Gramatica, Paola

2011-05-01

Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.

Modeling hormonal and inflammatory contributions to preterm and term labor using uterine temporal transcriptomics.

PubMed

Migale, Roberta; MacIntyre, David A; Cacciatore, Stefano; Lee, Yun S; Hagberg, Henrik; Herbert, Bronwen R; Johnson, Mark R; Peebles, Donald; Waddington, Simon N; Bennett, Phillip R

2016-06-13

Preterm birth is now recognized as the primary cause of infant mortality worldwide. Interplay between hormonal and inflammatory signaling in the uterus modulates the onset of contractions; however, the relative contribution of each remains unclear. In this study we aimed to characterize temporal transcriptome changes in the uterus preceding term labor and preterm labor (PTL) induced by progesterone withdrawal or inflammation in the mouse and compare these findings with human data. Myometrium was collected at multiple time points during gestation and labor from three murine models of parturition: (1) term gestation; (2) PTL induced by RU486; and (3) PTL induced by lipopolysaccharide (LPS). RNA was extracted and cDNA libraries were prepared and sequenced using the Illumina HiSeq 2000 system. Resulting RNA-Seq data were analyzed using multivariate modeling approaches as well as pathway and causal network analyses and compared against human myometrial transcriptome data. We identified a core set of temporal myometrial gene changes associated with term labor and PTL in the mouse induced by either inflammation or progesterone withdrawal. Progesterone withdrawal initiated labor without inflammatory gene activation, yet LPS activation of uterine inflammation was sufficient to override the repressive effects of progesterone and induce a laboring phenotype. Comparison of human and mouse uterine transcriptomic datasets revealed that human labor more closely resembles inflammation-induced PTL in the mouse. Labor in the mouse can be achieved through inflammatory gene activation yet these changes are not a requisite for labor itself. Human labor more closely resembles LPS-induced PTL in the mouse, supporting an essential role for inflammatory mediators in human "functional progesterone withdrawal." This improved understanding of inflammatory and progesterone influence on the uterine transcriptome has important implications for the development of PTL prevention strategies.

Role of NF-Kappa B Signaling in X-Box Binding Protein 1 (XBP1)-Mediated Antiestrogen Resistance in Breast Cancer

DTIC Science & Technology

2013-10-01

experiments, a statistically significant data is not yet available. Additional experiments are needed for us to be able to draw   conclu comb Figure...well-defined stress pathways, UPR and autophagy, are involved breast involution regulation. Using published gene expression array datasets from...performed involution time-course experiments using both low-dose drug interventions and an autophagy-related gene 7 (ATG7) deletion mouse model

Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

PubMed

Chen, Zheng; Soutto, Mohammed; Rahman, Bushra; Fazili, Muhammad W; Peng, DunFa; Blanca Piazuelo, Maria; Chen, Heidi; Kay Washington, M; Shyr, Yu; El-Rifai, Wael

2017-07-01

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

Genomic profiling of a Hepatocyte growth factor-dependent signature for MET-targeted therapy in glioblastoma.

PubMed

Johnson, Jennifer; Ascierto, Maria Libera; Mittal, Sandeep; Newsome, David; Kang, Liang; Briggs, Michael; Tanner, Kirk; Marincola, Francesco M; Berens, Michael E; Vande Woude, George F; Xie, Qian

2015-09-17

Constitutive MET signaling promotes invasiveness in most primary and recurrent GBM. However, deployment of available MET-targeting agents is confounded by lack of effective biomarkers for selecting suitable patients for treatment. Because endogenous HGF overexpression often causes autocrine MET activation, and also indicates sensitivity to MET inhibitors, we investigated whether it drives the expression of distinct genes which could serve as a signature indicating vulnerability to MET-targeted therapy in GBM. Interrogation of genomic data from TCGA GBM (Student's t test, GBM patients with high and low HGF expression, p ≤ 0.00001) referenced against patient-derived xenograft (PDX) models (Student's t test, sensitive vs. insensitive models, p ≤ 0.005) was used to identify the HGF-dependent signature. Genomic analysis of GBM xenograft models using both human and mouse gene expression microarrays (Student's t test, treated vs. vehicle tumors, p ≤ 0.01) were performed to elucidate the tumor and microenvironment cross talk. A PDX model with EGFR(amp) was tested for MET activation as a mechanism of erlotinib resistance. We identified a group of 20 genes highly associated with HGF overexpression in GBM and were up- or down-regulated only in tumors sensitive to MET inhibitor. The MET inhibitors regulate tumor (human) and host (mouse) cells within the tumor via distinct molecular processes, but overall impede tumor growth by inhibiting cell cycle progression. EGFR (amp) tumors undergo erlotinib resistance responded to a combination of MET and EGFR inhibitors. Combining TCGA primary tumor datasets (human) and xenograft tumor model datasets (human tumor grown in mice) using therapeutic efficacy as an endpoint may serve as a useful approach to discover and develop molecular signatures as therapeutic biomarkers for targeted therapy. The HGF dependent signature may serve as a candidate predictive signature for patient enrollment in clinical trials using MET inhibitors. Human and mouse microarrays maybe used to dissect the tumor-host interactions. Targeting MET in EGFR (amp) GBM may delay the acquired resistance developed during treatment with erlotinib.

GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.

PubMed

Jiang, Lan; Chen, Huidong; Pinello, Luca; Yuan, Guo-Cheng

2016-07-01

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population.

Live dynamic analysis of the developing cardiovascular system in mice

NASA Astrophysics Data System (ADS)

Lopez, Andrew L.; Wang, Shang; Larin, Kirill V.; Larina, Irina V.

2017-02-01

The study of the developing cardiovascular system in mice is important for understanding human cardiogenesis and congenital heart defects. Our research focuses on imaging early development in the mouse embryo to specifically understand cardiovascular development under the regulation of dynamic factors like contractile force and blood flow using optical coherence tomography (OCT). We have previously developed an OCT based approach that combines static embryo culture and advanced image processing with computational modeling to live-image mouse embryos and obtain 4D (3D+time) cardiodynamic datasets. Here we present live 4D dynamic blood flow imaging of the early embryonic mouse heart in correlation with heart wall movement. We are using this approach to understand how specific mutations impact heart wall dynamics, and how this influences flow patterns and cardiogenesis. We perform studies in mutant embryos with cardiac phenotypes such as myosin regulatory light chain 2, atrial isoform (Mlc2a). This work is brings us closer to understanding the connections between dynamic mechanical factors and gene programs responsible for early cardiovascular development.

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research.

PubMed

Weidner, Christopher; Steinfath, Matthias; Wistorf, Elisa; Oelgeschläger, Michael; Schneider, Marlon R; Schönfelder, Gilbert

2017-08-16

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

Reduced changes in protein compared to mRNA levels across non-proliferating tissues.

PubMed

Perl, Kobi; Ushakov, Kathy; Pozniak, Yair; Yizhar-Barnea, Ofer; Bhonker, Yoni; Shivatzki, Shaked; Geiger, Tamar; Avraham, Karen B; Shamir, Ron

2017-04-18

The quantitative relations between RNA and protein are fundamental to biology and are still not fully understood. Across taxa, it was demonstrated that the protein-to-mRNA ratio in steady state varies in a direction that lessens the change in protein levels as a result of changes in the transcript abundance. Evidence for this behavior in tissues is sparse. We tested this phenomenon in new data that we produced for the mouse auditory system, and in previously published tissue datasets. A joint analysis of the transcriptome and proteome was performed across four datasets: inner-ear mouse tissues, mouse organ tissues, lymphoblastoid primate samples and human cancer cell lines. We show that the protein levels are more conserved than the mRNA levels in all datasets, and that changes in transcription are associated with translational changes that exert opposite effects on the final protein level, in all tissues except cancer. Finally, we observe that some functions are enriched in the inner ear on the mRNA level but not in protein. We suggest that partial buffering between transcription and translation ensures that proteins can be made rapidly in response to a stimulus. Accounting for the buffering can improve the prediction of protein levels from mRNA levels.

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO.

PubMed

Ni, Ming; Ye, Fuqiang; Zhu, Juanjuan; Li, Zongwei; Yang, Shuai; Yang, Bite; Han, Lu; Wu, Yongge; Chen, Ying; Li, Fei; Wang, Shengqi; Bo, Xiaochen

2014-12-01

Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved. ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest. The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Geometry Processing of Conventionally Produced Mouse Brain Slice Images.

PubMed

Agarwal, Nitin; Xu, Xiangmin; Gopi, M

2018-04-21

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data. Copyright © 2018 Elsevier B.V. All rights reserved.

Modifier locus mapping of a transgenic F2 mouse population identifies CCDC115 as a novel aggressive prostate cancer modifier gene in humans.

PubMed

Winter, Jean M; Curry, Natasha L; Gildea, Derek M; Williams, Kendra A; Lee, Minnkyong; Hu, Ying; Crawford, Nigel P S

2018-06-11

It is well known that development of prostate cancer (PC) can be attributed to somatic mutations of the genome, acquired within proto-oncogenes or tumor-suppressor genes. What is less well understood is how germline variation contributes to disease aggressiveness in PC patients. To map germline modifiers of aggressive neuroendocrine PC, we generated a genetically diverse F2 intercross population using the transgenic TRAMP mouse model and the wild-derived WSB/EiJ (WSB) strain. The relevance of germline modifiers of aggressive PC identified in these mice was extensively correlated in human PC datasets and functionally validated in cell lines. Aggressive PC traits were quantified in a population of 30 week old (TRAMP x WSB) F2 mice (n = 307). Correlation of germline genotype with aggressive disease phenotype revealed seven modifier loci that were significantly associated with aggressive disease. RNA-seq were analyzed using cis-eQTL and trait correlation analyses to identify candidate genes within each of these loci. Analysis of 92 (TRAMP x WSB) F2 prostates revealed 25 candidate genes that harbored both a significant cis-eQTL and mRNA expression correlations with an aggressive PC trait. We further delineated these candidate genes based on their clinical relevance, by interrogating human PC GWAS and PC tumor gene expression datasets. We identified four genes (CCDC115, DNAJC10, RNF149, and STYXL1), which encompassed all of the following characteristics: 1) one or more germline variants associated with aggressive PC traits; 2) differential mRNA levels associated with aggressive PC traits; and 3) differential mRNA expression between normal and tumor tissue. Functional validation studies of these four genes using the human LNCaP prostate adenocarcinoma cell line revealed ectopic overexpression of CCDC115 can significantly impede cell growth in vitro and tumor growth in vivo. Furthermore, CCDC115 human prostate tumor expression was associated with better survival outcomes. We have demonstrated how modifier locus mapping in mouse models of PC, coupled with in silico analyses of human PC datasets, can reveal novel germline modifier genes of aggressive PC. We have also characterized CCDC115 as being associated with less aggressive PC in humans, placing it as a potential prognostic marker of aggressive PC.

The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes.

PubMed

Moss, Gary P; Sun, Yi; Wilkinson, Simon C; Davey, Neil; Adams, Rod; Martin, Gary P; Prapopopolou, M; Brown, Marc B

2011-11-01

Predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability (as K(p)) has been shown to be inherently non-linear when mathematically related to the physicochemical parameters of penetrants. As such, the aims of this study were to apply and evaluate Gaussian process (GP) regression methods to datasets for membranes other than human skin, and to explore how the nature of the dataset may influence its analysis. Permeability data for absorption across rodent and pig skin, and artificial membranes (polydimethylsiloxane, PDMS, i.e. Silastic) membranes was collected from the literature. Two quantitative structure-permeability relationship (QSPR) models were used to compare with the GP models. Further performance metrics were computed in terms of all predictions, and a range of covariance functions were examined: the squared exponential (SE), neural network (NNone) and rational quadratic (QR) covariance functions, along with two simple cases of Matern covariance function (Matern3 and Matern5) where the polynomial order is set to 1 and 2, respectively. As measures of performance, the correlation coefficient (CORR), negative log estimated predictive density (NLL, or negative log loss) and mean squared error (MSE) were employed. The results demonstrated that GP models with different covariance functions outperform QSPR models for human, pig and rodent datasets. For the artificial membranes, GPs perform better in one instance, and give similar results in other experiments (where different covariance parameters produce similar results). In some cases, the GP predictions for some of the artificial membrane dataset are poorly correlated, suggesting that the physicochemical parameters employed in this study might not be appropriate for developing models that represent this membrane. While the results of this study indicate that permeation across rodent (mouse and rat) and pig skin is, in a statistical sense, similar, and that the artificial membranes are poor replacements of human or animal skin, the overriding issue raised in this study is the nature of the dataset and how it can influence the results, and subsequent interpretation, of any model produced for particular membranes. The size of the datasets, in both absolute and comparative senses, appears to influence model quality. Ideally, to generate viable cross-comparisons the datasets for different mammalian membranes should, wherever possible, exhibit as much commonality as possible. © 2011 The Authors. JPP © 2011 Royal Pharmaceutical Society.

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

An Integrated Human/Murine Transcriptome and Pathway Approach To Identify Prenatal Treatments For Down Syndrome.

PubMed

Guedj, Faycal; Pennings, Jeroen LA; Massingham, Lauren J; Wick, Heather C; Siegel, Ashley E; Tantravahi, Umadevi; Bianchi, Diana W

2016-09-02

Anatomical and functional brain abnormalities begin during fetal life in Down syndrome (DS). We hypothesize that novel prenatal treatments can be identified by targeting signaling pathways that are consistently perturbed in cell types/tissues obtained from human fetuses with DS and mouse embryos. We analyzed transcriptome data from fetuses with trisomy 21, age and sex-matched euploid controls, and embryonic day 15.5 forebrains from Ts1Cje, Ts65Dn, and Dp16 mice. The new datasets were compared to other publicly available datasets from humans with DS. We used the human Connectivity Map (CMap) database and created a murine adaptation to identify FDA-approved drugs that can rescue affected pathways. USP16 and TTC3 were dysregulated in all affected human cells and two mouse models. DS-associated pathway abnormalities were either the result of gene dosage specific effects or the consequence of a global cell stress response with activation of compensatory mechanisms. CMap analyses identified 56 molecules with high predictive scores to rescue abnormal gene expression in both species. Our novel integrated human/murine systems biology approach identified commonly dysregulated genes and pathways. This can help to prioritize therapeutic molecules on which to further test safety and efficacy. Additional studies in human cells are ongoing prior to pre-clinical prenatal treatment in mice.

Segmentation and Visual Analysis of Whole-Body Mouse Skeleton microSPECT

PubMed Central

Khmelinskii, Artem; Groen, Harald C.; Baiker, Martin; de Jong, Marion; Lelieveldt, Boudewijn P. F.

2012-01-01

Whole-body SPECT small animal imaging is used to study cancer, and plays an important role in the development of new drugs. Comparing and exploring whole-body datasets can be a difficult and time-consuming task due to the inherent heterogeneity of the data (high volume/throughput, multi-modality, postural and positioning variability). The goal of this study was to provide a method to align and compare side-by-side multiple whole-body skeleton SPECT datasets in a common reference, thus eliminating acquisition variability that exists between the subjects in cross-sectional and multi-modal studies. Six whole-body SPECT/CT datasets of BALB/c mice injected with bone targeting tracers 99mTc-methylene diphosphonate (99mTc-MDP) and 99mTc-hydroxymethane diphosphonate (99mTc-HDP) were used to evaluate the proposed method. An articulated version of the MOBY whole-body mouse atlas was used as a common reference. Its individual bones were registered one-by-one to the skeleton extracted from the acquired SPECT data following an anatomical hierarchical tree. Sequential registration was used while constraining the local degrees of freedom (DoFs) of each bone in accordance to the type of joint and its range of motion. The Articulated Planar Reformation (APR) algorithm was applied to the segmented data for side-by-side change visualization and comparison of data. To quantitatively evaluate the proposed algorithm, bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances between each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance decreased from 11.5±12.1 to 2.6±2.1 voxels. The proposed approach yielded satisfactory segmentation results with minimal user intervention. It proved to be robust for “incomplete” data (large chunks of skeleton missing) and for an intuitive exploration and comparison of multi-modal SPECT/CT cross-sectional mouse data. PMID:23152834

Convection-enhanced delivery of etoposide is effective against murine proneural glioblastoma.

PubMed

Sonabend, Adam M; Carminucci, Arthur S; Amendolara, Benjamin; Bansal, Mukesh; Leung, Richard; Lei, Liang; Realubit, Ronald; Li, Hai; Karan, Charles; Yun, Jonathan; Showers, Christopher; Rothcock, Robert; O, Jane; Califano, Andrea; Canoll, Peter; Bruce, Jeffrey N

2014-09-01

Glioblastoma subtypes have been defined based on transcriptional profiling, yet personalized care based on molecular classification remains unexploited. Topoisomerase II (TOP2) contributes to the transcriptional signature of the proneural glioma subtype. Thus, we targeted TOP2 pharmacologically with etoposide in proneural glioma models. TOP2 gene expression was evaluated in mouse platelet derived growth factor (PDGF)(+)phosphatase and tensin homolog (PTEN)(-/-)p53(-/-) and PDGF(+)PTEN(-/-) proneural gliomas and cell lines, as well as human glioblastoma from The Cancer Genome Atlas. Correlation between TOP2 transcript levels and etoposide susceptibility was investigated in 139 human cancer cell lines from the Cancer Cell Line Encyclopedia public dataset and in mouse proneural glioma cell lines. Convection-enhanced delivery (CED) of etoposide was tested on cell-based PDGF(+)PTEN(-/-)p53(-/-) and retroviral-based PDGF(+)PTEN(-/-) mouse proneural glioma models. TOP2 expression was significantly higher in human proneural glioblastoma and in mouse proneural tumors at early as well as late stages of development compared with normal brain. TOP2B transcript correlated with susceptibility to etoposide in mouse proneural cell lines and in 139 human cancer cell lines from the Cancer Cell Line Encyclopedia. Intracranial etoposide CED treatment (680 μM) was well tolerated by mice and led to a significant survival benefit in the PDGF(+)PTEN(-/-)p53(-/-) glioma model. Moreover, etoposide CED treatment at 80 μM but not 4 μM led to a significant survival advantage in the PDGF(+)PTEN(-/-) glioma model. TOP2 is highly expressed in proneural gliomas, rendering its pharmacological targeting by intratumoral administration of etoposide by CED effective on murine proneural gliomas. We provide evidence supporting clinical testing of CED of etoposide with a molecular-based patient selection approach. Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Multi-tissue DNA methylation age predictor in mouse.

PubMed

Stubbs, Thomas M; Bonder, Marc Jan; Stark, Anne-Katrien; Krueger, Felix; von Meyenn, Ferdinand; Stegle, Oliver; Reik, Wolf

2017-04-11

DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse. We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet. Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.

Proteomic interactions in the mouse vitreous-retina complex.

PubMed

Skeie, Jessica M; Mahajan, Vinit B

2013-01-01

Human vitreoretinal diseases are due to presumed abnormal mechanical interactions between the vitreous and retina, and translational models are limited. This study determined whether nonstructural proteins and potential retinal biomarkers were expressed by the normal mouse vitreous and retina. Vitreous and retina samples from mice were collected by evisceration and analyzed by liquid chromatography-tandem mass spectrometry. Identified proteins were further analyzed for differential expression and functional interactions using bioinformatic software. We identified 1,680 unique proteins in the retina and 675 unique proteins in the vitreous. Unbiased clustering identified protein pathways that distinguish retina from vitreous including oxidative phosphorylation and neurofilament cytoskeletal remodeling, whereas the vitreous expressed oxidative stress and innate immunology pathways. Some intracellular protein pathways were found in both retina and vitreous, such as glycolysis and gluconeogenesis and neuronal signaling, suggesting proteins might be shuttled between the retina and vitreous. We also identified human disease biomarkers represented in the mouse vitreous and retina, including carbonic anhydrase-2 and 3, crystallins, macrophage inhibitory factor, glutathione peroxidase, peroxiredoxins, S100 precursors, and von Willebrand factor. Our analysis suggests the vitreous expresses nonstructural proteins that functionally interact with the retina to manage oxidative stress, immune reactions, and intracellular proteins may be exchanged between the retina and vitreous. This novel proteomic dataset can be used for investigating human vitreoretinopathies in mouse models. Validation of vitreoretinal biomarkers for human ocular diseases will provide a critical tool for diagnostics and an avenue for therapeutics.

Molecular Targeted Therapies of Childhood Choroid Plexus Carcinoma

DTIC Science & Technology

2013-10-01

Microarray intensities were analyzed in PGS, using the benign human choroid plexus papilloma (CPP) samples as an expression baseline reference. This...additional human and mouse CPC genomic profiles (timeframe: months 1-5). The goal of these studies is to expand our number of genomic profiles (DNA and...mRNA arrays) of both human and mouse CPCs to provide a comprehensive dataset with which to identify key candidate oncogenes, tumor suppressor genes

Molecular Targeted Therapies of Childhood Choroid Plexus Carcinoma

DTIC Science & Technology

2012-10-01

Microarray intensities were analyzed in PGS, using the benign human choroid plexus papilloma (CPP) samples as an expression baseline reference...identify candidate drug targets of CPC. Task 1: Generation of additional human and mouse CPC genomic profiles (timeframe: months 1-5). The goal...of these studies is to expand our number of genomic profiles (DNA and mRNA arrays) of both human and mouse CPCs to provide a comprehensive dataset

Molecular Targeted Therapies of Childhood Choroid Plexus Carcinoma

DTIC Science & Technology

2011-10-01

were analyzed in PGS, using the benign human choroid plexus papilloma (CPP) samples as an expression baseline reference. This analysis highlights...Task 1: Generation of additional human and mouse CPC genomic profiles (timeframe: months 1-5). The goal of these studies is to expand our...number of genomic profiles (DNA and mRNA arrays) of both human and mouse CPCs to provide a comprehensive dataset with which to identify key candidate

Virtual unfolding of light sheet fluorescence microscopy dataset for quantitative analysis of the mouse intestine

NASA Astrophysics Data System (ADS)

Candeo, Alessia; Sana, Ilenia; Ferrari, Eleonora; Maiuri, Luigi; D'Andrea, Cosimo; Valentini, Gianluca; Bassi, Andrea

2016-05-01

Light sheet fluorescence microscopy has proven to be a powerful tool to image fixed and chemically cleared samples, providing in depth and high resolution reconstructions of intact mouse organs. We applied light sheet microscopy to image the mouse intestine. We found that large portions of the sample can be readily visualized, assessing the organ status and highlighting the presence of regions with impaired morphology. Yet, three-dimensional (3-D) sectioning of the intestine leads to a large dataset that produces unnecessary storage and processing overload. We developed a routine that extracts the relevant information from a large image stack and provides quantitative analysis of the intestine morphology. This result was achieved by a three step procedure consisting of: (1) virtually unfold the 3-D reconstruction of the intestine; (2) observe it layer-by-layer; and (3) identify distinct villi and statistically analyze multiple samples belonging to different intestinal regions. Even if the procedure has been developed for the murine intestine, most of the underlying concepts have a general applicability.

The truth about mouse, human, worms and yeast

PubMed Central

2004-01-01

Genome comparisons are behind the powerful new annotation methods being developed to find all human genes, as well as genes from other genomes. Genomes are now frequently being studied in pairs to provide cross-comparison datasets. This 'Noah's Ark' approach often reveals unsuspected genes and may support the deletion of false-positive predictions. Joining mouse and human as the cross-comparison dataset for the first two mammals are: two Drosophila species, D. melanogaster and D. pseudoobscura; two sea squirts, Ciona intestinalis and Ciona savignyi; four yeast (Saccharomyces) species; two nematodes, Caenorhabditis elegans and Caenorhabditis briggsae; and two pufferfish (Takefugu rubripes and Tetraodon nigroviridis). Even genomes like yeast and C. elegans, which have been known for more than five years, are now being significantly improved. Methods developed for yeast or nematodes will now be applied to mouse and human, and soon to additional mammals such as rat and dog, to identify all the mammalian protein-coding genes. Current large disparities between human Unigene predictions (127,835 genes) and gene-scanning methods (45,000 genes) still need to be resolved. This will be the challenge during the next few years. PMID:15601543

The truth about mouse, human, worms and yeast.

PubMed

Nelson, David R; Nebert, Daniel W

2004-01-01

Genome comparisons are behind the powerful new annotation methods being developed to find all human genes, as well as genes from other genomes. Genomes are now frequently being studied in pairs to provide cross-comparison datasets. This 'Noah's Ark' approach often reveals unsuspected genes and may support the deletion of false-positive predictions. Joining mouse and human as the cross-comparison dataset for the first two mammals are: two Drosophila species, D. melanogaster and D. pseudoobscura; two sea squirts, Ciona intestinalis and Ciona savignyi; four yeast (Saccharomyces) species; two nematodes, Caenorhabditis elegans and Caenorhabditis briggsae; and two pufferfish (Takefugu rubripes and Tetraodon nigroviridis). Even genomes like yeast and C. elegans, which have been known for more than five years, are now being significantly improved. Methods developed for yeast or nematodes will now be applied to mouse and human, and soon to additional mammals such as rat and dog, to identify all the mammalian protein-coding genes. Current large disparities between human Unigene predictions (127,835 genes) and gene-scanning methods (45,000 genes) still need to be resolved. This will be the challenge during the next few years.

A Feature-Based Approach to Modeling Protein–DNA Interactions

PubMed Central

Segal, Eran

2008-01-01

Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF–DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/. PMID:18725950

Functional Targets of the Monogenic Diabetes Transcription Factors HNF-1α and HNF-4α Are Highly Conserved Between Mice and Humans

PubMed Central

Boj, Sylvia F.; Servitja, Joan Marc; Martin, David; Rios, Martin; Talianidis, Iannis; Guigo, Roderic; Ferrer, Jorge

2009-01-01

OBJECTIVE The evolutionary conservation of transcriptional mechanisms has been widely exploited to understand human biology and disease. Recent findings, however, unexpectedly showed that the transcriptional regulators hepatocyte nuclear factor (HNF)-1α and -4α rarely bind to the same genes in mice and humans, leading to the proposal that tissue-specific transcriptional regulation has undergone extensive divergence in the two species. Such observations have major implications for the use of mouse models to understand HNF-1α– and HNF-4α–deficient diabetes. However, the significance of studies that assess binding without considering regulatory function is poorly understood. RESEARCH DESIGN AND METHODS We compared previously reported mouse and human HNF-1α and HNF-4α binding studies with independent binding experiments. We also integrated binding studies with mouse and human loss-of-function gene expression datasets. RESULTS First, we confirmed the existence of species-specific HNF-1α and -4α binding, yet observed incomplete detection of binding in the different datasets, causing an underestimation of binding conservation. Second, only a minor fraction of HNF-1α– and HNF-4α–bound genes were downregulated in the absence of these regulators. This subset of functional targets did not show evidence for evolutionary divergence of binding or binding sequence motifs. Finally, we observed differences between conserved and species-specific binding properties. For example, conserved binding was more frequently located near transcriptional start sites and was more likely to involve multiple binding events in the same gene. CONCLUSIONS Despite evolutionary changes in binding, essential direct transcriptional functions of HNF-1α and -4α are largely conserved between mice and humans. PMID:19188435

seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data.

PubMed

Chen, Ziyi; Quan, Lijun; Huang, Anfei; Zhao, Qiang; Yuan, Yao; Yuan, Xuye; Shen, Qin; Shang, Jingzhe; Ben, Yinyin; Qin, F Xiao-Feng; Wu, Aiping

2018-01-01

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.

Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography

PubMed Central

Weiler, Nicholas C; Collman, Forrest; Vogelstein, Joshua T; Burns, Randal; Smith, Stephen J

2014-01-01

A major question in neuroscience is how diverse subsets of synaptic connections in neural circuits are affected by experience dependent plasticity to form the basis for behavioral learning and memory. Differences in protein expression patterns at individual synapses could constitute a key to understanding both synaptic diversity and the effects of plasticity at different synapse populations. Our approach to this question leverages the immunohistochemical multiplexing capability of array tomography (ATomo) and the columnar organization of mouse barrel cortex to create a dataset comprising high resolution volumetric images of spared and deprived cortical whisker barrels stained for over a dozen synaptic molecules each. These dataset has been made available through the Open Connectome Project for interactive online viewing, and may also be downloaded for offline analysis using web, Matlab, and other interfaces. PMID:25977797

Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography.

PubMed

Weiler, Nicholas C; Collman, Forrest; Vogelstein, Joshua T; Burns, Randal; Smith, Stephen J

2014-01-01

A major question in neuroscience is how diverse subsets of synaptic connections in neural circuits are affected by experience dependent plasticity to form the basis for behavioral learning and memory. Differences in protein expression patterns at individual synapses could constitute a key to understanding both synaptic diversity and the effects of plasticity at different synapse populations. Our approach to this question leverages the immunohistochemical multiplexing capability of array tomography (ATomo) and the columnar organization of mouse barrel cortex to create a dataset comprising high resolution volumetric images of spared and deprived cortical whisker barrels stained for over a dozen synaptic molecules each. These dataset has been made available through the Open Connectome Project for interactive online viewing, and may also be downloaded for offline analysis using web, Matlab, and other interfaces.

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

Large-Scale Mass Spectrometry Imaging Investigation of Consequences of Cortical Spreading Depression in a Transgenic Mouse Model of Migraine

NASA Astrophysics Data System (ADS)

Carreira, Ricardo J.; Shyti, Reinald; Balluff, Benjamin; Abdelmoula, Walid M.; van Heiningen, Sandra H.; van Zeijl, Rene J.; Dijkstra, Jouke; Ferrari, Michel D.; Tolner, Else A.; McDonnell, Liam A.; van den Maagdenberg, Arn M. J. M.

2015-06-01

Cortical spreading depression (CSD) is the electrophysiological correlate of migraine aura. Transgenic mice carrying the R192Q missense mutation in the Cacna1a gene, which in patients causes familial hemiplegic migraine type 1 (FHM1), exhibit increased propensity to CSD. Herein, mass spectrometry imaging (MSI) was applied for the first time to an animal cohort of transgenic and wild type mice to study the biomolecular changes following CSD in the brain. Ninety-six coronal brain sections from 32 mice were analyzed by MALDI-MSI. All MSI datasets were registered to the Allen Brain Atlas reference atlas of the mouse brain so that the molecular signatures of distinct brain regions could be compared. A number of metabolites and peptides showed substantial changes in the brain associated with CSD. Among those, different mass spectral features showed significant ( t-test, P < 0.05) changes in the cortex, 146 and 377 Da, and in the thalamus, 1820 and 1834 Da, of the CSD-affected hemisphere of FHM1 R192Q mice. Our findings reveal CSD- and genotype-specific molecular changes in the brain of FHM1 transgenic mice that may further our understanding about the role of CSD in migraine pathophysiology. The results also demonstrate the utility of aligning MSI datasets to a common reference atlas for large-scale MSI investigations.

Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability.

PubMed

Reggiani, Claudio; Coppens, Sandra; Sekhara, Tayeb; Dimov, Ivan; Pichon, Bruno; Lufin, Nicolas; Addor, Marie-Claude; Belligni, Elga Fabia; Digilio, Maria Cristina; Faletra, Flavio; Ferrero, Giovanni Battista; Gerard, Marion; Isidor, Bertrand; Joss, Shelagh; Niel-Bütschi, Florence; Perrone, Maria Dolores; Petit, Florence; Renieri, Alessandra; Romana, Serge; Topa, Alexandra; Vermeesch, Joris Robert; Lenaerts, Tom; Casimir, Georges; Abramowicz, Marc; Bontempi, Gianluca; Vilain, Catheline; Deconinck, Nicolas; Smits, Guillaume

2017-07-19

Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play. Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse. These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients' clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories. While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases.

Fasting and Fast Food Diet Play an Opposite Role in Mice Brain Aging.

PubMed

Castrogiovanni, Paola; Li Volti, Giovanni; Sanfilippo, Cristina; Tibullo, Daniele; Galvano, Fabio; Vecchio, Michele; Avola, Roberto; Barbagallo, Ignazio; Malaguarnera, Lucia; Castorina, Sergio; Musumeci, Giuseppe; Imbesi, Rosa; Di Rosa, Michelino

2018-01-20

Fasting may be exploited as a possible strategy for prevention and treatment of several diseases such as diabetes, obesity, and aging. On the other hand, high-fat diet (HFD) represents a risk factor for several diseases and increased mortality. The aim of the present study was to evaluate the impact of fasting on mouse brain aging transcriptome and how HFD regulates such pathways. We used the NCBI Gene Expression Omnibus (GEO) database, in order to identify suitable microarray datasets comparing mouse brain transcriptome under fasting or HFD vs aged mouse brain transcriptome. Three microarray datasets were selected for this study, GSE24504, GSE6285, and GSE8150, and the principal molecular mechanisms involved in this process were evaluated. This analysis showed that, regardless of fasting duration, mouse brain significantly expressed 21 and 30 upregulated and downregulated genes, respectively. The involved biological processes were related to cell cycle arrest, cell death inhibition, and regulation of cellular metabolism. Comparing mouse brain transcriptome under fasting and aged conditions, we found out that the number of genes in common increased with the duration of fasting (222 genes), peaking at 72 h. In addition, mouse brain transcriptome under HFD resembles for the 30% the one of the aged mice. Furthermore, several molecular processes were found to be shared between HFD and aging. In conclusion, we suggest that fasting and HFD play an opposite role in brain transcriptome of aged mice. Therefore, an intermittent diet could represent a possible clinical strategy to counteract aging, loss of memory, and neuroinflammation. Furthermore, low-fat diet leads to the inactivation of brain degenerative processes triggered by aging.

Cerebral oxidative metabolism mapping in four genetic mouse models of anxiety and mood disorders.

PubMed

Matrov, Denis; Kaart, Tanel; Lanfumey, Laurence; Maldonado, Rafael; Sharp, Trevor; Tordera, Rosa M; Kelly, Paul A; Deakin, Bill; Harro, Jaanus

2018-06-07

The psychopathology of depression is highly complex and the outcome of studies on animal models is divergent. In order to find brain regions that could be metabolically distinctively active across a variety of mouse depression models and to compare the interconnectivity of brain regions of wild-type and such genetically modified mice, histochemical mapping of oxidative metabolism was performed by the measurement of cytochrome oxidase activity. We included mice with the heterozygous knockout of the vesicular glutamate transporter (VGLUT 1 -/+ ), full knockout of the cannabinoid 1 receptor (CB1 -/- ), an anti-sense knockdown of the glucocorticoid receptor (GRi) and overexpression of the human 5-hydroxytryptamine transporter (h5-HTT). Altogether 76 mouse brains were studied to measure oxidative metabolism in one hundred brain regions, and the obtained dataset was submitted to a variety of machine learning algorithms and multidimensional scaling. Overall, the top brain regions having the largest contribution to classification into depression model were the lateroanterior hypothalamic nucleus, the anterior part of the basomedial amygdaloid nucleus, claustrum, the suprachiasmatic nucleus, the ventromedial hypothalamic nucleus, and the anterior hypothalamic area. In terms of the patterns of inter-regional relationship between wild-type and genetically modified mice there was little overall difference, while the most deviating brain regions were cortical amygdala and ventrolateral and ventral posteromedial thalamic nuclei. The GRi mice that most clearly differed from their controls exhibited deviation of connectivity for a number of brain regions, such as ventrolateral thalamic nucleus, the intermediate part of the lateral septal nucleus, the anteriodorsal part of the medial amygdaloid nucleus, the medial division of the central amygdaloid nucleus, ventral pallidum, nucleus of the vertical limb of the diagonal band, anteroventral parts of the thalamic nucleus and parts of the bed nucleus of the stria terminalis. Conclusively, the GRi mouse model was characterized by changes in the functional connectivity of the extended amygdala and stress response circuits. Copyright © 2018 Elsevier B.V. All rights reserved.

Follistatin is a metastasis suppressor in a mouse model of HER2-positive breast cancer.

PubMed

Seachrist, Darcie D; Sizemore, Steven T; Johnson, Emhonta; Abdul-Karim, Fadi W; Weber Bonk, Kristen L; Keri, Ruth A

2017-06-05

Follistatin (FST) is an intrinsic inhibitor of activin, a member of the transforming growth factor-β superfamily of ligands. The prognostic value of FST and its family members, the follistatin-like (FSTL) proteins, have been studied in various cancers. However, these studies, as well as limited functional analyses of the FSTL proteins, have yielded conflicting results on the role of these proteins in disease progression. Furthermore, very few have been focused on FST itself. We assessed whether FST may be a suppressor of tumorigenesis and/or metastatic progression in breast cancer. Using publicly available gene expression data, we examined the expression patterns of FST and INHBA, a subunit of activin, in normal and cancerous breast tissue and the prognostic value of FST in breast cancer metastases, recurrence-free survival, and overall survival. The functional effects of activin and FST on in vitro proliferation, migration, and invasion of breast cancer cells were also examined. FST overexpression in an autochthonous mouse model of breast cancer was then used to assess the in vivo impact of FST on metastatic progression. Examination of multiple breast cancer datasets revealed that FST expression is reduced in breast cancers compared with normal tissue and that low FST expression predicts increased metastasis and reduced overall survival. FST expression was also reduced in a mouse model of HER2/Neu-induced metastatic breast cancer. We found that FST blocks activin-induced breast epithelial cell migration in vitro, suggesting that its loss may promote breast cancer aggressiveness. To directly determine if FST restoration could inhibit metastatic progression, we transgenically expressed FST in the HER2/Neu model. Although FST had no impact on tumor initiation or growth, it completely blocked the formation of lung metastases. These data indicate that FST is a bona fide metastasis suppressor in this mouse model and support future efforts to develop an FST mimetic to suppress metastatic progression.

Thalamocortical Projection Neuron and Interneuron Numbers in the Visual Thalamic Nuclei of the Adult C57BL/6 Mouse.

PubMed

Evangelio, Marian; García-Amado, María; Clascá, Francisco

2018-01-01

A key parameter to constrain predictive, bottom-up circuit models of a given brain domain is the number and position of the neuronal populations involved. These include not only the neurons whose bodies reside within the domain, but also the neurons in distant regions that innervate the domain. The mouse visual cortex receives its main subcortical input from the dorsal lateral geniculate nucleus (dLGN) and the lateral posterior (LP) complex of the thalamus. The latter consists of three different nuclei: lateral posterior lateral (LPL), lateral posterior medial rostral (LPMR), and lateral posterior medial caudal (LPMC), each exhibiting specific patterns of connections with the various visual cortical areas. Here, we have determined the number of thalamocortical projection neurons and interneurons in the LP complex and dLGN of the adult C57BL/6 male mouse. We combined Nissl staining and histochemical and immunolabeling methods for consistently delineating nuclei borders, and applied unbiased stereological cell counting methods. Thalamic interneurons were identified using GABA immunolabeling. The C57BL/6 dLGN contains ∼21,200 neurons, while LP complex contains ∼31,000 total neurons. The dLGN and LP are the only nuclei of the mouse dorsal thalamus containing substantial numbers GABA-immunoreactive interneurons. These interneurons, however, are scarcer than previously estimated; they are 5.6% of dLGN neurons and just 1.9% of the LP neurons. It can be thus inferred that the dLGN contains ∼20,000 and the LP complex ∼30,400 thalamocortical projection neurons (∼12,000 in LPL, 15,200 in LPMR, and 4,200 in LPMC). The present dataset is relevant for constraining models of mouse visual thalamocortical circuits, as well as for quantitative comparisons between genetically modified mouse strains, or across species.

Thalamocortical Projection Neuron and Interneuron Numbers in the Visual Thalamic Nuclei of the Adult C57BL/6 Mouse

PubMed Central

Evangelio, Marian; García-Amado, María; Clascá, Francisco

2018-01-01

A key parameter to constrain predictive, bottom-up circuit models of a given brain domain is the number and position of the neuronal populations involved. These include not only the neurons whose bodies reside within the domain, but also the neurons in distant regions that innervate the domain. The mouse visual cortex receives its main subcortical input from the dorsal lateral geniculate nucleus (dLGN) and the lateral posterior (LP) complex of the thalamus. The latter consists of three different nuclei: lateral posterior lateral (LPL), lateral posterior medial rostral (LPMR), and lateral posterior medial caudal (LPMC), each exhibiting specific patterns of connections with the various visual cortical areas. Here, we have determined the number of thalamocortical projection neurons and interneurons in the LP complex and dLGN of the adult C57BL/6 male mouse. We combined Nissl staining and histochemical and immunolabeling methods for consistently delineating nuclei borders, and applied unbiased stereological cell counting methods. Thalamic interneurons were identified using GABA immunolabeling. The C57BL/6 dLGN contains ∼21,200 neurons, while LP complex contains ∼31,000 total neurons. The dLGN and LP are the only nuclei of the mouse dorsal thalamus containing substantial numbers GABA-immunoreactive interneurons. These interneurons, however, are scarcer than previously estimated; they are 5.6% of dLGN neurons and just 1.9% of the LP neurons. It can be thus inferred that the dLGN contains ∼20,000 and the LP complex ∼30,400 thalamocortical projection neurons (∼12,000 in LPL, 15,200 in LPMR, and 4,200 in LPMC). The present dataset is relevant for constraining models of mouse visual thalamocortical circuits, as well as for quantitative comparisons between genetically modified mouse strains, or across species. PMID:29706872

Mitochondrial dysfunction, oxidative stress and apoptosis revealed by proteomic and transcriptomic analyses of the striata in two mouse models of Parkinson’s disease

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

Chin, Mark H.; Qian, Weijun; Wang, Haixing

2008-02-10

The molecular mechanisms underlying the changes in the nigrostriatal pathway in Parkinson disease (PD) are not completely understood. Here we use mass spectrometry and microarrays to study the proteomic and transcriptomic changes in the striatum of two mouse models of PD, induced by the distinct neurotoxins 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and methamphetamine (METH). Proteomic analyses resulted in the identification and relative quantification of 912 proteins with two or more unique peptides and 85 proteins with significant abundance changes following neurotoxin treatment. Similarly, microarray analyses revealed 181 genes with significant changes in mRNA following neurotoxin treatment. The combined protein and gene list providesmore » a clearer picture of the potential mechanisms underlying neurodegeneration observed in PD. Functional analysis of this combined list revealed a number of significant categories, including mitochondrial dysfunction, oxidative stress response and apoptosis. Additionally, codon usage and miRNAs may play an important role in translational control in the striatum. These results constitute one of the largest datasets integrating protein and transcript changes for these neurotoxin models with many similar endpoint phenotypes but distinct mechanisms.« less

Interactive visualization and analysis of multimodal datasets for surgical applications.

PubMed

Kirmizibayrak, Can; Yim, Yeny; Wakid, Mike; Hahn, James

2012-12-01

Surgeons use information from multiple sources when making surgical decisions. These include volumetric datasets (such as CT, PET, MRI, and their variants), 2D datasets (such as endoscopic videos), and vector-valued datasets (such as computer simulations). Presenting all the information to the user in an effective manner is a challenging problem. In this paper, we present a visualization approach that displays the information from various sources in a single coherent view. The system allows the user to explore and manipulate volumetric datasets, display analysis of dataset values in local regions, combine 2D and 3D imaging modalities and display results of vector-based computer simulations. Several interaction methods are discussed: in addition to traditional interfaces including mouse and trackers, gesture-based natural interaction methods are shown to control these visualizations with real-time performance. An example of a medical application (medialization laryngoplasty) is presented to demonstrate how the combination of different modalities can be used in a surgical setting with our approach.

Comprehensive optical and data management infrastructure for high-throughput light-sheet microscopy of whole mouse brains.

PubMed

Müllenbroich, M Caroline; Silvestri, Ludovico; Onofri, Leonardo; Costantini, Irene; Hoff, Marcel Van't; Sacconi, Leonardo; Iannello, Giulio; Pavone, Francesco S

2015-10-01

Comprehensive mapping and quantification of neuronal projections in the central nervous system requires high-throughput imaging of large volumes with microscopic resolution. To this end, we have developed a confocal light-sheet microscope that has been optimized for three-dimensional (3-D) imaging of structurally intact clarified whole-mount mouse brains. We describe the optical and electromechanical arrangement of the microscope and give details on the organization of the microscope management software. The software orchestrates all components of the microscope, coordinates critical timing and synchronization, and has been written in a versatile and modular structure using the LabVIEW language. It can easily be adapted and integrated to other microscope systems and has been made freely available to the light-sheet community. The tremendous amount of data routinely generated by light-sheet microscopy further requires novel strategies for data handling and storage. To complete the full imaging pipeline of our high-throughput microscope, we further elaborate on big data management from streaming of raw images up to stitching of 3-D datasets. The mesoscale neuroanatomy imaged at micron-scale resolution in those datasets allows characterization and quantification of neuronal projections in unsectioned mouse brains.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data.

PubMed

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J

2014-07-01

High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. © The Author 2014. Published by Oxford University Press. All rights reserved.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

PubMed Central

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J.

2014-01-01

Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. Contact: fbuettner.phys@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24618470

Rodent phylogeny revised: analysis of six nuclear genes from all major rodent clades

PubMed Central

Blanga-Kanfi, Shani; Miranda, Hector; Penn, Osnat; Pupko, Tal; DeBry, Ronald W; Huchon, Dorothée

2009-01-01

Background Rodentia is the most diverse order of placental mammals, with extant rodent species representing about half of all placental diversity. In spite of many morphological and molecular studies, the family-level relationships among rodents and the location of the rodent root are still debated. Although various datasets have already been analyzed to solve rodent phylogeny at the family level, these are difficult to combine because they involve different taxa and genes. Results We present here the largest protein-coding dataset used to study rodent relationships. It comprises six nuclear genes, 41 rodent species, and eight outgroups. Our phylogenetic reconstructions strongly support the division of Rodentia into three clades: (1) a "squirrel-related clade", (2) a "mouse-related clade", and (3) Ctenohystrica. Almost all evolutionary relationships within these clades are also highly supported. The primary remaining uncertainty is the position of the root. The application of various models and techniques aimed to remove non-phylogenetic signal was unable to solve the basal rodent trifurcation. Conclusion Sequencing and analyzing a large sequence dataset enabled us to resolve most of the evolutionary relationships among Rodentia. Our findings suggest that the uncertainty regarding the position of the rodent root reflects the rapid rodent radiation that occurred in the Paleocene rather than the presence of conflicting phylogenetic and non-phylogenetic signals in the dataset. PMID:19341461

AHCODA-DB: a data repository with web-based mining tools for the analysis of automated high-content mouse phenomics data.

PubMed

Koopmans, Bastijn; Smit, August B; Verhage, Matthijs; Loos, Maarten

2017-04-04

Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available for standardized quality control, data analysis and mining at the resolution of individual mice. Here, we present AHCODA-DB, a public data repository with standardized quality control and exclusion criteria aimed to enhance robustness of data, enabled with web-based mining tools for the analysis of individually and group-wise collected mouse phenotypic data. AHCODA-DB allows monitoring in vivo effects of compounds collected from conventional behavioural tests and from automated home-cage experiments assessing spontaneous behaviour, anxiety and cognition without human interference. AHCODA-DB includes such data from mutant mice (transgenics, knock-out, knock-in), (recombinant) inbred strains, and compound effects in wildtype mice and disease models. AHCODA-DB provides real time statistical analyses with single mouse resolution and versatile suite of data presentation tools. On March 9th, 2017 AHCODA-DB contained 650 k data points on 2419 parameters from 1563 mice. AHCODA-DB provides users with tools to systematically explore mouse behavioural data, both with positive and negative outcome, published and unpublished, across time and experiments with single mouse resolution. The standardized (automated) experimental settings and the large current dataset (1563 mice) in AHCODA-DB provide a unique framework for the interpretation of behavioural data and drug effects. The use of common ontologies allows data export to other databases such as the Mouse Phenome Database. Unbiased presentation of positive and negative data obtained under the highly standardized screening conditions increase cost efficiency of publicly funded mouse screening projects and help to reach consensus conclusions on drug responses and mouse behavioural phenotypes. The website is publicly accessible through https://public.sylics.com and can be viewed in every recent version of all commonly used browsers.

The integration of quantitative genetics, paleontology, and neontology reveals genetic underpinnings of primate dental evolution.

PubMed

Hlusko, Leslea J; Schmitt, Christopher A; Monson, Tesla A; Brasil, Marianne F; Mahaney, Michael C

2016-08-16

Developmental genetics research on mice provides a relatively sound understanding of the genes necessary and sufficient to make mammalian teeth. However, mouse dentitions are highly derived compared with human dentitions, complicating the application of these insights to human biology. We used quantitative genetic analyses of data from living nonhuman primates and extensive osteological and paleontological collections to refine our assessment of dental phenotypes so that they better represent how the underlying genetic mechanisms actually influence anatomical variation. We identify ratios that better characterize the output of two dental genetic patterning mechanisms for primate dentitions. These two newly defined phenotypes are heritable with no measurable pleiotropic effects. When we consider how these two phenotypes vary across neontological and paleontological datasets, we find that the major Middle Miocene taxonomic shift in primate diversity is characterized by a shift in these two genetic outputs. Our results build on the mouse model by combining quantitative genetics and paleontology, and thereby elucidate how genetic mechanisms likely underlie major events in primate evolution.

Fusimonas intestini gen. nov., sp. nov., a novel intestinal bacterium of the family Lachnospiraceae associated with diabetes in mice.

PubMed

Kusada, Hiroyuki; Kameyama, Keishi; Meng, Xian-Ying; Kamagata, Yoichi; Tamaki, Hideyuki

2017-12-22

Our previous study shows that an anaerobic intestinal bacterium strain AJ110941 P contributes to type 2 diabetes development in mice. Here we phylogenetically and physiologically characterized this unique mouse gut bacterium. The 16S rRNA gene analysis revealed that the strain belongs to the family Lachnospiraceae but shows low sequence similarities ( < 92.5%) to valid species, and rather formed a distinct cluster with uncultured mouse gut bacteria clones. In metagenomic database survey, the 16S sequence of AJ110941 P also matched with mouse gut-derived datasets (56% of total datasets) with > 99% similarity, suggesting that AJ110941 P -related bacteria mainly reside in mouse digestive tracts. Strain AJ110941 P shared common physiological traits (e.g., Gram-positive, anaerobic, mesophilic, and fermentative growth with carbohydrates) with relative species of the Lachnospiraceae. Notably, the biofilm-forming capacity was found in both AJ110941 P and relative species. However, AJ110941 P possessed far more strong ability to produce biofilm than relative species and formed unique structure of extracellular polymeric substances. Furthermore, AJ110941 P cells are markedly long fusiform-shaped rods (9.0-62.5 µm) with multiple flagella that have never been observed in any other Lachnospiraceae members. Based on the phenotypic and phylogenetic features, we propose a new genus and species, Fusimonas intestini gen. nov., sp. nov. for strain AJ110941 P (FERM BP-11443).

Proteomic characterization of EL4 lymphoma-derived tumors upon chemotherapy treatment reveals potential roles for lysosomes and caspase-6 during tumor cell death in vivo.

PubMed

Kramer, David A; Eldeeb, Mohamed A; Wuest, Melinda; Mercer, John; Fahlman, Richard P

2017-06-01

The murine mouse lymphoblastic lymphoma cell line (EL4) tumor model is an established in vivo apoptosis model for the investigation of novel cancer imaging agents and immunological treatments due to the rapid and significant response of the EL4 tumors to cyclophosphamide and etoposide combination chemotherapy. Despite the utility of this model system in cancer research, little is known regarding the molecular details of in vivo tumor cell death. Here, we report the first in-depth quantitative proteomic analysis of the changes that occur in these tumors upon cyclophosphamide and etoposide treatment in vivo. Using a label-free quantitative proteomic approach a total of 5838 proteins were identified in the treated and untreated tumors, of which 875 were determined to change in abundance with statistical significance. Initial analysis of the data reveals changes that may have been predicted, such as the downregulation of ribosomes, but demonstrates the robustness of the dataset. Analysis of the dataset also reveals the unexpected downregulation of caspase-3 and an upregulation of caspase-6 in addition to a global upregulation of lysosomal proteins in the bulk of the tumor. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Serotonin 6 receptor controls Alzheimer's disease and depression.

PubMed

Yun, Hyung-Mun; Park, Kyung-Ran; Kim, Eun-Cheol; Kim, Sanghyeon; Hong, Jin Tae

2015-09-29

Alzheimer's disease (AD) and depression in late life are one of the most severe health problems in the world disorders. Serotonin 6 receptor (5-HT6R) has caused much interest for potential roles in AD and depression. However, a causative role of perturbed 5-HT6R function between two diseases was poorly defined. In the present study, we found that a 5-HT6R antagonist, SB271036 rescued memory impairment by attenuating the generation of Aβ via the inhibition of γ-secretase activity and the inactivation of astrocytes and microglia in the AD mouse model. It was found that the reduction of serotonin level was significantly recovered by SB271036, which was mediated by an indirect regulation of serotonergic neurons via GABA. Selective serotonin reuptake inhibitor (SSRI), fluoxetine significantly improved cognitive impairment and behavioral changes. In human brain of depression patients, we then identified the potential genes, amyloid beta (A4) precursor protein-binding, family A, member 2 (APBA2), well known AD modulators by integrating datasets from neuropathology, microarray, and RNA seq. studies with correlation analysis tools. And also, it was demonstrated in mouse models and patients of AD. These data indicate functional network of 5-HT6R between AD and depression.

Mapping Genetic Variants Associated with Beta-Adrenergic Responses in Inbred Mice

PubMed Central

Hersch, Micha; Peter, Bastian; Kang, Hyun Min; Schüpfer, Fanny; Abriel, Hugues; Pedrazzini, Thierry; Eskin, Eleazar; Beckmann, Jacques S.

2012-01-01

β-blockers and β-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective β 1-blocker, Atenolol (ate), and a β-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the β-adrenergic system, and one locus for the responsiveness of QTc (p<10−8). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10−6). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to β-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied. PMID:22859963

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De; ...

2017-01-28

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

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

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Comparison of TCDD-elicited genome-wide hepatic gene expression in Sprague–Dawley rats and C57BL/6 mice

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

Nault, Rance; Kim, Suntae; Zacharewski, Timothy R., E-mail: tzachare@msu.edu

2013-03-01

Although the structure and function of the AhR are conserved, emerging evidence suggests that downstream effects are species-specific. In this study, rat hepatic gene expression data from the DrugMatrix database (National Toxicology Program) were compared to mouse hepatic whole-genome gene expression data following treatment with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For the DrugMatrix study, male Sprague–Dawley rats were gavaged daily with 20 μg/kg TCDD for 1, 3 and 5 days, while female C57BL/6 ovariectomized mice were examined 1, 3 and 7 days after a single oral gavage of 30 μg/kg TCDD. A total of 649 rat and 1386 mouse genes (|fold change| ≥more » 1.5, P1(t) ≥ 0.99) were differentially expressed following treatment. HomoloGene identified 11,708 orthologs represented across the rat Affymetrix 230 2.0 GeneChip (12,310 total orthologs), and the mouse 4 × 44K v.1 Agilent oligonucleotide array (17,578 total orthologs). Comparative analysis found 563 and 922 orthologs differentially expressed in response to TCDD in the rat and mouse, respectively, with 70 responses associated with immune function and lipid metabolism in common to both. Moreover, QRTPCR analysis of Ceacam1, showed divergent expression (induced in rat; repressed in mouse) functionally consistent with TCDD-elicited hepatic steatosis in the mouse but not the rat. Functional analysis identified orthologs involved in nucleotide binding and acetyltransferase activity in rat, while mouse-specific responses were associated with steroid, phospholipid, fatty acid, and carbohydrate metabolism. These results provide further evidence that TCDD elicits species-specific regulation of distinct gene networks, and outlines considerations for future comparisons of publicly available microarray datasets. - Highlights: ► We performed a whole-genome comparison of TCDD-regulated genes in mice and rats. ► Previous species comparisons were extended using data from the DrugMatrix database. ► Less than 15% of TCDD-regulated orthologs were common to mice and rats. ► Considerations for the comparison of publicly available datasets are described.« less

PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.

PubMed

Soul, Jamie; Hardingham, Timothy E; Boot-Handford, Raymond P; Schwartz, Jean-Marc

2015-01-29

We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.

Creating reference gene annotation for the mouse C57BL6/J genome assembly.

PubMed

Mudge, Jonathan M; Harrow, Jennifer

2015-10-01

Annotation on the reference genome of the C57BL6/J mouse has been an ongoing project ever since the draft genome was first published. Initially, the principle focus was on the identification of all protein-coding genes, although today the importance of describing long non-coding RNAs, small RNAs, and pseudogenes is recognized. Here, we describe the progress of the GENCODE mouse annotation project, which combines manual annotation from the HAVANA group with Ensembl computational annotation, alongside experimental and in silico validation pipelines from other members of the consortium. We discuss the more recent incorporation of next-generation sequencing datasets into this workflow, including the usage of mass-spectrometry data to potentially identify novel protein-coding genes. Finally, we will outline how the C57BL6/J genebuild can be used to gain insights into the variant sites that distinguish different mouse strains and species.

Metabolomic and Lipidomic Analysis of the Heart of Peroxisome Proliferator-Activated Receptor-γ Coactivator 1-β Knock Out Mice on a High Fat Diet.

PubMed

McCombie, Gregor; Medina-Gomez, Gema; Lelliott, Christopher J; Vidal-Puig, Antonio; Griffin, Julian L

2012-06-18

The peroxisome proliferator-activated receptor-γ coactivators (PGC-1) are transcriptional coactivators with an important role in mitochondrial biogenesis and regulation of genes involved in the electron transport chain and oxidative phosphorylation in oxidative tissues including cardiac tissue. These coactivators are thought to play a key role in the development of obesity, type 2 diabetes and the metabolic syndrome. In this study we have used a combined metabolomic and lipidomic analysis of cardiac tissue from the PGC-1β null mouse to examine the effects of a high fat diet on this organ. Multivariate statistics readily separated tissue from PGC-1β null mice from their wild type controls either in gender specific models or in combined datasets. This was associated with an increase in creatine and a decrease in taurine in the null mouse, and an increase in myristic acid and a reduction in long chain polyunsaturated fatty acids for both genders. The most profound changes were detected by liquid chromatography mass spectrometry analysis of intact lipids with the tissue from the null mouse having a profound increase in a number of triglycerides. The metabolomic and lipodomic changes indicate PGC-1β has a profound influence on cardiac metabolism.

EnzML: multi-label prediction of enzyme classes using InterPro signatures

PubMed Central

2012-01-01

Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

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

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001; Gupta, Shikha

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models wasmore » performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.« less

RETROSPECTIVE ANALYSIS: REPRODUCIBILITY OF INTERBLASTOMERE DIFFERENCES OF mRNA EXPRESSION IN 2-CELL STAGE MOUSE EMBRYOS IS REMARKABLY POOR DUE TO COMBINATORIAL MECHANISMS OF BLASTOMERE DIVERSIFICATION.

PubMed

Casser, E; Israel, S; Schlatt, S; Nordhoff, V; Boiani, M

2018-05-09

What is the prevalence, reproducibility and biological significance of transcriptomic differences between sister blastomeres of the mouse 2-cell embryo? Sister 2-cell stage blastomeres are distinguishable from each other by mRNA analysis, attesting to the fact that differentiation starts mostly early in the mouse embryo; however, the interblastomere differences are poorly reproducible and invoke the combinatorial effects of known and new mechanisms of blastomere diversification. Transcriptomic datasets for single blastomeres in mice have been available for years but have never been systematically analysed together, although such an analysis may shed light onto some unclarified topics of early mammalian development. Two unknowns that remain are at which stage embryonic blastomeres start to diversify from each other and what is the molecular origin of that difference. At the earliest postzygotic stage, the 2-cell stage, opinions differ regarding the answer to these questions; one group claims that the first zygotic division yields two equal blastomeres capable of forming a full organism (totipotency) and another group claims evidence for interblastomere differences reminiscent of the prepatterning found in embryos of lower taxa. Regarding the molecular origin of interblastomere differences, there are four prevalent models which invoke 1) oocyte anisotropy, 2) sperm entry point, 3) partition errors of the transcript pool, and 4) asynchronous embryonic genome activation in the two blastomeres. Seven transcriptomic studies published between 2011 and 2017 were eligible for retrospective analysis, since both blastomeres of the mouse 2-cell embryo had been analysed individually regarding the original pair associations and since the datasets were made available in public repositories. Five of these studies, encompassing a total of 43 pairs of sister blastomeres, were selected for further analyses based on high interblastomere correlations of mRNA levels. A double cut-off was used to select mRNAs that had robust interblastomere differences both within and between embryos (hits). The hits of each study were compared and contrasted with the hits of the other studies using Venn diagrams. The hits shared by at least four of five studies were analysed further by bioinformatics. PubMed was systematically examined for mRNA expression profiles of single 2-cell stage blastomeres in addition to publicly available microarray datasets (GEO, ArrayExpress). Based on the original normalisations, data from seven studies were screened for pairwise sample correlation at the gene level (Spearman), and the top five datasets with the highest correlation were subjected to hierarchical cluster analysis. Interblastomere differences of gene expression were expressed as a ratio of the higher to the lower mRNA level for each pair of blastomeres. A double cut-off was used to make the call of interblastomere difference, accepting genes with mRNA ratios above 2 when observed in at least 50% of the pairs, and discarding the other genes. The proportion of interblastomere differences common to at least four of the five datasets was calculated. Finally, the corresponding gene, pathway and enrichment analyses were performed utilising PANTHER and GORILLA platforms. An average of 17% of genes within the datasets are differently expressed between sister blastomeres, a proportion which falls to 1% when considering the differences that are common to at least four of the five studies. Housekeeping mRNAs were not included in the 17% and 1% gene lists, suggesting that the interblastomere differences do not occur simply by chance. The 1% of shared interblastomere differences comprise 100 genes, of which 35 are consistent with at least one of the four prevalent models of sister blastomere diversification. Bioinformatics analysis of the remaining 65 genes that are not consistent with the four models suggests that at least one more mechanism is at play, potentially related to the endomembrane system. Although there are many dimensions to the issue of reproducibility (biological, experimental, analytical), we consider that the sister blastomeres are poised to escape high interblastomere correlations of mRNA levels, because at least five sources of diversity superimpose on each other, accounting for at least 25 = 32 different states. As a result, interblastomere mRNA differences of a given 2-cell embryo are necessarily difficult to reproduce in another 2-cell embryo. Data were as provided by the original studies (GSE21688, GSE22182, GSE27396, GSE45719, GSE57249, E-MTAB-3321, GSE94050). The original studies present similarities (e.g. fertilization in vivo after ovarian stimulation) as well as differences (e.g. mouse strains, method and timing of blastomere separation). We identified robust mRNA differences between the sister blastomeres, but these differences are underestimated because our double cut-off method works with thresholds and affords more protection against false positives than false negatives. Regarding the false negatives, transcriptome analysis may have captured only part of the interblastomere differences due to: 1) the twofold cut-off not being sensitive enough to detect the remaining part of the interblastomere differences, 2) the detection limit of the transcriptomic methods not being sufficient, or 3) interblastomere differences being oblivious to transcriptomic identification because transcriptional changes are oscillatory or because differences are mediated non-transcriptionally or post-transcriptionally. Regarding the false positives, it seems unlikely that a difference was found just by chance for the same group of transcripts due to the same technical error, given that different laboratories produced the data. It is clear that the sister blastomeres are distinguishable from each other by mRNA analysis even at the 2-cell stage; however, efforts to identify large stable patterns may be in vain. This elicits thoughts about the wisdom of adding new transcriptomic datasets to the ones that already exist; if all transcriptomic datasets produced so far show a reproducibility of 1%, then any future study would probably face the same issue again. Possibly, a solid identification of the 'large stable pattern that should be there but was not found' requires an even larger dataset than the sum of the seven datasets considered here. Conversely, small stable patterns may be easier to identify, but their biological relevance is less obvious. Alternatively, interblastomere differences may not be mediated by nucleic acids but by other cellular components. This study was supported by the Deutsche Forschungsgemeinschaft (grant DFG BO 2540-4-3 to M.B. and grant NO 413/3-3 to V.N.). The authors declare that they have no competing financial interests.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.

PubMed

Sweeney, Timothy E; Lofgren, Shane; Khatri, Purvesh; Rogers, Angela J

2017-08-01

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P < 1E -16 ). Neutrophil signatures are enriched in both animal and human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

Pole Figure Explorer v. 1.8

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

Van Benthem, Mark H.

2016-05-04

This software is employed for 3D visualization of X-ray diffraction (XRD) data with functionality for slicing, reorienting, isolating and plotting of 2D color contour maps and 3D renderings of large datasets. The program makes use of the multidimensionality of textured XRD data where diffracted intensity is not constant over a given set of angular positions (as dictated by the three defined dimensional angles of phi, chi, and two-theta). Datasets are rendered in 3D with intensity as a scaler which is represented as a rainbow color scale. A GUI interface and scrolling tools along with interactive function via the mouse allowmore » for fast manipulation of these large datasets so as to perform detailed analysis of diffraction results with full dimensionality of the diffraction space.« less

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.

PubMed

Zhang, Shuyun; Deng, Libin; Jia, Qiyue; Huang, Shaoting; Gu, Junwang; Zhou, Fankun; Gao, Meng; Sun, Xinyi; Feng, Chang; Fan, Guangqin

2017-11-16

Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed. Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain. This database, dbMDEGA ( https://dbmdega.shinyapps.io/dbMDEGA/ ), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder. This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders.

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

PubMed

Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database

PubMed Central

Drabkin, Harold J.; Blake, Judith A.

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications. PMID:23110975

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database.

PubMed

Drabkin, Harold J; Blake, Judith A

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as 'GO' or 'homology' or 'phenotype'. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as 'papers selected for GO that refer to genes with NO GO annotation'. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications.

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

DOE PAGES

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; ...

2015-04-23

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to the probabilistic model. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (µCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of ourmore » probabilistic model. As a result, we perform experiments on micro magnetic resonance angiography (µMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.« less

PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

PubMed Central

Reuter, Kerstin; Helms, Volkhard

2016-01-01

Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non–AUG codons located in the annotated 5’ UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in–frame and out–of–frame translational start sites within the 5’ UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near–cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http://service.bioinformatik.uni-saarland.de/pretis. PMID:27768687

White House Office of the Vice President Announces New Memorandum of Understanding in Clinical Proteogenomics Between the United States and Australia | Office of Cancer Clinical Proteomics Research

Cancer.gov

The White House Office of the Vice President has announced the signing of three Memoranda of Understanding (MOUs) that will make available an unprecedented international dataset to advance cancer research and care.

Integrating multiple analytical datasets to compare metabolite profiles of mouse colonic-cecal contents and feces

USDA-ARS?s Scientific Manuscript database

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of fec...

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain

NASA Astrophysics Data System (ADS)

Castonguay, Alexandre; Lefebvre, Joël; Pouliot, Philippe; Lesage, Frédéric

2018-01-01

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) μm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.

PubMed

Antony, Bhavna Josephine; Kim, Byung-Jin; Lang, Andrew; Carass, Aaron; Prince, Jerry L; Zack, Donald J

2017-01-01

The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.

Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model

PubMed Central

Lang, Andrew; Carass, Aaron; Prince, Jerry L.; Zack, Donald J.

2017-01-01

The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study. PMID:28817571

PM-07LOSS OF ATRX DECREASES SURVIVAL AND IMPROVES RESPONSE TO DNA DAMAGING AGENTS IN A NOVEL MOUSE MODEL OF GLIOBLASTOMA

PubMed Central

Koschmann, Carl; Calinescu, Alexandra; Thomas, Daniel; Kamran, Neha; Nunez-Aguilera, Felipe; Dzaman, Marta; Lemons, Rosie; Li, Youping; Roh, Haeji; Lowenstein, Pedro; Castro, Maria

2014-01-01

Pediatric glioblastoma (GBM) remains one of the most difficult childhood tumors to treat. ATRX is a histone chaperone protein that is mutated primarily in younger patients with GBM. No previous animal model has demonstrated the effect of ATRX loss on GBM formation. We cloned an ATRX knockdown sequence into a Sleeping Beauty (SB) transposase-responsive plasmid (shATRX) for insertion into host genomic DNA. Glioblastomas were induced in mice by injecting plasmids encoding SB transposase/ luciferase, shp53 and NRAS, with or without shATRX, into the ventricle of neonatal mice. Tumors in both groups (with or without shATRX) showed histological hallmarks of human glioblastoma. The loss of ATRX was specifically localized only within tumors generated with the shATRX plasmid and not in the adjacent cortex. Notably, loss of ATRX reduced median survival of mice by 43% (p = 0.012). ATRX-deficient tumors were significantly more likely to develop microsatellite instability (p = 0.014), a hallmark of impaired DNA-damage repair. Analysis of three human GBM sequencing datasets confirmed increased number of somatic nucleotide mutations in ATRX-deficient tumors. Treatment of primary cell cultures generated from mouse GBMs showed that ATRX-deficient tumor cells are significantly more sensitive to DNA damaging agents. In addition, mice with ATRX-deficient GBM treated with whole brain irradiation had trend towards improved survival (p= 0.06), with some long-term survivors. Treated ATRX-deficient tumor cells showed greater evidence of double-stranded DNA breakage, by gH2A.X. In summary, this mouse model prospectively validates ATRX as a tumor suppressor in human GBM for the first time in an animal model. In addition, loss of ATRX leads to increased mutation frequency and response to DNA-damaging therapy. We have generated the hypothesis that ATRX loss leads to a genetically unstable tumor; which is more aggressive when untreated, but more responsive to DNA-damaging therapy, ultimately resulting in equivalent or improved overall survival.

Transposable elements in TDP-43-mediated neurodegenerative disorders.

PubMed

Li, Wanhe; Jin, Ying; Prazak, Lisa; Hammell, Molly; Dubnau, Josh

2012-01-01

Elevated expression of specific transposable elements (TEs) has been observed in several neurodegenerative disorders. TEs also can be active during normal neurogenesis. By mining a series of deep sequencing datasets of protein-RNA interactions and of gene expression profiles, we uncovered extensive binding of TE transcripts to TDP-43, an RNA-binding protein central to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Second, we find that association between TDP-43 and many of its TE targets is reduced in FTLD patients. Third, we discovered that a large fraction of the TEs to which TDP-43 binds become de-repressed in mouse TDP-43 disease models. We propose the hypothesis that TE mis-regulation contributes to TDP-43 related neurodegenerative diseases.

Sequencing, Annotation and Analysis of the Syrian Hamster (Mesocricetus auratus) Transcriptome

PubMed Central

Tchitchek, Nicolas; Safronetz, David; Rasmussen, Angela L.; Martens, Craig; Virtaneva, Kimmo; Porcella, Stephen F.; Feldmann, Heinz

2014-01-01

Background The Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species. Results A cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species. Conclusions This Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies. PMID:25398096

Exploring pathway interactions in insulin resistant mouse liver

PubMed Central

2011-01-01

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. PMID:21843341

Genetically manipulated mouse models of lung disease: potential and pitfalls

PubMed Central

Choi, Alexander J. S.; Owen, Caroline A.; Choi, Augustine M. K.

2012-01-01

Gene targeting in mice (transgenic and knockout) has provided investigators with an unparalleled armamentarium in recent decades to dissect the cellular and molecular basis of critical pathophysiological states. Fruitful information has been derived from studies using these genetically engineered mice with significant impact on our understanding, not only of specific biological processes spanning cell proliferation to cell death, but also of critical molecular events involved in the pathogenesis of human disease. This review will focus on the use of gene-targeted mice to study various models of lung disease including airways diseases such as asthma and chronic obstructive pulmonary disease, and parenchymal lung diseases including idiopathic pulmonary fibrosis, pulmonary hypertension, pneumonia, and acute lung injury. We will attempt to review the current technological approaches of generating gene-targeted mice and the enormous dataset derived from these studies, providing a template for lung investigators. PMID:22198907

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-imaging

PubMed Central

Wuttisarnwattana, Patiwet; Gargesha, Madhusudhana; Hof, Wouter van’t; Cooke, Kenneth R.

2016-01-01

With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (~200GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and <1% false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→99.36%) without a significant drop in precision (99.99%→99.75%). With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells. PMID:26552080

High-throughput discovery of novel developmental phenotypes

PubMed Central

Dickinson, Mary E.; Flenniken, Ann M.; Ji, Xiao; Teboul, Lydia; Wong, Michael D.; White, Jacqueline K.; Meehan, Terrence F.; Weninger, Wolfgang J.; Westerberg, Henrik; Adissu, Hibret; Baker, Candice N.; Bower, Lynette; Brown, James M.; Caddle, L. Brianna; Chiani, Francesco; Clary, Dave; Cleak, James; Daly, Mark J.; Denegre, James M.; Doe, Brendan; Dolan, Mary E.; Edie, Sarah M.; Fuchs, Helmut; Gailus-Durner, Valerie; Galli, Antonella; Gambadoro, Alessia; Gallegos, Juan; Guo, Shiying; Horner, Neil R.; Hsu, Chih-wei; Johnson, Sara J.; Kalaga, Sowmya; Keith, Lance C.; Lanoue, Louise; Lawson, Thomas N.; Lek, Monkol; Mark, Manuel; Marschall, Susan; Mason, Jeremy; McElwee, Melissa L.; Newbigging, Susan; Nutter, Lauryl M.J.; Peterson, Kevin A.; Ramirez-Solis, Ramiro; Rowland, Douglas J.; Ryder, Edward; Samocha, Kaitlin E.; Seavitt, John R.; Selloum, Mohammed; Szoke-Kovacs, Zsombor; Tamura, Masaru; Trainor, Amanda G; Tudose, Ilinca; Wakana, Shigeharu; Warren, Jonathan; Wendling, Olivia; West, David B.; Wong, Leeyean; Yoshiki, Atsushi; MacArthur, Daniel G.; Tocchini-Valentini, Glauco P.; Gao, Xiang; Flicek, Paul; Bradley, Allan; Skarnes, William C.; Justice, Monica J.; Parkinson, Helen E.; Moore, Mark; Wells, Sara; Braun, Robert E.; Svenson, Karen L.; de Angelis, Martin Hrabe; Herault, Yann; Mohun, Tim; Mallon, Ann-Marie; Henkelman, R. Mark; Brown, Steve D.M.; Adams, David J.; Lloyd, K.C. Kent; McKerlie, Colin; Beaudet, Arthur L.; Bucan, Maja; Murray, Stephen A.

2016-01-01

Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 lethal genes during the production of the first 1751 unique gene knockouts. Using a standardised phenotyping platform that incorporates high-resolution 3D imaging, we identified novel phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes identified in our screen, thus providing a novel dataset that facilitates prioritization and validation of mutations identified in clinical sequencing efforts. PMID:27626380

Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis

DOE PAGES

Cahill, John F.; Kertesz, Vilmos; Porta, Tiffany; ...

2018-02-08

Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof.more » Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. Lastly, these results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.« less

Solvent effects on differentiation of mouse brain tissue using laser microdissection ‘cut and drop’ sampling with direct mass spectral analysis

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

Cahill, John F.; Kertesz, Vilmos; Porta, Tiffany

Rationale: Laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry (LMD-LVC/ESI-MS) has potential for on-line classification of tissue but an investigation into what analytical conditions provide best spectral differentiation has not been conducted. The effects of solvent, ionization polarity, and spectral acquisition parameters on differentiation of mouse brain tissue regions are described.Methods: Individual 40 × 40 μm microdissections from cortex, white, grey, granular, and nucleus regions of mouse brain tissue were analyzed using different capture/ESI solvents, in positive and negative ion mode ESI, using time-of-flight (TOF)-MS and sequential window acquisitions of all theoretical spectra (SWATH)-MS (a permutation of tandem-MS), and combinations thereof.more » Principal component analysis-linear discriminant analysis (PCA-LDA), applied to each mass spectral dataset, was used to determine the accuracy of differentiation of mouse brain tissue regions. Results: Mass spectral differences associated with capture/ESI solvent composition manifested as altered relative distributions of ions rather than the presence or absence of unique ions. In negative ion mode ESI, 80/20 (v/v) methanol/water yielded spectra with low signal/noise ratios relative to other solvents. PCA-LDA models acquired using 90/10 (v/v) methanol/chloroform differentiated tissue regions with 100% accuracy while data collected using methanol misclassified some samples. The combination of SWATH-MS and TOF-MS data improved differentiation accuracy.Conclusions: Combined TOF-MS and SWATH-MS data differentiated white, grey, granular, and nucleus mouse tissue regions with greater accuracy than when solely using TOF-MS data. Using 90/10 (v/v) methanol/chloroform, tissue regions were perfectly differentiated. Lastly, these results will guide future studies looking to utilize the potential of LMD-LVC/ESI-MS for tissue and disease differentiation.« less

TISSUES 2.0: an integrative web resource on mammalian tissue expression

PubMed Central

Palasca, Oana; Santos, Alberto; Stolte, Christian; Gorodkin, Jan; Jensen, Lars Juhl

2018-01-01

Abstract Physiological and molecular similarities between organisms make it possible to translate findings from simpler experimental systems—model organisms—into more complex ones, such as human. This translation facilitates the understanding of biological processes under normal or disease conditions. Researchers aiming to identify the similarities and differences between organisms at the molecular level need resources collecting multi-organism tissue expression data. We have developed a database of gene–tissue associations in human, mouse, rat and pig by integrating multiple sources of evidence: transcriptomics covering all four species and proteomics (human only), manually curated and mined from the scientific literature. Through a scoring scheme, these associations are made comparable across all sources of evidence and across organisms. Furthermore, the scoring produces a confidence score assigned to each of the associations. The TISSUES database (version 2.0) is publicly accessible through a user-friendly web interface and as part of the STRING app for Cytoscape. In addition, we analyzed the agreement between datasets, across and within organisms, and identified that the agreement is mainly affected by the quality of the datasets rather than by the technologies used or organisms compared. Database URL: http://tissues.jensenlab.org/ PMID:29617745

Predicting enhancer activity and variant impact using gkm-SVM.

PubMed

Beer, Michael A

2017-09-01

We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone. © 2017 Wiley Periodicals, Inc.

How Random Is Social Behaviour? Disentangling Social Complexity through the Study of a Wild House Mouse Population

PubMed Central

Perony, Nicolas; Tessone, Claudio J.; König, Barbara; Schweitzer, Frank

2012-01-01

Out of all the complex phenomena displayed in the behaviour of animal groups, many are thought to be emergent properties of rather simple decisions at the individual level. Some of these phenomena may also be explained by random processes only. Here we investigate to what extent the interaction dynamics of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the notion of perceptual landscape, a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations. We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns. We find that many high-level features – with the exception of territoriality – of our behavioural dataset can be accounted for at the population level through the use of this simplified representation. Our findings underline the potential importance of random factors in the apparent complexity of the mice's social structure. These results resonate in the general context of adaptive behaviour versus elementary environmental interactions. PMID:23209394

Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle.

PubMed

Stecher, Bärbel; Berry, David; Loy, Alexander

2013-09-01

The highly diverse intestinal microbiota forms a structured community engaged in constant communication with itself and its host and is characterized by extensive ecological interactions. A key benefit that the microbiota affords its host is its ability to protect against infections in a process termed colonization resistance (CR), which remains insufficiently understood. In this review, we connect basic concepts of CR with new insights from recent years and highlight key technological advances in the field of microbial ecology. We present a selection of statistical and bioinformatics tools used to generate hypotheses about synergistic and antagonistic interactions in microbial ecosystems from metagenomic datasets. We emphasize the importance of experimentally testing these hypotheses and discuss the value of gnotobiotic mouse models for investigating specific aspects related to microbiota-host-pathogen interactions in a well-defined experimental system. We further introduce new developments in the area of single-cell analysis using fluorescence in situ hybridization in combination with metabolic stable isotope labeling technologies for studying the in vivo activities of complex community members. These approaches promise to yield novel insights into the mechanisms of CR and intestinal ecophysiology in general, and give researchers the means to experimentally test hypotheses in vivo at varying levels of biological and ecological complexity. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Spontaneous ultra-weak photon emission in correlation to inflammatory metabolism and oxidative stress in a mouse model of collagen-induced arthritis.

PubMed

He, Min; van Wijk, Eduard; van Wietmarschen, Herman; Wang, Mei; Sun, Mengmeng; Koval, Slavik; van Wijk, Roeland; Hankemeier, Thomas; van der Greef, Jan

2017-03-01

The increasing prevalence of rheumatoid arthritis has driven the development of new approaches and technologies for investigating the pathophysiology of this devastating, chronic disease. From the perspective of systems biology, combining comprehensive personal data such as metabolomics profiling with ultra-weak photon emission (UPE) data may provide key information regarding the complex pathophysiology underlying rheumatoid arthritis. In this article, we integrated UPE with metabolomics-based technologies in order to investigate collagen-induced arthritis, a mouse model of rheumatoid arthritis, at the systems level, and we investigated the biological underpinnings of the complex dataset. Using correlation networks, we found that elevated inflammatory and ROS-mediated plasma metabolites are strongly correlated with a systematic reduction in amine metabolites, which is linked to muscle wasting in rheumatoid arthritis. We also found that increased UPE intensity is strongly linked to metabolic processes (with correlation co-efficiency |r| value >0.7), which may be associated with lipid oxidation that related to inflammatory and/or ROS-mediated processes. Together, these results indicate that UPE is correlated with metabolomics and may serve as a valuable tool for diagnosing chronic disease by integrating inflammatory signals at the systems level. Our correlation network analysis provides important and valuable information regarding the disease process from a system-wide perspective. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrated analysis identifies microRNA-195 as a suppressor of Hippo-YAP pathway in colorectal cancer.

PubMed

Sun, Min; Song, Haibin; Wang, Shuyi; Zhang, Chunxiao; Zheng, Liang; Chen, Fangfang; Shi, Dongdong; Chen, Yuanyuan; Yang, Chaogang; Xiang, Zhenxian; Liu, Qing; Wei, Chen; Xiong, Bin

2017-03-29

With persistent inconsistencies in colorectal cancer (CRC) miRNAs expression data, it is crucial to shift toward inclusion of a "pre-laboratory" integrated analysis to expedite effective precision medicine and translational research. Aberrant expression of hsa-miRNA-195 (miR-195) which is distinguished as a clinically noteworthy miRNA has previously been observed in multiple cancers, yet its role in CRC remains unclear. In this study, we performed an integrated analysis of seven CRC miRNAs expression datasets. The expression of miR-195 was validated in The Cancer Genome Atlas (TCGA) datasets, and an independent validation sample cohort. Colon cancer cells were transfected with miR-195 mimic and inhibitor, after which cell proliferation, colony formation, migration, invasion, and dual luciferase reporter were assayed. Xenograft mouse models were used to determine the role of miR-195 in CRC tumorigenicity in vivo. Four downregulated miRNAs (hsa-let-7a, hsa-miR-125b, hsa-miR-145, and hsa-miR-195) were demonstrated to be potentially useful diagnostic markers in the clinical setting. CRC patients with a decreased level of miR-195-5p in tumor tissues had significantly shortened survival as revealed by the TCGA colon adenocarcinoma (COAD) dataset and our CRC cohort. Overexpression of miR-195-5p in DLD1 and HCT116 cells repressed cell growth, colony formation, invasion, and migration. Inhibition of miR-195-5p function contributed to aberrant cell proliferation, migration, invasion, and epithelial mesenchymal transition (EMT). We identified miR-195-5p binding sites within the 3'-untranslated region (3'-UTR) of the human yes-associated protein (YAP) mRNA. YAP1 expression was downregulated after miR-195-5p treatment by qRT-PCR analysis and western blot. Four downregulated miRNAs were shown to be prime candidates for a panel of biomarkers with sufficient diagnostic accuracy for CRC in a clinical setting. Our integrated microRNA profiling approach identified miR-195-5p independently associated with prognosis in CRC. Our results demonstrated that miR-195-5p was a potent suppressor of YAP1, and miR-195-5p-mediated downregulation of YAP1 significantly reduced tumor development in a mouse CRC xenograft model. In the clinic, miR-195-5p can serve as a prognostic marker to predict the outcome of the CRC patients.

Protein Dynamics Associated with Failed and Rescued Learning in the Ts65Dn Mouse Model of Down Syndrome

PubMed Central

Ahmed, Md. Mahiuddin; Dhanasekaran, A. Ranjitha; Block, Aaron; Tong, Suhong; Costa, Alberto C. S.; Stasko, Melissa; Gardiner, Katheleen J.

2015-01-01

Down syndrome (DS) is caused by an extra copy of human chromosome 21 (Hsa21). Although it is the most common genetic cause of intellectual disability (ID), there are, as yet, no effective pharmacotherapies. The Ts65Dn mouse model of DS is trisomic for orthologs of ∼55% of Hsa21 classical protein coding genes. These mice display many features relevant to those seen in DS, including deficits in learning and memory (L/M) tasks requiring a functional hippocampus. Recently, the N-methyl-D-aspartate (NMDA) receptor antagonist, memantine, was shown to rescue performance of the Ts65Dn in several L/M tasks. These studies, however, have not been accompanied by molecular analyses. In previous work, we described changes in protein expression induced in hippocampus and cortex in control mice after exposure to context fear conditioning (CFC), with and without memantine treatment. Here, we extend this analysis to Ts65Dn mice, measuring levels of 85 proteins/protein modifications, including components of MAP kinase and MTOR pathways, and subunits of NMDA receptors, in cortex and hippocampus of Ts65Dn mice after failed learning in CFC and after learning was rescued by memantine. We show that, compared with wild type littermate controls, (i) of the dynamic responses seen in control mice in normal learning, >40% also occur in Ts65Dn in failed learning or are compensated by baseline abnormalities, and thus are considered necessary but not sufficient for successful learning, and (ii) treatment with memantine does not in general normalize the initial protein levels but instead induces direct and indirect responses in approximately half the proteins measured and results in normalization of the endpoint protein levels. Together, these datasets provide a first view of the complexities associated with pharmacological rescue of learning in the Ts65Dn. Extending such studies to additional drugs and mouse models of DS will aid in identifying pharmacotherapies for effective clinical trials. PMID:25793384

Compression of Born ratio for fluorescence molecular tomography/x-ray computed tomography hybrid imaging: methodology and in vivo validation.

PubMed

Mohajerani, Pouyan; Ntziachristos, Vasilis

2013-07-01

The 360° rotation geometry of the hybrid fluorescence molecular tomography/x-ray computed tomography modality allows for acquisition of very large datasets, which pose numerical limitations on the reconstruction. We propose a compression method that takes advantage of the correlation of the Born-normalized signal among sources in spatially formed clusters to reduce the size of system model. The proposed method has been validated using an ex vivo study and an in vivo study of a nude mouse with a subcutaneous 4T1 tumor, with and without inclusion of a priori anatomical information. Compression rates of up to two orders of magnitude with minimum distortion of reconstruction have been demonstrated, resulting in large reduction in weight matrix size and reconstruction time.

Predictive dose-based estimation of systemic exposure multiples in mouse and monkey relative to human for antisense oligonucleotides with 2'-o-(2-methoxyethyl) modifications.

PubMed

Yu, Rosie Z; Grundy, John S; Henry, Scott P; Kim, Tae-Won; Norris, Daniel A; Burkey, Jennifer; Wang, Yanfeng; Vick, Andrew; Geary, Richard S

2015-01-20

Evaluation of species differences and systemic exposure multiples (or ratios) in toxicological animal species versus human is an ongoing exercise during the course of drug development. The systemic exposure ratios are best estimated by directly comparing area under the plasma concentration-time curves (AUCs), and sometimes by comparing the dose administered, with the dose being adjusted either by body surface area (BSA) or body weight (BW). In this study, the association between AUC ratio and the administered dose ratio from animals to human were studied using a retrospective data-driven approach. The dataset included nine antisense oligonucleotides (ASOs) with 2'-O-(2-methoxyethyl) modifications, evaluated in two animal species (mouse and monkey) following single and repeated parenteral administrations. We found that plasma AUCs were similar between ASOs within the same species, and are predictable to human exposure using a single animal species, either mouse or monkey. Between monkey and human, the plasma exposure ratio can be predicted directly based on BW-adjusted dose ratios, whereas between mouse and human, the exposure ratio would be nearly fivefold lower in mouse compared to human based on BW-adjusted dose values. Thus, multiplying a factor of 5 for the mouse BW-adjusted dose would likely provide a reasonable AUC exposure estimate in human at steady-state.

Irreversible Inhibition of EGFR: Modeling the Combined Pharmacokinetic-Pharmacodynamic Relationship of Osimertinib and Its Active Metabolite AZ5104.

PubMed

Yates, James W T; Ashton, Susan; Cross, Darren; Mellor, Martine J; Powell, Steve J; Ballard, Peter

2016-10-01

Osimertinib (AZD9291) is a potent, selective, irreversible inhibitor of EGFR-sensitizing (exon 19 and L858R) and T790M-resistant mutation. In vivo, in the mouse, it is metabolized to an active des-methyl metabolite, AZ5104. To understand the therapeutic potential in patients, this study aimed to assess the relationship between osimertinib pharmacokinetics, the pharmacokinetics of the active metabolite, the pharmacodynamics of phosphorylated EGFR reduction, and efficacy in mouse xenograft models of EGFR-driven cancers, including two NSCLC lines. Osimertinib was dosed in xenografted models of EGFR-driven cancers. In one set of experiments, changes in phosphorylated EGFR were measured to confirm target engagement. In a second set of efficacy studies, the resulting changes in tumor volume over time after repeat dosing of osimertinib were observed. To account for the contributions of both molecules, a mathematical modeling approach was taken to integrate the resulting datasets. The model was able to describe the pharmacokinetics, pharmacodynamics, and efficacy in A431, PC9, and NCI-H1975 xenografts, with the differences in sensitivity described by the varying potency against wild-type, sensitizing, and T790M-mutant EGFR and the phosphorylated EGFR reduction required to reduce tumor volume. It was inferred that recovery of pEGFR is slower after chronic dosing due to reduced resynthesis. It was predicted and further demonstrated that although inhibition is irreversible, the resynthesis of EGFR is such that infrequent intermittent dosing is not as efficacious as once daily dosing. Mol Cancer Ther; 15(10); 2378-87. ©2016 AACR. ©2016 American Association for Cancer Research.

A novel Python program for implementation of quality control in the ELISA.

PubMed

Wetzel, Hanna N; Cohen, Cinder; Norman, Andrew B; Webster, Rose P

2017-09-01

The use of semi-quantitative assays such as the enzyme-linked immunosorbent assay (ELISA) requires stringent quality control of the data. However, such quality control is often lacking in academic settings due to unavailability of software and knowledge. Therefore, our aim was to develop methods to easily implement Levey-Jennings quality control methods. For this purpose, we created a program written in Python (a programming language with an open-source license) and tested it using a training set of ELISA standard curves quantifying the Fab fragment of an anti-cocaine monoclonal antibody in mouse blood. A colorimetric ELISA was developed using a goat anti-human anti-Fab capture method. Mouse blood samples spiked with the Fab fragment were tested against a standard curve of known concentrations of Fab fragment in buffer over a period of 133days stored at 4°C to assess stability of the Fab fragment and to generate a test dataset to assess the program. All standard curves were analyzed using our program to batch process the data and to generate Levey-Jennings control charts and statistics regarding the datasets. The program was able to identify values outside of two standard deviations, and this identification of outliers was consistent with the results of a two-way ANOVA. This program is freely available, which will help laboratories implement quality control methods, thus improving reproducibility within and between labs. We report here successful testing of the program with our training set and development of a method for quantification of the Fab fragment in mouse blood. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

NASA Astrophysics Data System (ADS)

Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

2017-12-01

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

The impact of simplified boundary conditions and aortic arch inclusion on CFD simulations in the mouse aorta: a comparison with mouse-specific reference data.

PubMed

Trachet, Bram; Bols, Joris; De Santis, Gianluca; Vandenberghe, Stefaan; Loeys, Bart; Segers, Patrick

2011-12-01

Computational fluid dynamics (CFD) simulations allow for calculation of a detailed flow field in the mouse aorta and can thus be used to investigate a potential link between local hemodynamics and disease development. To perform these simulations in a murine setting, one often needs to make assumptions (e.g. when mouse-specific boundary conditions are not available), but many of these assumptions have not been validated due to a lack of reference data. In this study, we present such a reference data set by combining high-frequency ultrasound and contrast-enhanced micro-CT to measure (in vivo) the time-dependent volumetric flow waveforms in the complete aorta (including seven major side branches) of 10 male ApoE -/- deficient mice on a C57Bl/6 background. In order to assess the influence of some assumptions that are commonly applied in literature, four different CFD simulations were set up for each animal: (i) imposing the measured volumetric flow waveforms, (ii) imposing the average flow fractions over all 10 animals, presented as a reference data set, (iii) imposing flow fractions calculated by Murray's law, and (iv) restricting the geometrical model to the abdominal aorta (imposing measured flows). We found that - even if there is sometimes significant variation in the flow fractions going to a particular branch - the influence of using average flow fractions on the CFD simulations is limited and often restricted to the side branches. On the other hand, Murray's law underestimates the fraction going to the brachiocephalic trunk and strongly overestimates the fraction going to the distal aorta, influencing the outcome of the CFD results significantly. Changing the exponential factor in Murray's law equation from 3 to 2 (as suggested by several authors in literature) yields results that correspond much better to those obtained imposing the average flow fractions. Restricting the geometrical model to the abdominal aorta did not influence the outcome of the CFD simulations. In conclusion, the presented reference dataset can be used to impose boundary conditions in the mouse aorta in future studies, keeping in mind that they represent a subsample of the total population, i.e., relatively old, non-diseased, male C57Bl/6 ApoE -/- mice.

Quantitative analysis of multiple high-resolution mass spectrometry images using chemometric methods: quantitation of chlordecone in mouse liver.

PubMed

Mohammadi, Saeedeh; Parastar, Hadi

2018-05-15

In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI). In this regard, quantification of chlordecone as a carcinogenic organochlorinated pesticide (C10Cll0O) in mouse liver using the matrix-assisted laser desorption ionization MSI (MALDI-MSI) method is used as a case study. The MSI datasets corresponded to 1, 5 and 10 days of mouse exposure to the standard chlordecone in the quantity range of 0 to 450 μg g-1. The binning approach in the m/z direction is used to group high resolution m/z values and to reduce the big data size. To consider the effect of bin size on the quality of results, three different bin sizes of 0.25, 0.5 and 1.0 were chosen. Afterwards, three-way MSI data arrays (two spatial and one m/z dimensions) for seven standards and four unknown samples were column-wise augmented with m/z values as the common mode. Then, these datasets were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) using proper constraints. The resolved mass spectra were used for identification of chlordecone in the presence of a complex background and interference. Additionally, the augmented spatial profiles were post-processed and 2D images for each component were obtained in calibration and unknown samples. The sum of these profiles was utilized to set the calibration curve and to obtain the analytical figures of merit (AFOMs). Inspection of the results showed that the lower bin size (i.e., 0.25) provides more accurate results. Finally, the obtained results by MCR for three datasets were compared with those of gas chromatography-mass spectrometry (GC-MS) and MALDI-MSI. The results showed that the MCR-assisted method gives a higher amount of chlordecone than MALDI-MSI and a lower amount than GC-MS. It is concluded that a combination of chemometric methods with MSI can be considered as an alternative way for MSI quantification.

Finding mouse models of human lymphomas and leukemia's using the Jackson laboratory mouse tumor biology database.

PubMed

Begley, Dale A; Sundberg, John P; Krupke, Debra M; Neuhauser, Steven B; Bult, Carol J; Eppig, Janan T; Morse, Herbert C; Ward, Jerrold M

2015-12-01

Many mouse models have been created to study hematopoietic cancer types. There are over thirty hematopoietic tumor types and subtypes, both human and mouse, with various origins, characteristics and clinical prognoses. Determining the specific type of hematopoietic lesion produced in a mouse model and identifying mouse models that correspond to the human subtypes of these lesions has been a continuing challenge for the scientific community. The Mouse Tumor Biology Database (MTB; http://tumor.informatics.jax.org) is designed to facilitate use of mouse models of human cancer by providing detailed histopathologic and molecular information on lymphoma subtypes, including expertly annotated, on line, whole slide scans, and providing a repository for storing information on and querying these data for specific lymphoma models. Copyright © 2015 Elsevier Inc. All rights reserved.

Mouse models rarely mimic the transcriptome of human neurodegenerative diseases: A systematic bioinformatics-based critique of preclinical models.

PubMed

Burns, Terry C; Li, Matthew D; Mehta, Swapnil; Awad, Ahmed J; Morgan, Alexander A

2015-07-15

Translational research for neurodegenerative disease depends intimately upon animal models. Unfortunately, promising therapies developed using mouse models mostly fail in clinical trials, highlighting uncertainty about how well mouse models mimic human neurodegenerative disease at the molecular level. We compared the transcriptional signature of neurodegeneration in mouse models of Alzheimer׳s disease (AD), Parkinson׳s disease (PD), Huntington׳s disease (HD) and amyotrophic lateral sclerosis (ALS) to human disease. In contrast to aging, which demonstrated a conserved transcriptome between humans and mice, only 3 of 19 animal models showed significant enrichment for gene sets comprising the most dysregulated up- and down-regulated human genes. Spearman׳s correlation analysis revealed even healthy human aging to be more closely related to human neurodegeneration than any mouse model of AD, PD, ALS or HD. Remarkably, mouse models frequently upregulated stress response genes that were consistently downregulated in human diseases. Among potential alternate models of neurodegeneration, mouse prion disease outperformed all other disease-specific models. Even among the best available animal models, conserved differences between mouse and human transcriptomes were found across multiple animal model versus human disease comparisons, surprisingly, even including aging. Relative to mouse models, mouse disease signatures demonstrated consistent trends toward preserved mitochondrial function protein catabolism, DNA repair responses, and chromatin maintenance. These findings suggest a more complex and multifactorial pathophysiology in human neurodegeneration than is captured through standard animal models, and suggest that even among conserved physiological processes such as aging, mice are less prone to exhibit neurodegeneration-like changes. This work may help explain the poor track record of mouse-based translational therapies for neurodegeneration and provides a path forward to critically evaluate and improve animal models of human disease. Copyright © 2015 Elsevier B.V. All rights reserved.

Phase 1 Free Air CO2 Enrichment Model-Data Synthesis (FACE-MDS): Meteorological Data

DOE Data Explorer

Norby, R. J.; Oren, R.; Boden, T. A. [Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL); De Kauwe, M. G.; Kim, D.; Medlyn, B. E.; Riggs, J. S.; Tharp, M. L.; Walker, A. P.; Yang, B.; Zaehle, S.

2015-01-01

These datasets comprise the meteorological, CO2 and N deposition data used to run models for the Duke and Oak Ridge FACE experiments. Phase 1 datasets are reproduced here for posterity and reproducibility although these meteorological datasets are superseded by the Phase 2 datasets. If you would like to use the meteorological datasets to run your own model or for any other purpose please use the Phase 2 datasets.

Phase 1 Free Air CO2 Enrichment Model-Data Synthesis (FACE-MDS): Model Output Data (2015)

DOE Data Explorer

Walker, A. P.; De Kauwe, M. G.; Medlyn, B. E.; Zaehle, S.; Asao, S.; Dietze, M.; El-Masri, B.; Hanson, P. J.; Hickler, T.; Jain, A.; Luo, Y.; Parton, W. J.; Prentice, I. C.; Ricciuto, D. M.; Thornton, P. E.; Wang, S.; Wang, Y -P; Warlind, D.; Weng, E.; Oren, R.; Norby, R. J.

2015-01-01

These datasets comprise the model output from phase 1 of the FACE-MDS. These include simulations of the Duke and Oak Ridge experiments and also idealised long-term (300 year) simulations at both sites (please see the modelling protocol for details). Included as part of this dataset are modelling and output protocols. The model datasets are formatted according to the output protocols. Phase 1 datasets are reproduced here for posterity and reproducibility although the model output for the experimental period have been somewhat superseded by the Phase 2 datasets.

Modulation of tumor fatty acids, through overexpression or loss of thyroid hormone responsive protein spot 14 is associated with altered growth and metastasis.

PubMed

Wellberg, Elizabeth A; Rudolph, Michael C; Lewis, Andrew S; Padilla-Just, Nuria; Jedlicka, Paul; Anderson, Steven M

2014-12-04

Spot14 (S14), encoded by the THRSP gene, regulates de novo fatty acid synthesis in the liver, adipose, and lactating mammary gland. We recently showed that S14 stimulated fatty acid synthase (FASN) activity in vitro, and increased the synthesis of fatty acids in mammary epithelial cells in vivo. Elevated de novo fatty acid synthesis is a distinguishing feature of many solid tumors compared with adjacent normal tissue. This characteristic is thought to be acquired during tumor progression, as rapidly proliferating cells have a heightened requirement for membrane phospholipids. Further, overexpression of FASN is sufficient to stimulate cell proliferation. While many studies have focused on the FASN enzyme in cancer biology, few studies have addressed the roles of proteins that modify FASN activity, such as S14. Tumor fatty acids were modulated using two mouse models, mouse mammary tumor virus (MMTV)-neu mice overexpressing S14 and MMTV-polyomavirus middle T antigen (PyMT) mice lacking S14, and associations between elevated or impaired fatty acid synthesis on tumor latency, growth, metastasis, and signaling pathways were investigated. We evaluated S14-dependent gene expression profiles in mouse tumors by microarray and used publicly available microarray datasets of human breast tumors. S14 overexpression in the MMTV-Neu transgenic model is associated with elevated medium-chain fatty acids, increased proliferation and a shorter tumor latency, but reduced tumor metastasis compared to controls. Loss of S14 in the MMTV-PyMT model decreased FASN activity and the synthesis of medium-chain fatty acids but did not alter tumor latency. Impaired fatty acid synthesis was associated with reduced solid tumor cell proliferation, the formation of cystic lesions in some animals, and decreased phosphorylation of Src and protein kinase B (Akt). Analysis of gene expression in these mouse and human tumors revealed a relationship between S14 status and the expression of genes associated with luminal epithelial differentiation. This study demonstrates a potential role for S14 in regulating mammary tumor growth and fatty acid synthesis in vivo. Furthermore, these results suggest that modulating the amount of medium chain fatty acids, by changing the levels of S14, has the potential to impact malignant mammary tumor phenotypes.

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

PubMed Central

Grassi, Elena; Damasco, Christian; Silengo, Lorenzo; Oti, Martin; Provero, Paolo; Di Cunto, Ferdinando

2008-01-01

Background Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. Methodology/Principal Findings We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. Conclusion Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. PMID:18369433

Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning

PubMed Central

Mogi, Masaki; Iwanami, Jun; Min, Li-Juan; Bai, Hui-Yu; Shan, Bao-Shuai; Kukida, Masayoshi; Kan-no, Harumi; Ikeda, Shuntaro; Higaki, Jitsuo; Horiuchi, Masatsugu

2018-01-01

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model. PMID:29415035

Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning.

PubMed

Higaki, Akinori; Mogi, Masaki; Iwanami, Jun; Min, Li-Juan; Bai, Hui-Yu; Shan, Bao-Shuai; Kukida, Masayoshi; Kan-No, Harumi; Ikeda, Shuntaro; Higaki, Jitsuo; Horiuchi, Masatsugu

2018-01-01

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents' spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model.

Leveraging business intelligence to make better decisions: Part III.

PubMed

Reimers, Mona

2014-01-01

Accounts receivable and scheduling datasets have been available to medical practices since the 1990s, and discrete medical records data have become available over the past few years. But the frustrations that arose from the difficulties in reporting data grew with each keyboard stroke and mouse click. With reporting mandated to meet changing payment models, measuring quality of care and medical outcomes, practice managers must find more efficient and effective methods of extracting and compiling the data they have in their systems. Taming the reporting beast and learning to effectively apply business intelligence (BI) tools will become an expected managerial proficiency in the next few years. Practice managers' roles are changing quickly, and they will be required to understand the meaning of their practice's data and craft ways to leverage that data toward a strategic advantage.

System parameters for erythropoiesis control model: Comparison of normal values in human and mouse model

NASA Technical Reports Server (NTRS)

1979-01-01

The computer model for erythropoietic control was adapted to the mouse system by altering system parameters originally given for the human to those which more realistically represent the mouse. Parameter values were obtained from a variety of literature sources. Using the mouse model, the mouse was studied as a potential experimental model for spaceflight. Simulation studies of dehydration and hypoxia were performed. A comparison of system parameters for the mouse and human models is presented. Aside from the obvious differences expected in fluid volumes, blood flows and metabolic rates, larger differences were observed in the following: erythrocyte life span, erythropoietin half-life, and normal arterial pO2.

Monitoring transcription initiation activities in rat and dog.

PubMed

Lizio, Marina; Mukarram, Abdul Kadir; Ohno, Mizuho; Watanabe, Shoko; Itoh, Masayoshi; Hasegawa, Akira; Lassmann, Timo; Severin, Jessica; Harshbarger, Jayson; Abugessaisa, Imad; Kasukawa, Takeya; Hon, Chung Chau; Carninci, Piero; Hayashizaki, Yoshihide; Forrest, Alistair R R; Kawaji, Hideya

2017-11-28

The promoter landscape of several non-human model organisms is far from complete. As a part of FANTOM5 data collection, we generated 13 profiles of transcription initiation activities in dog and rat aortic smooth muscle cells, mesenchymal stem cells and hepatocytes by employing CAGE (Cap Analysis of Gene Expression) technology combined with single molecule sequencing. Our analyses show that the CAGE profiles recapitulate known transcription start sites (TSSs) consistently, in addition to uncover novel TSSs. Our dataset can be thus used with high confidence to support gene annotation in dog and rat species. We identified 28,497 and 23,147 CAGE peaks, or promoter regions, for rat and dog respectively, and associated them to known genes. This approach could be seen as a standard method for improvement of existing gene models, as well as discovery of novel genes. Given that the FANTOM5 data collection includes dog and rat matched cell types in human and mouse as well, this data would also be useful for cross-species studies.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

Human mammary microenvironment better regulates the biology of human breast cancer in humanized mouse model.

PubMed

Zheng, Ming-Jie; Wang, Jue; Xu, Lu; Zha, Xiao-Ming; Zhao, Yi; Ling, Li-Jun; Wang, Shui

2015-02-01

During the past decades, many efforts have been made in mimicking the clinical progress of human cancer in mouse models. Previously, we developed a human breast tissue-derived (HB) mouse model. Theoretically, it may mimic the interactions between "species-specific" mammary microenvironment of human origin and human breast cancer cells. However, detailed evidences are absent. The present study (in vivo, cellular, and molecular experiments) was designed to explore the regulatory role of human mammary microenvironment in the progress of human breast cancer cells. Subcutaneous (SUB), mammary fat pad (MFP), and HB mouse models were developed for in vivo comparisons. Then, the orthotopic tumor masses from three different mouse models were collected for primary culture. Finally, the biology of primary cultured human breast cancer cells was compared by cellular and molecular experiments. Results of in vivo mouse models indicated that human breast cancer cells grew better in human mammary microenvironment. Cellular and molecular experiments confirmed that primary cultured human breast cancer cells from HB mouse model showed a better proliferative and anti-apoptotic biology than those from SUB to MFP mouse models. Meanwhile, primary cultured human breast cancer cells from HB mouse model also obtained the migratory and invasive biology for "species-specific" tissue metastasis to human tissues. Comprehensive analyses suggest that "species-specific" mammary microenvironment of human origin better regulates the biology of human breast cancer cells in our humanized mouse model of breast cancer, which is more consistent with the clinical progress of human breast cancer.

The α3β4* nicotinic ACh receptor subtype mediates physical dependence to morphine: mouse and human studies.

PubMed

Muldoon, P P; Jackson, K J; Perez, E; Harenza, J L; Molas, S; Rais, B; Anwar, H; Zaveri, N T; Maldonado, R; Maskos, U; McIntosh, J M; Dierssen, M; Miles, M F; Chen, X; De Biasi, M; Damaj, M I

2014-08-01

Recent data have indicated that α3β4* neuronal nicotinic (n) ACh receptors may play a role in morphine dependence. Here we investigated if nACh receptors modulate morphine physical withdrawal. To assess the role of α3β4* nACh receptors in morphine withdrawal, we used a genetic correlation approach using publically available datasets within the GeneNetwork web resource, genetic knockout and pharmacological tools. Male and female European-American (n = 2772) and African-American (n = 1309) subjects from the Study of Addiction: Genetics and Environment dataset were assessed for possible associations of polymorphisms in the 15q25 gene cluster and opioid dependence. BXD recombinant mouse lines demonstrated an increased expression of α3, β4 and α5 nACh receptor mRNA in the forebrain and midbrain, which significantly correlated with increased defecation in mice undergoing morphine withdrawal. Mice overexpressing the gene cluster CHRNA5/A3/B4 exhibited increased somatic signs of withdrawal. Furthermore, α5 and β4 nACh receptor knockout mice expressed decreased somatic withdrawal signs compared with their wild-type counterparts. Moreover, selective α3β4* nACh receptor antagonists, α-conotoxin AuIB and AT-1001, attenuated somatic signs of morphine withdrawal in a dose-related manner. In addition, two human datasets revealed a protective role for variants in the CHRNA3 gene, which codes for the α3 nACh receptor subunit, in opioid dependence and withdrawal. In contrast, we found that the α4β2* nACh receptor subtype is not involved in morphine somatic withdrawal signs. Overall, our findings suggest an important role for the α3β4* nACh receptor subtype in morphine physical dependence. © 2014 The British Pharmacological Society.

Phenobarbital and propiconazole toxicogenomic profiles in mice show major similarities consistent with the key role that constitutive androstane receptor (CAR) activation plays in their mode of action

PubMed Central

Currie, Richard A.; Peffer, Richard C.; Goetz, Amber K.; Omiecinski, Curtis J.; Goodman, Jay I.

2014-01-01

Toxicogenomics (TGx) is employed frequently to investigate underlying molecular mechanisms of the compound of interest and, thus, has become an aid to mode of action determination. However, the results and interpretation of a TGx dataset are influenced by the experimental design and methods of analysis employed. This article describes an evaluation and reanalysis, by two independent laboratories, of previously published TGx mouse liver microarray data for a triazole fungicide, propiconazole (PPZ), and the anticonvulsant drug phenobarbital (PB). Propiconazole produced an increase incidence of liver tumors in male CD-1 mice only at a dose that exceeded the maximum tolerated dose (2500 ppm). Firstly, we illustrate how experimental design differences between two in vivo studies with PPZ and PB may impact the comparisons of TGx results. Secondly, we demonstrate that different researchers using different pathway analysis tools can come to different conclusions on specific mechanistic pathways, even when using the same datasets. Finally, despite these differences the results across three different analyses also show a striking degree of similarity observed for PPZ and PB treated livers when the expression data are viewed as major signaling pathways and cell processes affected. Additional studies described here show that the postulated key event of hepatocellular proliferation was observed in CD-1 mice for both PPZ and PB, and that PPZ is also a potent activator of the mouse CAR nuclear receptor. Thus, with regard to the events which are hallmarks of CAR-induced effects that are key events in the mode of action (MOA) of mouse liver carcinogenesis with PB, PPZ-induced tumors can be viewed as being promoted by a similar PB-like CAR-dependent MOA. PMID:24675475

GeNemo: a search engine for web-based functional genomic data.

PubMed

Zhang, Yongqing; Cao, Xiaoyi; Zhong, Sheng

2016-07-08

A set of new data types emerged from functional genomic assays, including ChIP-seq, DNase-seq, FAIRE-seq and others. The results are typically stored as genome-wide intensities (WIG/bigWig files) or functional genomic regions (peak/BED files). These data types present new challenges to big data science. Here, we present GeNemo, a web-based search engine for functional genomic data. GeNemo searches user-input data against online functional genomic datasets, including the entire collection of ENCODE and mouse ENCODE datasets. Unlike text-based search engines, GeNemo's searches are based on pattern matching of functional genomic regions. This distinguishes GeNemo from text or DNA sequence searches. The user can input any complete or partial functional genomic dataset, for example, a binding intensity file (bigWig) or a peak file. GeNemo reports any genomic regions, ranging from hundred bases to hundred thousand bases, from any of the online ENCODE datasets that share similar functional (binding, modification, accessibility) patterns. This is enabled by a Markov Chain Monte Carlo-based maximization process, executed on up to 24 parallel computing threads. By clicking on a search result, the user can visually compare her/his data with the found datasets and navigate the identified genomic regions. GeNemo is available at www.genemo.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization.

PubMed

Roberts, David W; Patlewicz, Grace; Kern, Petra S; Gerberick, Frank; Kimber, Ian; Dearman, Rebecca J; Ryan, Cindy A; Basketter, David A; Aptula, Aynur O

2007-07-01

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

PubMed Central

Köhler, Sebastian; Doelken, Sandra C.; Mungall, Christopher J.; Bauer, Sebastian; Firth, Helen V.; Bailleul-Forestier, Isabelle; Black, Graeme C. M.; Brown, Danielle L.; Brudno, Michael; Campbell, Jennifer; FitzPatrick, David R.; Eppig, Janan T.; Jackson, Andrew P.; Freson, Kathleen; Girdea, Marta; Helbig, Ingo; Hurst, Jane A.; Jähn, Johanna; Jackson, Laird G.; Kelly, Anne M.; Ledbetter, David H.; Mansour, Sahar; Martin, Christa L.; Moss, Celia; Mumford, Andrew; Ouwehand, Willem H.; Park, Soo-Mi; Riggs, Erin Rooney; Scott, Richard H.; Sisodiya, Sanjay; Vooren, Steven Van; Wapner, Ronald J.; Wilkie, Andrew O. M.; Wright, Caroline F.; Vulto-van Silfhout, Anneke T.; de Leeuw, Nicole; de Vries, Bert B. A.; Washingthon, Nicole L.; Smith, Cynthia L.; Westerfield, Monte; Schofield, Paul; Ruef, Barbara J.; Gkoutos, Georgios V.; Haendel, Melissa; Smedley, Damian; Lewis, Suzanna E.; Robinson, Peter N.

2014-01-01

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online. PMID:24217912

Multi-level omics analysis in a murine model of dystrophin loss and therapeutic restoration.

PubMed

Roberts, Thomas C; Johansson, Henrik J; McClorey, Graham; Godfrey, Caroline; Blomberg, K Emelie M; Coursindel, Thibault; Gait, Michael J; Smith, C I Edvard; Lehtiö, Janne; El Andaloussi, Samir; Wood, Matthew J A

2015-12-01

Duchenne muscular dystrophy (DMD) is a classical monogenic disorder, a model disease for genomic studies and a priority candidate for regenerative medicine and gene therapy. Although the genetic cause of DMD is well known, the molecular pathogenesis of disease and the response to therapy are incompletely understood. Here, we describe analyses of protein, mRNA and microRNA expression in the tibialis anterior of the mdx mouse model of DMD. Notably, 3272 proteins were quantifiable and 525 identified as differentially expressed in mdx muscle (P < 0.01). Therapeutic restoration of dystrophin by exon skipping induced widespread shifts in protein and mRNA expression towards wild-type expression levels, whereas the miRNome was largely unaffected. Comparison analyses between datasets showed that protein and mRNA ratios were only weakly correlated (r = 0.405), and identified a multitude of differentially affected cellular pathways, upstream regulators and predicted miRNA-target interactions. This study provides fundamental new insights into gene expression and regulation in dystrophic muscle. © The Author 2015. Published by Oxford University Press.

Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

PubMed

Zeng, Huawei; Grapov, Dmitry; Jackson, Matthew I; Fahrmann, Johannes; Fiehn, Oliver; Combs, Gerald F

2015-09-11

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed.

Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces

PubMed Central

Zeng, Huawei; Grapov, Dmitry; Jackson, Matthew I.; Fahrmann, Johannes; Fiehn, Oliver; Combs, Gerald F.

2015-01-01

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed. PMID:26378591

A Morpholino-based screen to identify novel genes involved in craniofacial morphogenesis

PubMed Central

Melvin, Vida Senkus; Feng, Weiguo; Hernandez-Lagunas, Laura; Artinger, Kristin Bruk; Williams, Trevor

2014-01-01

BACKGROUND The regulatory mechanisms underpinning facial development are conserved between diverse species. Therefore, results from model systems provide insight into the genetic causes of human craniofacial defects. Previously, we generated a comprehensive dataset examining gene expression during development and fusion of the mouse facial prominences. Here, we used this resource to identify genes that have dynamic expression patterns in the facial prominences, but for which only limited information exists concerning developmental function. RESULTS This set of ~80 genes was used for a high throughput functional analysis in the zebrafish system using Morpholino gene knockdown technology. This screen revealed three classes of cranial cartilage phenotypes depending upon whether knockdown of the gene affected the neurocranium, viscerocranium, or both. The targeted genes that produced consistent phenotypes encoded proteins linked to transcription (meis1, meis2a, tshz2, vgll4l), signaling (pkdcc, vlk, macc1, wu:fb16h09), and extracellular matrix function (smoc2). The majority of these phenotypes were not altered by reduction of p53 levels, demonstrating that both p53 dependent and independent mechanisms were involved in the craniofacial abnormalities. CONCLUSIONS This Morpholino-based screen highlights new genes involved in development of the zebrafish craniofacial skeleton with wider relevance to formation of the face in other species, particularly mouse and human. PMID:23559552

Research on Zheng Classification Fusing Pulse Parameters in Coronary Heart Disease

PubMed Central

Guo, Rui; Wang, Yi-Qin; Xu, Jin; Yan, Hai-Xia; Yan, Jian-Jun; Li, Fu-Feng; Xu, Zhao-Xia; Xu, Wen-Jie

2013-01-01

This study was conducted to illustrate that nonlinear dynamic variables of Traditional Chinese Medicine (TCM) pulse can improve the performances of TCM Zheng classification models. Pulse recordings of 334 coronary heart disease (CHD) patients and 117 normal subjects were collected in this study. Recurrence quantification analysis (RQA) was employed to acquire nonlinear dynamic variables of pulse. TCM Zheng models in CHD were constructed, and predictions using a novel multilabel learning algorithm based on different datasets were carried out. Datasets were designed as follows: dataset1, TCM inquiry information including inspection information; dataset2, time-domain variables of pulse and dataset1; dataset3, RQA variables of pulse and dataset1; and dataset4, major principal components of RQA variables and dataset1. The performances of the different models for Zheng differentiation were compared. The model for Zheng differentiation based on RQA variables integrated with inquiry information had the best performance, whereas that based only on inquiry had the worst performance. Meanwhile, the model based on time-domain variables of pulse integrated with inquiry fell between the above two. This result showed that RQA variables of pulse can be used to construct models of TCM Zheng and improve the performance of Zheng differentiation models. PMID:23737839

The Mouse Tumor Biology Database: A Comprehensive Resource for Mouse Models of Human Cancer.

PubMed

Krupke, Debra M; Begley, Dale A; Sundberg, John P; Richardson, Joel E; Neuhauser, Steven B; Bult, Carol J

2017-11-01

Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67-70. ©2017 AACR . ©2017 American Association for Cancer Research.

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

NASA Technical Reports Server (NTRS)

Alacron, Vladimir J.; Nigro, Joseph D.; McAnally, William H.; OHara, Charles G.; Engman, Edwin Ted; Toll, David

2011-01-01

This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

Online Visualization and Analysis of Merged Global Geostationary Satellite Infrared Dataset

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, D.; Leptoukh, G.; Mehta, A.

2008-01-01

The NASA Goddard Earth Sciences Data Information Services Center (GES DISC) is home of Tropical Rainfall Measuring Mission (TRMM) data archive. The global merged IR product also known as the NCEP/CPC 4-km Global (60 degrees N - 60 degrees S) IR Dataset, is one of TRMM ancillary datasets. They are globally merged (60 degrees N - 60 degrees S) pixel-resolution (4 km) IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 and GMS). The availability of data from METEOSAT-5, which is located at 63E at the present time, yields a unique opportunity for total global (60 degrees N- 60 degrees S) coverage. The GES DISC has collected over 8 years of the data beginning from February of 2000. This high temporal resolution dataset can not only provide additional background information to TRMM and other satellite missions, but also allow observing a wide range of meteorological phenomena from space, such as, mesoscale convection systems, tropical cyclones, hurricanes, etc. The dataset can also be used to verify model simulations. Despite that the data can be downloaded via ftp, however, its large volume poses a challenge for many users. A single file occupies about 70 MB disk space and there is a total of approximately 73,000 files (approximately 4.5 TB) for the past 8 years. In order to facilitate data access, we have developed a web prototype to allow users to conduct online visualization and analysis of this dataset. With a web browser and few mouse clicks, users can have a full access to over 8 year and over 4.5 TB data and generate black and white IR imagery and animation without downloading any software and data. In short, you can make your own images! Basic functions include selection of area of interest, single imagery or animation, a time skip capability for different temporal resolution and image size. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. The prototype will be integrated into GIOVANNI and existing GIOVANNI capabilities, such as, data download, Google Earth KMZ, etc will be available. Users will also be able to access other data products in the GIOVANNI family.

Comparative Analysis of Mitochondrial N-Termini from Mouse, Human, and Yeast *

PubMed Central

Clauser, Karl R.; Shen, Hongying; Kamer, Kimberli J.; Wells, James A.

2017-01-01

The majority of mitochondrial proteins are encoded in the nuclear genome, translated in the cytoplasm, and directed to the mitochondria by an N-terminal presequence that is cleaved upon import. Recently, N-proteome catalogs have been generated for mitochondria from yeast and from human U937 cells. Here, we applied the subtiligase method to determine N-termini for 327 proteins in mitochondria isolated from mouse liver and kidney. Comparative analysis between mitochondrial N-termini from mouse, human, and yeast proteins shows that whereas presequences are poorly conserved at the sequence level, other presequence properties are extremely conserved, including a length of ∼20–60 amino acids, a net charge between +3 to +6, and the presence of stabilizing amino acids at the N-terminus of mature proteins that follow the N-end rule from bacteria. As in yeast, ∼80% of mouse presequence cleavage sites match canonical motifs for three mitochondrial peptidases (MPP, Icp55, and Oct1), whereas the remainder do not match any known peptidase motifs. We show that mature mitochondrial proteins often exist with a spectrum of N-termini, consistent with a model of multiple cleavage events by MPP and Icp55. In addition to analysis of canonical targeting presequences, our N-terminal dataset allows the exploration of other cleavage events and provides support for polypeptide cleavage into two distinct enzymes (Hsd17b4), protein cleavages key for signaling (Oma1, Opa1, Htra2, Mavs, and Bcs2l13), and in several cases suggests novel protein isoforms (Scp2, Acadm, Adck3, Hsdl2, Dlst, and Ogdh). We present an integrated catalog of mammalian mitochondrial N-termini that can be used as a community resource to investigate individual proteins, to elucidate mechanisms of mammalian mitochondrial processing, and to allow researchers to engineer tags distally to the presequence cleavage. PMID:28122942

Urine cytokine and chemokine levels predict urinary tract infection severity independent of uropathogen, urine bacterial burden, host genetics, and host age.

PubMed

Armbruster, Chelsie E; Smith, Sara N; Mody, Lona; Mobley, Harry L T

2018-06-11

Urinary tract infections (UTIs) are among the most common infections worldwide. Diagnosing UTIs in older adults poses a significant challenge as asymptomatic colonization is common. Identification of a non-invasive profile that predicts likelihood of progressing from urine colonization to severe disease would provide a significant advantage in clinical practice. We monitored colonization susceptibility, disease severity, and immune response to two uropathogens in two mouse strains across three age groups to identify predictors of infection outcome. Proteus mirabilis caused more severe disease than Escherichia coli, regardless of mouse strain or age, and was associated with differences in IL-1β, IFN-β, CXCL5 (LIX), CCL5 (RANTES), and CCL2 (MCP-1). In comparing the response to infection across age groups, mature adult mice were better able to control colonization and prevent progression to kidney colonization and bacteremia than young or aged mice, regardless of mouse strain or bacterial species, and this was associated with differences in IL-23, CXCL1, and CCL5. A bimodal distribution was noted for urine colonization, which was strongly associated with bladder CFUs and the magnitude of the immune response but independent of age or disease severity. To determine the value of urine cytokine and chemokine levels for predicting severe disease, all infection datasets were combined and subjected to a series of logistic regressions. A multivariate model incorporating IL-1β, CXCL1, and CCL2 had strong predictive value for identifying mice that did not develop kidney colonization or bacteremia, regardless of mouse genetic background, age, infecting bacterial species, or urine bacterial burden. In conclusion, urine cytokine profiles could potentially serve as a non-invasive decision-support tool in clinical practice and contribute to antimicrobial stewardship. Copyright © 2018 American Society for Microbiology.

Drug discovery in prostate cancer mouse models.

PubMed

Valkenburg, Kenneth C; Pienta, Kenneth J

2015-01-01

The mouse is an important, though imperfect, organism with which to model human disease and to discover and test novel drugs in a preclinical setting. Many experimental strategies have been used to discover new biological and molecular targets in the mouse, with the hopes of translating these discoveries into novel drugs to treat prostate cancer in humans. Modeling prostate cancer in the mouse, however, has been challenging, and often drugs that work in mice have failed in human trials. The authors discuss the similarities and differences between mice and men; the types of mouse models that exist to model prostate cancer; practical questions one must ask when using a mouse as a model; and potential reasons that drugs do not often translate to humans. They also discuss the current value in using mouse models for drug discovery to treat prostate cancer and what needs are still unmet in field. With proper planning and following practical guidelines by the researcher, the mouse is a powerful experimental tool. The field lacks genetically engineered metastatic models, and xenograft models do not allow for the study of the immune system during the metastatic process. There remain several important limitations to discovering and testing novel drugs in mice for eventual human use, but these can often be overcome. Overall, mouse modeling is an essential part of prostate cancer research and drug discovery. Emerging technologies and better and ever-increasing forms of communication are moving the field in a hopeful direction.

Orthology for comparative genomics in the mouse genome database.

PubMed

Dolan, Mary E; Baldarelli, Richard M; Bello, Susan M; Ni, Li; McAndrews, Monica S; Bult, Carol J; Kadin, James A; Richardson, Joel E; Ringwald, Martin; Eppig, Janan T; Blake, Judith A

2015-08-01

The mouse genome database (MGD) is the model organism database component of the mouse genome informatics system at The Jackson Laboratory. MGD is the international data resource for the laboratory mouse and facilitates the use of mice in the study of human health and disease. Since its beginnings, MGD has included comparative genomics data with a particular focus on human-mouse orthology, an essential component of the use of mouse as a model organism. Over the past 25 years, novel algorithms and addition of orthologs from other model organisms have enriched comparative genomics in MGD data, extending the use of orthology data to support the laboratory mouse as a model of human biology. Here, we describe current comparative data in MGD and review the history and refinement of orthology representation in this resource.

Genetically Engineered Mouse Models for Studying Inflammatory Bowel Disease

PubMed Central

Mizoguchi, Atsushi; Takeuchi, Takahito; Himuro, Hidetomo; Okada, Toshiyuki; Mizoguchi, Emiko

2015-01-01

Inflammatory bowel disease (IBD) is a chronic intestinal inflammatory condition that is mediated by very complex mechanisms controlled by genetic, immune, and environmental factors. More than 74 kinds of genetically engineered mouse strains have been established since 1993 for studying IBD. Although mouse models cannot fully reflect human IBD, they have provided significant contributions for not only understanding the mechanism, but also developing new therapeutic means for IBD. Indeed, 20 kinds of genetically engineered mouse models carry the susceptibility genes identified in human IBD, and the functions of some other IBD susceptibility genes have also been dissected out using mouse models. Cutting-edge technologies such as cell-specific and inducible knockout systems, which were recently employed to mouse IBD models, have further enhanced the ability of investigators to provide important and unexpected rationales for developing new therapeutic strategies for IBD. In this review article, we briefly introduce 74 kinds of genetically engineered mouse models that spontaneously develop intestinal inflammation. PMID:26387641

Natural disease history of the dy2J mouse model of laminin α2 (merosin)-deficient congenital muscular dystrophy.

PubMed

Pasteuning-Vuhman, S; Putker, K; Tanganyika-de Winter, C L; Boertje-van der Meulen, J W; van Vliet, L; Overzier, M; Plomp, J J; Aartsma-Rus, A; van Putten, M

2018-01-01

Merosin deficient congenital muscular dystrophy 1A (MDC1A) is a very rare autosomal recessive disorder caused by mutations in the LAMA2 gene leading to severe and progressive muscle weakness and atrophy. Although over 350 causative mutations have been identified for MDC1A, no treatment is yet available. There are many therapeutic approaches in development, but the lack of natural history data of the mouse model and standardized outcome measures makes it difficult to transit these pre-clinical findings to clinical trials. Therefore, in the present study, we collected natural history data and assessed pre-clinical outcome measures for the dy2J/dy2J mouse model using standardized operating procedures available from the TREAT-NMD Alliance. Wild type and dy2J/dy2J mice were subjected to five different functional tests from the age of four to 32 weeks. Non-tested control groups were taken along to assess whether the functional test regime interfered with muscle pathology. Respiratory function, body weights and creatine kinase levels were recorded. Lastly, skeletal muscles were collected for further histopathological and gene expression analyses. Muscle function of dy2J/dy2J mice was severely impaired at four weeks of age and all mice lost the ability to use their hind limbs. Moreover, respiratory function was altered in dy2J/dy2J mice. Interestingly, the respiration rate was decreased and declined with age, whereas the respiration amplitude was increased in dy2J/dy2J mice when compared to wild type mice. Creatine kinase levels were comparable to wild type mice. Muscle histopathology and gene expression analysis revealed that there was a specific regional distribution pattern of muscle damage in dy2J/dy2J mice. Gastrocnemius appeared to be the most severely affected muscle with a high proportion of atrophic fibers, increased fibrosis and inflammation. By contrast, triceps was affected moderately and diaphragm only mildly. Our study presents a complete natural history dataset which can be used in setting up standardized studies in dy2J/dy2J mice.

Efficacy of Alteplase® in a mouse model of acute ischemic stroke: a retrospective pooled analysis

PubMed Central

Orset, Cyrille; Haelewyn, Benoit; Allan, Stuart M.; Ansar, Saema; Campos, Francesco; Cho, Tae Hee; Durand, Anne; El Amki, Mohamad; Fatar, Marc; Garcia-Yébenes, Isaac; Gauberti, Maxime; Grudzenski, Saskia; Lizasoain, Ignacio; Lo, Eng; Macrez, Richard; Margaill, Isabelle; Maysami, Samaneh; Meairs, Stephen; Nighoghossian, Norbert; Orbe, Josune; Paramo, Jose Antonio; Parienti, Jean-Jacques; Rothwell, Nancy J.; Rubio, Marina; Waeber, Christian; Young, Alan R.

2016-01-01

Background and purpose The debate over the fact that experimental drugs proposed for the treatment of stroke fail in the translation to the clinical situation, has attracted considerable attention in the literature. In this context, we present a retrospective pooled analysis of a large dataset from pre-clinical studies, in order to examine the effects of early versus late administration of intravenous recombinant tissue type plasminogen activator (rt-PA). Methods We collected data from 26 individual studies from 9 international centers (13 researchers, 716 animals) that compared rt-PA to controls, in a unique mouse model of thromboembolic stroke induced by an in situ injection of thrombin into the middle cerebral artery. Studies were classified into early (<3h) versus late (≥3h) drug administration. Final infarct volumes, assessed by histology or MRI, were compared in each study and the absolute differences were pooled in a random-effect meta-analysis. The influence of time of administration was tested. Results When compared to saline controls, early rt-PA administration was associated with a significant benefit (absolute difference = −6.63 mm3; 95%CI, −9.08 to −4.17; I2=76%) whereas late rt-PA treatment showed a deleterious effect (+5.06 mm3; 95%CI, +2.78 to +7.34; I2=42%, Pint<0.00001). Results remained unchanged following subgroup analyses. Conclusion Our results provide the basis needed for the design of future pre-clinical studies on recanalization therapies using this model of thromboembolic stroke in mice. The power analysis reveals that a multi-center trial would require 123 animals per group instead of 40 for a single center trial. PMID:27032444

Heart-rate sensitive optical coherence angiography for measuring vascular changes due to posttraumatic brain injury in mice

NASA Astrophysics Data System (ADS)

Tremoleda, Jordi L.; Alvarez, Karl; Aden, Abdirahman; Donnan, Robert; Michael-Titus, Adina T.; Tomlins, Peter H.

2017-12-01

Traumatic brain injury (TBI) results in direct vascular disruption, triggering edema, and reduction in cerebral blood flow. Therefore, understanding the pathophysiology of brain microcirculation following TBI is important for the development of effective therapies. Optical coherence angiography (OCA) is a promising tool for evaluating TBI in rodent models. We develop an approach to OCA that uses the heart-rate frequency to discriminate between static tissue and vasculature. This method operates on intensity data and is therefore not phase sensitive. Furthermore, it does not require spatial overlap of voxels and thus can be applied to pre-existing datasets for which oversampling may not have been explicitly considered. Heart-rate sensitive OCA was developed for dynamic assessment of mouse microvasculature post-TBI. Results show changes occurring at 5-min intervals within the first 50 min of injury.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

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

Zhou, Jian-Ying; Chen, Lijun; Zhang, Bai

The identification of protein biomarkers requires large-scale analysis of human specimens to achieve statistical significance. In this study, we evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification) based quantitative proteomics strategy using one channel for universal normalization across all samples. A total of 307 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating 107 one-dimensional (1D) LC-MS/MS datasets and 8 offline two-dimensional (2D) LC-MS/MS datasets (25 fractions for each set) for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assessmore » the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we developed a quantification confidence score based on the quality of each peptide-spectrum match (PSM) to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS datasets collected over a 16 month period.« less

Identification of fungi in shotgun metagenomics datasets

PubMed Central

Donovan, Paul D.; Gonzalez, Gabriel; Higgins, Desmond G.

2018-01-01

Metagenomics uses nucleic acid sequencing to characterize species diversity in different niches such as environmental biomes or the human microbiome. Most studies have used 16S rRNA amplicon sequencing to identify bacteria. However, the decreasing cost of sequencing has resulted in a gradual shift away from amplicon analyses and towards shotgun metagenomic sequencing. Shotgun metagenomic data can be used to identify a wide range of species, but have rarely been applied to fungal identification. Here, we develop a sequence classification pipeline, FindFungi, and use it to identify fungal sequences in public metagenome datasets. We focus primarily on animal metagenomes, especially those from pig and mouse microbiomes. We identified fungi in 39 of 70 datasets comprising 71 fungal species. At least 11 pathogenic species with zoonotic potential were identified, including Candida tropicalis. We identified Pseudogymnoascus species from 13 Antarctic soil samples initially analyzed for the presence of bacteria capable of degrading diesel oil. We also show that Candida tropicalis and Candida loboi are likely the same species. In addition, we identify several examples where contaminating DNA was erroneously included in fungal genome assemblies. PMID:29444186

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

Glycan array data management at Consortium for Functional Glycomics.

PubMed

Venkataraman, Maha; Sasisekharan, Ram; Raman, Rahul

2015-01-01

Glycomics or the study of structure-function relationships of complex glycans has reshaped post-genomics biology. Glycans mediate fundamental biological functions via their specific interactions with a variety of proteins. Recognizing the importance of glycomics, large-scale research initiatives such as the Consortium for Functional Glycomics (CFG) were established to address these challenges. Over the past decade, the Consortium for Functional Glycomics (CFG) has generated novel reagents and technologies for glycomics analyses, which in turn have led to generation of diverse datasets. These datasets have contributed to understanding glycan diversity and structure-function relationships at molecular (glycan-protein interactions), cellular (gene expression and glycan analysis), and whole organism (mouse phenotyping) levels. Among these analyses and datasets, screening of glycan-protein interactions on glycan array platforms has gained much prominence and has contributed to cross-disciplinary realization of the importance of glycomics in areas such as immunology, infectious diseases, cancer biomarkers, etc. This manuscript outlines methodologies for capturing data from glycan array experiments and online tools to access and visualize glycan array data implemented at the CFG.

Transcriptome analyses of adult mouse brain reveal enrichment of lncRNAs in specific brain regions and neuronal populations

PubMed Central

Kadakkuzha, Beena M.; Liu, Xin-An; McCrate, Jennifer; Shankar, Gautam; Rizzo, Valerio; Afinogenova, Alina; Young, Brandon; Fallahi, Mohammad; Carvalloza, Anthony C.; Raveendra, Bindu; Puthanveettil, Sathyanarayanan V.

2015-01-01

Despite the importance of the long non-coding RNAs (lncRNAs) in regulating biological functions, the expression profiles of lncRNAs in the sub-regions of the mammalian brain and neuronal populations remain largely uncharacterized. By analyzing RNASeq datasets, we demonstrate region specific enrichment of populations of lncRNAs and mRNAs in the mouse hippocampus and pre-frontal cortex (PFC), the two major regions of the brain involved in memory storage and neuropsychiatric disorders. We identified 2759 lncRNAs and 17,859 mRNAs in the hippocampus and 2561 lncRNAs and 17,464 mRNAs expressed in the PFC. The lncRNAs identified correspond to ~14% of the transcriptome of the hippocampus and PFC and ~70% of the lncRNAs annotated in the mouse genome (NCBIM37) and are localized along the chromosomes as varying numbers of clusters. Importantly, we also found that a few of the tested lncRNA-mRNA pairs that share a genomic locus display specific co-expression in a region-specific manner. Furthermore, we find that sub-regions of the brain and specific neuronal populations have characteristic lncRNA expression signatures. These results reveal an unexpected complexity of the lncRNA expression in the mouse brain. PMID:25798087

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

PubMed Central

Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. Results In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Conclusion Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

Topic modeling for cluster analysis of large biological and medical datasets.

PubMed

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Genome-wide expression profiling of five mouse models identifies similarities and differences with human psoriasis.

PubMed

Swindell, William R; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P; Voorhees, John J; Elder, James T; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P; DiGiovanni, John; Pittelkow, Mark R; Ward, Nicole L; Gudjonsson, Johann E

2011-04-04

Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis.

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

PubMed Central

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. PMID:25427176

Toward computational cumulative biology by combining models of biological datasets.

PubMed

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Analysis of PNGase F-resistant N-glycopeptides using SugarQb for Proteome Discoverer 2.1 reveals cryptic substrate specificities.

PubMed

Stadlmann, Johannes; Hoi, David M; Taubenschmid, Jasmin; Mechtler, Karl; Penninger, Josef M

2018-05-18

SugarQb (www.imba.oeaw.ac.at/sugarqb) is a freely available collection of computational tools for the automated identification of intact glycopeptides from high-resolution HCD MS/MS data-sets in the Proteome Discoverer environment. We report the migration of SugarQb to the latest and free version of Proteome Discoverer 2.1, and apply it to the analysis of PNGase F-resistant N-glycopeptides from mouse embryonic stem cells. The analysis of intact glycopeptides highlights unexpected technical limitations to PNGase F-dependent glycoproteomic workflows at the proteome level, and warrants a critical re-interpretation of seminal data-sets in the context of N-glycosylation-site prediction. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A large dataset of protein dynamics in the mammalian heart proteome.

PubMed

Lau, Edward; Cao, Quan; Ng, Dominic C M; Bleakley, Brian J; Dincer, T Umut; Bot, Brian M; Wang, Ding; Liem, David A; Lam, Maggie P Y; Ge, Junbo; Ping, Peipei

2016-03-15

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems.

Sensitivity of a numerical wave model on wind re-analysis datasets

NASA Astrophysics Data System (ADS)

Lavidas, George; Venugopal, Vengatesan; Friedrich, Daniel

2017-03-01

Wind is the dominant process for wave generation. Detailed evaluation of metocean conditions strengthens our understanding of issues concerning potential offshore applications. However, the scarcity of buoys and high cost of monitoring systems pose a barrier to properly defining offshore conditions. Through use of numerical wave models, metocean conditions can be hindcasted and forecasted providing reliable characterisations. This study reports the sensitivity of wind inputs on a numerical wave model for the Scottish region. Two re-analysis wind datasets with different spatio-temporal characteristics are used, the ERA-Interim Re-Analysis and the CFSR-NCEP Re-Analysis dataset. Different wind products alter results, affecting the accuracy obtained. The scope of this study is to assess different available wind databases and provide information concerning the most appropriate wind dataset for the specific region, based on temporal, spatial and geographic terms for wave modelling and offshore applications. Both wind input datasets delivered results from the numerical wave model with good correlation. Wave results by the 1-h dataset have higher peaks and lower biases, in expense of a high scatter index. On the other hand, the 6-h dataset has lower scatter but higher biases. The study shows how wind dataset affects the numerical wave modelling performance, and that depending on location and study needs, different wind inputs should be considered.

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Optimizing mouse models of neurodegenerative disorders: are therapeutics in sight?

PubMed

Lutz, Cathleen M; Osborne, Melissa A

2013-01-01

The genomic and biologic conservation between mice and humans, along with our increasing ability to manipulate the mouse genome, places the mouse as a premier model for deciphering disease mechanisms and testing potential new therapies. Despite these advantages, mouse models of neurodegenerative disease are sometimes difficult to generate and can present challenges that must be carefully addressed when used for preclinical studies. For those models that do exist, the standardization and optimization of the models is a critical step in ensuring success in both basic research and preclinical use. This review looks back on the history of model development for neurodegenerative diseases and highlights the key strategies that have been learned in order to improve the design, development and use of mouse models in the study of neurodegenerative disease.

Computer-based analysis of microvascular alterations in a mouse model for Alzheimer's disease

NASA Astrophysics Data System (ADS)

Heinzer, Stefan; Müller, Ralph; Stampanoni, Marco; Abela, Rafael; Meyer, Eric P.; Ulmann-Schuler, Alexandra; Krucker, Thomas

2007-03-01

Vascular factors associated with Alzheimer's disease (AD) have recently gained increased attention. To investigate changes in vascular, particularly microvascular architecture, we developed a hierarchical imaging framework to obtain large-volume, high-resolution 3D images from brains of transgenic mice modeling AD. In this paper, we present imaging and data analysis methods which allow compiling unique characteristics from several hundred gigabytes of image data. Image acquisition is based on desktop micro-computed tomography (µCT) and local synchrotron-radiation µCT (SRµCT) scanning with a nominal voxel size of 16 µm and 1.4 µm, respectively. Two visualization approaches were implemented: stacks of Z-buffer projections for fast data browsing, and progressive-mesh based surface rendering for detailed 3D visualization of the large datasets. In a first step, image data was assessed visually via a Java client connected to a central database. Identified characteristics of interest were subsequently quantified using global morphometry software. To obtain even deeper insight into microvascular alterations, tree analysis software was developed providing local morphometric parameters such as number of vessel segments or vessel tortuosity. In the context of ever increasing image resolution and large datasets, computer-aided analysis has proven both powerful and indispensable. The hierarchical approach maintains the context of local phenomena, while proper visualization and morphometry provide the basis for detailed analysis of the pathology related to structure. Beyond analysis of microvascular changes in AD this framework will have significant impact considering that vascular changes are involved in other neurodegenerative diseases as well as in cancer, cardiovascular disease, asthma, and arthritis.

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

PubMed

Darlington, Yolanda F; Naumov, Alexey; McOwiti, Apollo; Kankanamge, Wasula H; Becnel, Lauren B; McKenna, Neil J

2017-03-01

Although omics datasets represent valuable assets for hypothesis generation, model testing, and data validation, the infrastructure supporting their reuse lacks organization and consistency. Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary datasets were exposed on responsive web pages to support mining of gene lists, discovery of related datasets, and single-click citation integration with popular reference managers. Automated processes were established to embed digital object identifier-driven links to the secondary datasets in associated journal articles, small molecule and gene-centric databases, and a dataset search engine. Our model creates multiple points of access to reprocessed and reannotated derivative datasets across the digital biomedical research ecosystem, promoting their visibility and usability across disparate research communities. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Applications and Limitations of Mouse Models for Understanding Human Atherosclerosis

PubMed Central

von Scheidt, Moritz; Zhao, Yuqi; Kurt, Zeyneb; Pan, Calvin; Zeng, Lingyao; Yang, Xia; Schunkert, Heribert; Lusis, Aldons J.

2017-01-01

Most of the biological understanding of mechanisms underlying coronary artery disease (CAD) derives from studies of mouse models. The identification of multiple CAD loci and strong candidate genes in large human genome-wide association studies (GWAS) presented an opportunity to examine the relevance of mouse models for the human disease. We comprehensively reviewed the mouse literature, including 827 literature-derived genes, and compared it to human data. First, we observed striking concordance of risk factors for atherosclerosis in mice and humans. Second, there was highly significant overlap of mouse genes with human genes identified by GWAS. In particular, of the 46 genes with strong association signals in CAD-GWAS that were studied in mouse models all but one exhibited consistent effects on atherosclerosis-related phenotypes. Third, we compared 178 CAD-associated pathways derived from human GWAS with 263 from mouse studies and observed that over 50% were consistent between both species. PMID:27916529

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures.

PubMed

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

PubMed Central

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies. PMID:27148077

Bayesian correlated clustering to integrate multiple datasets

PubMed Central

Kirk, Paul; Griffin, Jim E.; Savage, Richard S.; Ghahramani, Zoubin; Wild, David L.

2012-01-01

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/. Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23047558

Genetically engineered mouse models for studying inflammatory bowel disease.

PubMed

Mizoguchi, Atsushi; Takeuchi, Takahito; Himuro, Hidetomo; Okada, Toshiyuki; Mizoguchi, Emiko

2016-01-01

Inflammatory bowel disease (IBD) is a chronic intestinal inflammatory condition that is mediated by very complex mechanisms controlled by genetic, immune, and environmental factors. More than 74 kinds of genetically engineered mouse strains have been established since 1993 for studying IBD. Although mouse models cannot fully reflect human IBD, they have provided significant contributions for not only understanding the mechanism, but also developing new therapeutic means for IBD. Indeed, 20 kinds of genetically engineered mouse models carry the susceptibility genes identified in human IBD, and the functions of some other IBD susceptibility genes have also been dissected out using mouse models. Cutting-edge technologies such as cell-specific and inducible knockout systems, which were recently employed to mouse IBD models, have further enhanced the ability of investigators to provide important and unexpected rationales for developing new therapeutic strategies for IBD. In this review article, we briefly introduce 74 kinds of genetically engineered mouse models that spontaneously develop intestinal inflammation. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Mouse Tumor Biology (MTB): a database of mouse models for human cancer.

PubMed

Bult, Carol J; Krupke, Debra M; Begley, Dale A; Richardson, Joel E; Neuhauser, Steven B; Sundberg, John P; Eppig, Janan T

2015-01-01

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

The latest animal models of ovarian cancer for novel drug discovery.

PubMed

Magnotti, Elizabeth; Marasco, Wayne A

2018-03-01

Epithelial ovarian cancer is a heterogeneous disease classified into five subtypes, each with a different molecular profile. Most cases of ovarian cancer are diagnosed after metastasis of the primary tumor and are resistant to traditional platinum-based chemotherapeutics. Mouse models of ovarian cancer have been utilized to discern ovarian cancer tumorigenesis and the tumor's response to therapeutics. Areas covered: The authors provide a review of mouse models currently employed to understand ovarian cancer. This article focuses on advances in the development of orthotopic and patient-derived tumor xenograft (PDX) mouse models of ovarian cancer and discusses current humanized mouse models of ovarian cancer. Expert opinion: The authors suggest that humanized mouse models of ovarian cancer will provide new insight into the role of the human immune system in combating and augmenting ovarian cancer and aid in the development of novel therapeutics. Development of humanized mouse models will take advantage of the NSG and NSG-SGM3 strains of mice as well as new strains that are actively being derived.

How Genetically Engineered Mouse Tumor Models Provide Insights Into Human Cancers

PubMed Central

Politi, Katerina; Pao, William

2011-01-01

Genetically engineered mouse models (GEMMs) of human cancer were first created nearly 30 years ago. These early transgenic models demonstrated that mouse cells could be transformed in vivo by expression of an oncogene. A new field emerged, dedicated to generating and using mouse models of human cancer to address a wide variety of questions in cancer biology. The aim of this review is to highlight the contributions of mouse models to the diagnosis and treatment of human cancers. Because of the breadth of the topic, we have selected representative examples of how GEMMs are clinically relevant rather than provided an exhaustive list of experiments. Today, as detailed here, sophisticated mouse models are being created to study many aspects of cancer biology, including but not limited to mechanisms of sensitivity and resistance to drug treatment, oncogene cooperation, early detection, and metastasis. Alternatives to GEMMs, such as chemically induced or spontaneous tumor models, are not discussed in this review. PMID:21263096

Integrating model behavior, optimization, and sensitivity/uncertainty analysis: overview and application of the MOUSE software toolbox

USDA-ARS?s Scientific Manuscript database

This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration syst...

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Sex-comparative study of mouse cerebellum physiology under adult-onset hypothyroidism: The significance of GC-MS metabolomic data normalization in meta-analysis.

PubMed

Maga-Nteve, Christoniki; Vasilopoulou, Catherine G; Constantinou, Caterina; Margarity, Marigoula; Klapa, Maria I

2017-01-15

A systematic data quality validation and normalization strategy is an important component of the omic profile meta-analysis, ensuring comparability of the profiles and exclusion of experimental biases from the derived biological conclusions. In this study, we present the normalization methodology applied on the sets of cerebellum gas chromatography-mass spectrometry metabolic profiles of 124days old male and female animals in an adult-onset-hypothyroidism (AOH) mouse model before combining them into a sex-comparative analysis. The employed AOH model concerns the monitoring of the brain physiology of Balb/cJ mice after eight-week administration of 1%w/v KClO 4 in the drinking water, initiated on the 60th day of their life. While originating from the same animal study, the tissues of the two sexes were processed and their profiles acquired and analyzed at different time periods. Hence, the previously published profile set of male mice was first re-annotated based on the presently available resources. Then, after being validated as acquired under the same analytical conditions, both profiles sets were corrected for derivatization biases and filtered for low-confidence measurements based on the same criteria. The final normalized 73-metabolite profiles contribute to the currently few available omic datasets of the AOH effect on brain molecular physiology, especially with respect to sex differentiation. Multivariate statistical analysis indicated one (unknown) and three (succinate, benzoate, myristate) metabolites with significantly higher and lower, respectively, cerebellum concentration in the hypothyroid compared to the euthyroid female mice. The respective numbers for the males were two and 24. Comparison of the euthyroid cerebellum metabolic profiles between the two sexes indicated 36 metabolites, including glucose, myo- and scyllo-inositol, with significantly lower concentration in the females versus the males. This implies that the female mouse cerebellum has been conditioned to smaller changes in its metabolic activity with respect to the pathways involving these metabolites compared to the male animals. In conclusion, our study indicated a much subtler AOH effect on the cerebellum metabolic activity of the female compared to the male mice. The leaner metabolic profile of the female mouse cerebellum was suggested as a potential factor contributing to this phenomenon. Copyright © 2016 Elsevier B.V. All rights reserved.

Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

PubMed

Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio

2016-09-01

A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see "On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection" (Bruni et al. [2])). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.

Disrupting the male germ line to find infertility and contraception targets.

PubMed

Archambeault, Denise R; Matzuk, Martin M

2014-05-01

Genetically-manipulated mouse models have become indispensible for broadening our understanding of genes and pathways related to male germ cell development. Until suitable in vitro systems for studying spermatogenesis are perfected, in vivo models will remain the gold standard for inquiry into testicular function. Here, we discuss exciting advances that are allowing researchers faster, easier, and more customizable access to their mouse models of interest. Specifically, the trans-NIH Knockout Mouse Project (KOMP) is working to generate knockout mouse models of every gene in the mouse genome. The related Knockout Mouse Phenotyping Program (KOMP2) is performing systematic phenotypic analysis of this genome-wide collection of knockout mice, including fertility screening. Together, these programs will not only uncover new genes involved in male germ cell development but also provide the research community with the mouse models necessary for further investigations. In addition to KOMP/KOMP2, another promising development in the field of mouse models is the advent of CRISPR (clustered regularly interspaced short palindromic repeat)-Cas technology. Utilizing 20 nucleotide guide sequences, CRISPR/Cas has the potential to introduce sequence-specific insertions, deletions, and point mutations to produce null, conditional, activated, or reporter-tagged alleles. CRISPR/Cas can also successfully target multiple genes in a single experimental step, forgoing the multiple generations of breeding traditionally required to produce mouse models with deletions, insertions, or mutations in multiple genes. In addition, CRISPR/Cas can be used to create mouse models carrying variants identical to those identified in infertile human patients, providing the opportunity to explore the effects of such mutations in an in vivo system. Both the KOMP/KOMP2 projects and the CRISPR/Cas system provide powerful, accessible genetic approaches to the study of male germ cell development in the mouse. A more complete understanding of male germ cell biology is critical for the identification of novel targets for potential non-hormonal contraceptive intervention. Copyright © 2014. Published by Elsevier Masson SAS.

Genome-Wide Expression Profiling of Five Mouse Models Identifies Similarities and Differences with Human Psoriasis

PubMed Central

Swindell, William R.; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P.; Voorhees, John J.; Elder, James T.; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P.; DiGiovanni, John; Pittelkow, Mark R.; Ward, Nicole L.; Gudjonsson, Johann E.

2011-01-01

Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis. PMID:21483750

Exploiting Genome Structure in Association Analysis

PubMed Central

Kim, Seyoung

2014-01-01

Abstract A genome-wide association study involves examining a large number of single-nucleotide polymorphisms (SNPs) to identify SNPs that are significantly associated with the given phenotype, while trying to reduce the false positive rate. Although haplotype-based association methods have been proposed to accommodate correlation information across nearby SNPs that are in linkage disequilibrium, none of these methods directly incorporated the structural information such as recombination events along chromosome. In this paper, we propose a new approach called stochastic block lasso for association mapping that exploits prior knowledge on linkage disequilibrium structure in the genome such as recombination rates and distances between adjacent SNPs in order to increase the power of detecting true associations while reducing false positives. Following a typical linear regression framework with the genotypes as inputs and the phenotype as output, our proposed method employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a first-order Markov process along the sequence of SNPs that incorporates the prior information on the linkage disequilibrium structure. The Markov-chain prior models the structural dependencies between a pair of adjacent SNPs, and allows us to look for association SNPs in a coupled manner, combining strength from multiple nearby SNPs. Our results on HapMap-simulated datasets and mouse datasets show that there is a significant advantage in incorporating the prior knowledge on linkage disequilibrium structure for marker identification under whole-genome association. PMID:21548809

In-depth evaluation of software tools for data-independent acquisition based label-free quantification.

PubMed

Kuharev, Jörg; Navarro, Pedro; Distler, Ute; Jahn, Olaf; Tenzer, Stefan

2015-09-01

Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Effect of topical application of a recombinant adenovirus carrying promyelocytic leukemia gene in a psoriasis-like mouse model].

PubMed

Wang, Qiongyu; Zhang, Aijun; Ma, Huiqun; Wang, Shijie; Ma, Yunyun; Zou, Xingwei; Li, Ruilian

2013-03-01

To investigate the effects of topical treatment with adenovirus-mediated promyelocytic leukemia gene (PML) gene in a psoriasis-like mouse model. The effect of adenovirus-mediated PML gene on the granular layer of mouse tail scale epidermis and epithelial mitosis were observed on longitudinal histological sections prepared from the tail skin and vaginal epithelium of the mice. Adenovirus-mediated PML gene significantly inhibited mitosis of mouse vaginal epithelial cells and promoted the formation of granular layer in mouse tail scale epidermis. The therapeutic effect of PML gene in the psoriasis-like mouse model may be associated with increased granular cells and suppressed epidemic cell proliferation.

Generation of transgenic mouse model using PTTG as an oncogene.

PubMed

Kakar, Sham S; Kakar, Cohin

2015-01-01

The close physiological similarity between the mouse and human has provided tools to understanding the biological function of particular genes in vivo by introduction or deletion of a gene of interest. Using a mouse as a model has provided a wealth of resources, knowledge, and technology, helping scientists to understand the biological functions, translocation, trafficking, and interaction of a candidate gene with other intracellular molecules, transcriptional regulation, posttranslational modification, and discovery of novel signaling pathways for a particular gene. Most importantly, the generation of the mouse model for a specific human disease has provided a powerful tool to understand the etiology of a disease and discovery of novel therapeutics. This chapter describes in detail the step-by-step generation of the transgenic mouse model, which can be helpful in guiding new investigators in developing successful models. For practical purposes, we will describe the generation of a mouse model using pituitary tumor transforming gene (PTTG) as the candidate gene of interest.

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

PubMed Central

Wernisch, Lorenz

2017-01-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. PMID:29036190

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

PubMed

Gabasova, Evelina; Reid, John; Wernisch, Lorenz

2017-10-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

DOE PAGES

James, Eric P.; Benjamin, Stanley G.; Marquis, Melinda

2016-10-28

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents anmore » initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.« less

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

PubMed

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-07-02

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Reevaluating the Potential of Mouse Models for the Human Immune System.

PubMed

Jameson, Stephen C; Masopust, David

2018-04-02

Much of what we understand about immunology, including the response to vaccines, come from studies in mice because they provide many practical advantages compared with research in higher mammals and humans. Nevertheless, modalities for preventing or treating disease do not always translate from mouse to humans, which has led to increasing scrutiny of the continued merits of mouse research. Here, we summarize the pros and cons of current laboratory mouse models for immunology research and discuss whether overreliance on nonphysiological, ultra-hygienic animal husbandry approaches has limited the ultimate translation potential of mouse-derived data to humans. Alternative approaches are discussed that may extend the use of the mouse model for preclinical studies. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Mouse Models in Bone Marrow Transplantation and Adoptive Cellular Therapy

PubMed Central

Arber, Caroline; Brenner, Malcolm K.; Reddy, Pavan

2014-01-01

Mouse models of transplantation have been indispensable to the development of bone marrow transplantation (BMT). Their role in the generation of basic science knowledge is invaluable and is subject to discussion below. However, this article focuses on the direct role and relevance of mouse models towards the clinical development and advances in BMT and adoptive T-cell therapy for human diseases. The authors aim to present a thoughtful perspective on the pros and cons of mouse models while noting that despite imperfections these models are obligatory for the development of science-based medicine. PMID:24216170

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Dataset used to improve liquid water absorption models in the microwave

DOE Data Explorer

Turner, David

2015-12-14

Two datasets, one a compilation of laboratory data and one a compilation from three field sites, are provided here. These datasets provide measurements of the real and imaginary refractive indices and absorption as a function of cloud temperature. These datasets were used in the development of the new liquid water absorption model that was published in Turner et al. 2015.

The Isprs Benchmark on Indoor Modelling

NASA Astrophysics Data System (ADS)

Khoshelham, K.; Díaz Vilariño, L.; Peter, M.; Kang, Z.; Acharya, D.

2017-09-01

Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at: http://www2.isprs.org/commissions/comm4/wg5/benchmark-on-indoor-modelling.html.

Centralized mouse repositories.

PubMed

Donahue, Leah Rae; Hrabe de Angelis, Martin; Hagn, Michael; Franklin, Craig; Lloyd, K C Kent; Magnuson, Terry; McKerlie, Colin; Nakagata, Naomi; Obata, Yuichi; Read, Stuart; Wurst, Wolfgang; Hörlein, Andreas; Davisson, Muriel T

2012-10-01

Because the mouse is used so widely for biomedical research and the number of mouse models being generated is increasing rapidly, centralized repositories are essential if the valuable mouse strains and models that have been developed are to be securely preserved and fully exploited. Ensuring the ongoing availability of these mouse strains preserves the investment made in creating and characterizing them and creates a global resource of enormous value. The establishment of centralized mouse repositories around the world for distributing and archiving these resources has provided critical access to and preservation of these strains. This article describes the common and specialized activities provided by major mouse repositories around the world.

Centralized Mouse Repositories

PubMed Central

Donahue, Leah Rae; de Angelis, Martin Hrabe; Hagn, Michael; Franklin, Craig; Lloyd, K. C. Kent; Magnuson, Terry; McKerlie, Colin; Nakagata, Naomi; Obata, Yuichi; Read, Stuart; Wurst, Wolfgang; Hörlein, Andreas; Davisson, Muriel T.

2013-01-01

Because the mouse is used so widely for biomedical research and the number of mouse models being generated is increasing rapidly, centralized repositories are essential if the valuable mouse strains and models that have been developed are to be securely preserved and fully exploited. Ensuring the ongoing availability of these mouse strains preserves the investment made in creating and characterizing them and creates a global resource of enormous value. The establishment of centralized mouse repositories around the world for distributing and archiving these resources has provided critical access to and preservation of these strains. This article describes the common and specialized activities provided by major mouse repositories around the world. PMID:22945696

SPAR: small RNA-seq portal for analysis of sequencing experiments.

PubMed

Kuksa, Pavel P; Amlie-Wolf, Alexandre; Katanic, Živadin; Valladares, Otto; Wang, Li-San; Leung, Yuk Yee

2018-05-04

The introduction of new high-throughput small RNA sequencing protocols that generate large-scale genomics datasets along with increasing evidence of the significant regulatory roles of small non-coding RNAs (sncRNAs) have highlighted the urgent need for tools to analyze and interpret large amounts of small RNA sequencing data. However, it remains challenging to systematically and comprehensively discover and characterize sncRNA genes and specifically-processed sncRNA products from these datasets. To fill this gap, we present Small RNA-seq Portal for Analysis of sequencing expeRiments (SPAR), a user-friendly web server for interactive processing, analysis, annotation and visualization of small RNA sequencing data. SPAR supports sequencing data generated from various experimental protocols, including smRNA-seq, short total RNA sequencing, microRNA-seq, and single-cell small RNA-seq. Additionally, SPAR includes publicly available reference sncRNA datasets from our DASHR database and from ENCODE across 185 human tissues and cell types to produce highly informative small RNA annotations across all major small RNA types and other features such as co-localization with various genomic features, precursor transcript cleavage patterns, and conservation. SPAR allows the user to compare the input experiment against reference ENCODE/DASHR datasets. SPAR currently supports analyses of human (hg19, hg38) and mouse (mm10) sequencing data. SPAR is freely available at https://www.lisanwanglab.org/SPAR.

The α3β4* nicotinic ACh receptor subtype mediates physical dependence to morphine: mouse and human studies

PubMed Central

Muldoon, P P; Jackson, K J; Perez, E; Harenza, J L; Molas, S; Rais, B; Anwar, H; Zaveri, N T; Maldonado, R; Maskos, U; McIntosh, J M; Dierssen, M; Miles, M F; Chen, X; De Biasi, M; Damaj, M I

2014-01-01

BACKGROUND AND PURPOSE Recent data have indicated that α3β4* neuronal nicotinic (n) ACh receptors may play a role in morphine dependence. Here we investigated if nACh receptors modulate morphine physical withdrawal. EXPERIMENTAL APPROACHES To assess the role of α3β4* nACh receptors in morphine withdrawal, we used a genetic correlation approach using publically available datasets within the GeneNetwork web resource, genetic knockout and pharmacological tools. Male and female European-American (n = 2772) and African-American (n = 1309) subjects from the Study of Addiction: Genetics and Environment dataset were assessed for possible associations of polymorphisms in the 15q25 gene cluster and opioid dependence. KEY RESULTS BXD recombinant mouse lines demonstrated an increased expression of α3, β4 and α5 nACh receptor mRNA in the forebrain and midbrain, which significantly correlated with increased defecation in mice undergoing morphine withdrawal. Mice overexpressing the gene cluster CHRNA5/A3/B4 exhibited increased somatic signs of withdrawal. Furthermore, α5 and β4 nACh receptor knockout mice expressed decreased somatic withdrawal signs compared with their wild-type counterparts. Moreover, selective α3β4* nACh receptor antagonists, α-conotoxin AuIB and AT-1001, attenuated somatic signs of morphine withdrawal in a dose-related manner. In addition, two human datasets revealed a protective role for variants in the CHRNA3 gene, which codes for the α3 nACh receptor subunit, in opioid dependence and withdrawal. In contrast, we found that the α4β2* nACh receptor subtype is not involved in morphine somatic withdrawal signs. CONCLUSION AND IMPLICATIONS Overall, our findings suggest an important role for the α3β4* nACh receptor subtype in morphine physical dependence. PMID:24750073

A large dataset of protein dynamics in the mammalian heart proteome

PubMed Central

Lau, Edward; Cao, Quan; Ng, Dominic C.M.; Bleakley, Brian J.; Dincer, T. Umut; Bot, Brian M.; Wang, Ding; Liem, David A.; Lam, Maggie P.Y.; Ge, Junbo; Ping, Peipei

2016-01-01

Protein stability is a major regulatory principle of protein function and cellular homeostasis. Despite limited understanding on mechanisms, disruption of protein turnover is widely implicated in diverse pathologies from heart failure to neurodegenerations. Information on global protein dynamics therefore has the potential to expand the depth and scope of disease phenotyping and therapeutic strategies. Using an integrated platform of metabolic labeling, high-resolution mass spectrometry and computational analysis, we report here a comprehensive dataset of the in vivo half-life of 3,228 and the expression of 8,064 cardiac proteins, quantified under healthy and hypertrophic conditions across six mouse genetic strains commonly employed in biomedical research. We anticipate these data will aid in understanding key mitochondrial and metabolic pathways in heart diseases, and further serve as a reference for methodology development in dynamics studies in multiple organ systems. PMID:26977904

Imaging samples larger than the field of view: the SLS experience

NASA Astrophysics Data System (ADS)

Vogiatzis Oikonomidis, Ioannis; Lovric, Goran; Cremona, Tiziana P.; Arcadu, Filippo; Patera, Alessandra; Schittny, Johannes C.; Stampanoni, Marco

2017-06-01

Volumetric datasets with micrometer spatial and sub-second temporal resolutions are nowadays routinely acquired using synchrotron X-ray tomographic microscopy (SRXTM). Although SRXTM technology allows the examination of multiple samples with short scan times, many specimens are larger than the field-of-view (FOV) provided by the detector. The extension of the FOV in the direction perpendicular to the rotation axis remains non-trivial. We present a method that can efficiently increase the FOV merging volumetric datasets obtained by region-of-interest tomographies in different 3D positions of the sample with a minimal amount of artefacts and with the ability to handle large amounts of data. The method has been successfully applied for the three-dimensional imaging of a small number of mouse lung acini of intact animals, where pixel sizes down to the micrometer range and short exposure times are required.

Hurricane Data Analysis Tool

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Leptoukh, Gregory

2011-01-01

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

PhosphOrtholog: a web-based tool for cross-species mapping of orthologous protein post-translational modifications.

PubMed

Chaudhuri, Rima; Sadrieh, Arash; Hoffman, Nolan J; Parker, Benjamin L; Humphrey, Sean J; Stöckli, Jacqueline; Hill, Adam P; James, David E; Yang, Jean Yee Hwa

2015-08-19

Most biological processes are influenced by protein post-translational modifications (PTMs). Identifying novel PTM sites in different organisms, including humans and model organisms, has expedited our understanding of key signal transduction mechanisms. However, with increasing availability of deep, quantitative datasets in diverse species, there is a growing need for tools to facilitate cross-species comparison of PTM data. This is particularly important because functionally important modification sites are more likely to be evolutionarily conserved; yet cross-species comparison of PTMs is difficult since they often lie in structurally disordered protein domains. Current tools that address this can only map known PTMs between species based on known orthologous phosphosites, and do not enable the cross-species mapping of newly identified modification sites. Here, we addressed this by developing a web-based software tool, PhosphOrtholog ( www.phosphortholog.com ) that accurately maps protein modification sites between different species. This facilitates the comparison of datasets derived from multiple species, and should be a valuable tool for the proteomics community. Here we describe PhosphOrtholog, a web-based application for mapping known and novel orthologous PTM sites from experimental data obtained from different species. PhosphOrtholog is the only generic and automated tool that enables cross-species comparison of large-scale PTM datasets without relying on existing PTM databases. This is achieved through pairwise sequence alignment of orthologous protein residues. To demonstrate its utility we apply it to two sets of human and rat muscle phosphoproteomes generated following insulin and exercise stimulation, respectively, and one publicly available mouse phosphoproteome following cellular stress revealing high mapping and coverage efficiency. Although coverage statistics are dataset dependent, PhosphOrtholog increased the number of cross-species mapped sites in all our example data sets by more than double when compared to those recovered using existing resources such as PhosphoSitePlus. PhosphOrtholog is the first tool that enables mapping of thousands of novel and known protein phosphorylation sites across species, accessible through an easy-to-use web interface. Identification of conserved PTMs across species from large-scale experimental data increases our knowledgebase of functional PTM sites. Moreover, PhosphOrtholog is generic being applicable to other PTM datasets such as acetylation, ubiquitination and methylation.

Molecular Indicators of Stress-Induced Neuroinflammation in a Mouse Model Simulating Features of Post-Traumatic Stress Disorder (Open Access)

DTIC Science & Technology

2017-05-23

OPEN ORIGINAL ARTICLE Molecular indicators of stress-induced neuroinflammation in a mouse model simulating features of post -traumatic stress disorder... post -traumatic stress disorder (PTSD). The model involved exposure of an intruder (male C57BL/6) mouse to a resident aggressor (male SJL) mouse for 5...revealed that neurogenesis and synaptic plasticity pathways were activated during the early responses but were inhibited after the later post -trauma

Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

NASA Astrophysics Data System (ADS)

Lateh, Masitah Abdul; Kamilah Muda, Azah; Yusof, Zeratul Izzah Mohd; Azilah Muda, Noor; Sanusi Azmi, Mohd

2017-09-01

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

Genetic characterization and improved genotyping of the dysferlin-deficient mouse strain Dysf (tm1Kcam).

PubMed

Wiktorowicz, Tatiana; Kinter, Jochen; Kobuke, Kazuhiro; Campbell, Kevin P; Sinnreich, Michael

2015-01-01

Mouse models of dysferlinopathies are valuable tools with which to investigate the pathomechanisms underlying these diseases and to test novel therapeutic strategies. One such mouse model is the Dysf (tm1Kcam) strain, which was generated using a targeting vector to replace a 12-kb region of the dysferlin gene and which features a progressive muscular dystrophy. A prerequisite for successful animal studies using genetic mouse models is an accurate genotyping protocol. Unfortunately, the lack of robustness of currently available genotyping protocols for the Dysf (tm1Kcam) mouse has prevented efficient colony management. Initial attempts to improve the genotyping protocol based on the published genomic structure failed. These difficulties led us to analyze the targeted locus of the dysferlin gene of the Dysf (tm1Kcam) mouse in greater detail. In this study we resequenced and analyzed the targeted locus of the Dysf (tm1Kcam) mouse and developed a novel PCR protocol for genotyping. We found that instead of a deletion, the dysferlin locus in the Dysf (tm1Kcam) mouse carries a targeted insertion. This genetic characterization enabled us to establish a reliable method for genotyping of the Dysf (tm1Kcam) mouse, and thus has made efficient colony management possible. Our work will make the Dysf (tm1Kcam) mouse model more attractive for animal studies of dysferlinopathies.

High-throughput discovery of novel developmental phenotypes.

PubMed

Dickinson, Mary E; Flenniken, Ann M; Ji, Xiao; Teboul, Lydia; Wong, Michael D; White, Jacqueline K; Meehan, Terrence F; Weninger, Wolfgang J; Westerberg, Henrik; Adissu, Hibret; Baker, Candice N; Bower, Lynette; Brown, James M; Caddle, L Brianna; Chiani, Francesco; Clary, Dave; Cleak, James; Daly, Mark J; Denegre, James M; Doe, Brendan; Dolan, Mary E; Edie, Sarah M; Fuchs, Helmut; Gailus-Durner, Valerie; Galli, Antonella; Gambadoro, Alessia; Gallegos, Juan; Guo, Shiying; Horner, Neil R; Hsu, Chih-Wei; Johnson, Sara J; Kalaga, Sowmya; Keith, Lance C; Lanoue, Louise; Lawson, Thomas N; Lek, Monkol; Mark, Manuel; Marschall, Susan; Mason, Jeremy; McElwee, Melissa L; Newbigging, Susan; Nutter, Lauryl M J; Peterson, Kevin A; Ramirez-Solis, Ramiro; Rowland, Douglas J; Ryder, Edward; Samocha, Kaitlin E; Seavitt, John R; Selloum, Mohammed; Szoke-Kovacs, Zsombor; Tamura, Masaru; Trainor, Amanda G; Tudose, Ilinca; Wakana, Shigeharu; Warren, Jonathan; Wendling, Olivia; West, David B; Wong, Leeyean; Yoshiki, Atsushi; MacArthur, Daniel G; Tocchini-Valentini, Glauco P; Gao, Xiang; Flicek, Paul; Bradley, Allan; Skarnes, William C; Justice, Monica J; Parkinson, Helen E; Moore, Mark; Wells, Sara; Braun, Robert E; Svenson, Karen L; de Angelis, Martin Hrabe; Herault, Yann; Mohun, Tim; Mallon, Ann-Marie; Henkelman, R Mark; Brown, Steve D M; Adams, David J; Lloyd, K C Kent; McKerlie, Colin; Beaudet, Arthur L; Bućan, Maja; Murray, Stephen A

2016-09-22

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding.

PubMed

Li, Yujie; Chen, Hanbo; Jiang, Xi; Li, Xiang; Lv, Jinglei; Peng, Hanchuan; Tsien, Joe Z; Liu, Tianming

2017-12-01

Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways, and/or anatomical regions.

Delineation of vertebral area on the coronal plane using three-dimensional ultrasonography advanced volume contrast imaging (VCI) Omni view: intrarater reliability and agreement using standard mouse, high definition mouse, and pen-tablet.

PubMed

Araujo Júnior, Edward; Martinez, Luis Henrique; Simioni, Christiane; Martins, Wellington P; Nardozza, Luciano M; Moron, Antonio F

2012-09-01

To assess the fetal lumbosacral spine by three-dimensional (3D) ultrasonography using volume contrast imaging (VCI) omni view method and compare reproducibility and agreement between three different measurement techniques: standard mouse, high definition mouse and pen-tablet. A comparative and prospective study with 40 pregnant women between 20 and 34 + 6 weeks was realized. 3D volume datasets of the fetal spine were acquired using a convex transabdominal transducer. Starting scan plane was the coronal section of fetal lumbosacral spine by VCI-C function. Omni view manual trace was selected and a parallel plane of fetal spine was drawn including interest region. Intraclass correlation coefficient (ICC) was used for reproducibility analysis. The relative difference between three used techniques was compared by chi-square test and Fischer test. Pen-tablet showed better reliability (ICC=0.987). In the relative proportion of differences, this was significantly higher for the pen-tablet (82.14%; p<0.01). In paired comparison, the relative difference was significantly greater for the pen-tablet (p<0.01). The pen-tablet showed to be the most reproductive and concordant method in the measurement of body vertebral area of fetal lumbosacral spine by 3D ultrasonography using the VCI.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Mouse models of neurodegenerative diseases: criteria and general methodology.

PubMed

Janus, Christopher; Welzl, Hans

2010-01-01

The major symptom of Alzheimer's disease is rapidly progressing dementia, coinciding with the formation of amyloid and tau deposits in the central nervous system, and neuronal death. At present familial cases of dementias provide the most promising foundation for modelling neurodegeneration. We describe the mnemonic and other major behavioral symptoms of tauopathies, briefly outline the genetics underlying familiar cases and discuss the arising implications for modelling the disease in mostly transgenic mouse lines. We then depict to what degree the most recent mouse models replicate pathological and cognitive characteristics observed in patients.There is no universally valid behavioral test battery to evaluate mouse models. The selection of individual tests depends on the behavioral and/or memory system in focus, the type of a model and how well it replicates the pathology of a disease and the amount of control over the genetic background of the mouse model. However it is possible to provide guidelines and criteria for modelling the neurodegeneration, setting up the experiments and choosing relevant tests. One should not adopt a "one (trans)gene, one disease" interpretation, but should try to understand how the mouse genome copes with the protein expression of the transgene in question. Further, it is not possible to recommend some mouse models over others since each model is valuable within its own constraints, and the way experiments are performed often reflects the idiosyncratic reality of specific laboratories. Our purpose is to improve bridging molecular and behavioural approaches in translational research.

Direct comparison of the pharmacodynamics of four antifungal drugs in a mouse model of disseminated candidiasis using microbiological assays of serum drug concentrations.

PubMed

Maki, Katsuyuki; Holmes, Ann R; Watabe, Etsuko; Iguchi, Yumi; Matsumoto, Satoru; Ikeda, Fumiaki; Tawara, Shuichi; Mutoh, Seitaro

2007-01-01

The aim of this study was to compare the pharmacodynamics of the azole antifungal drugs fluconazole, itraconazole and ketoconazole, and the polyene antifungal amphotericin B, in a mouse model of disseminated Candida albicans infection. In order to directly compare effective serum concentrations of these antifungals, drug concentrations were assayed microbiologically by measuring inhibition of C. albicans mycelial growth (mMIC) in a mouse serum-based assay (serum antifungal titer). Efficacy in the mouse infection model was determined using an organ-based (kidney burden) endpoint. For all four drugs, the serum antifungal titers, 8 hr after administration of single doses of drugs at a range of drug concentrations, correlated closely with C. albicans kidney fungal burden in the mouse model. The results showed that determining serum antifungal titer may be used to accurately represent kidney fungal burden in a mouse model of disseminated candidiasis and allowed direct comparison of the pharmacodynamics of differing classes of antifungal drugs.

Mouse Models as Tools to Identify Genetic Pathways for Retinal Degeneration, as Exemplified by Leber's Congenital Amaurosis.

PubMed

Chang, Bo

2016-01-01

Leber's congenital amaurosis (LCA) is an inherited retinal degenerative disease characterized by severe loss of vision in the first year of life. In addition to early vision loss, a variety of other eye-related abnormalities including roving eye movements, deep-set eyes, and sensitivity to bright light also occur with this disease. Many animal models of LCA are available and the study them has led to a better understanding of the pathology of the disease, and has led to the development of therapeutic strategies aimed at curing or slowing down LCA. Mouse models, with their well-developed genetics and similarity to human physiology and anatomy, serve as powerful tools with which to investigate the etiology of human LCA. Such mice provide reproducible, experimental systems for elucidating pathways of normal development, function, designing strategies and testing compounds for translational research and gene-based therapies aimed at delaying the diseases progression. In this chapter, I describe tools used in the discovery and evaluation of mouse models of LCA including a Phoenix Image-Guided Optical Coherence Tomography (OCT) and a Diagnosys Espion Visual Electrophysiology System. Three mouse models are described, the rd3 mouse model for LCA12 and LCA1, the rd12 mouse model for LCA2, and the rd16 mouse model for LCA10.

Differences in Pathogenesis for Salmonella enterica serovar Typhimurium in the Mouse Versus the Swine Model Identify Bacterial Gene Products Required for Systemic but not Gastrointestinal Disease

USDA-ARS?s Scientific Manuscript database

Over the last several decades, the mouse model of Typhoid fever has been an extremely productive model to investigate Salmonella enterica serovar Typhimurium pathogenesis. The mouse is the paradigm for investigating systemic disease due to infection by Salmonella; however, the swine model of gastro...

A unified model of the excitability of mouse sensory and motor axons.

PubMed

Makker, Preet G S; Matamala, José Manuel; Park, Susanna B; Lees, Justin G; Kiernan, Matthew C; Burke, David; Moalem-Taylor, Gila; Howells, James

2018-06-19

Non-invasive nerve excitability techniques have provided valuable insight into the understanding of neurological disorders. The widespread use of mice in translational research on peripheral nerve disorders and by pharmaceutical companies during drug development requires valid and reliable models that can be compared to humans. This study established a novel experimental protocol that enables comparative assessment of the excitability properties of motor and sensory axons at the same site in mouse caudal nerve, compared the mouse data to data for motor and sensory axons in human median nerve at the wrist, and constructed a mathematical model of the excitability of mouse axons. In a separate study, ischaemia was employed as an experimental manoeuvre to test the translational utility of this preparation. The patterns of mouse sensory and motor excitability were qualitatively similar to human studies under normal and ischaemic conditions. The most conspicuous differences between mouse and human studies were observed in the recovery cycle and the response to hyperpolarization. Modelling showed that an increase in temperature in mouse axons could account for most of the differences in the recovery cycle. The modelling also suggested a larger hyperpolarization-activated conductance in mouse axons. The kinetics of this conductance appeared to be much slower raising the possibility that an additional or different hyperpolarization-activated cyclic-nucleotide gated (HCN) channel isoform underlies the accommodation to hyperpolarization in mouse axons. Given a possible difference in HCN isoforms, caution should be exercised in extrapolating from studies of mouse motor and sensory axons to human nerve disorders. This article is protected by copyright. All rights reserved.

Rapamycin improves sociability in the BTBR T(+)Itpr3(tf)/J mouse model of autism spectrum disorders.

PubMed

Burket, Jessica A; Benson, Andrew D; Tang, Amy H; Deutsch, Stephen I

2014-01-01

Overactivation of the mammalian target of rapamycin (mTOR) has been implicated in the pathogenesis of syndromic forms of autism spectrum disorders (ASDs), such as tuberous sclerosis complex, neurofibromatosis 1, and fragile X syndrome. Administration of mTORC1 (mTOR complex 1) inhibitors (e.g. rapamycin) in syndromic mouse models of ASDs improved behavior, cognition, and neuropathology. However, since only a minority of ASDs are due to the effects of single genes (∼10%), there is a need to explore inhibition of mTOR activity in mouse models that may be more relevant to the majority of nonsyndromic presentations, such as the genetically inbred BTBR T(+)Itpr3(tf)/J (BTBR) mouse model of ASDs. BTBR mice have social impairment and exhibit increased stereotypic behavior. In prior work, d-cycloserine, a partial glycineB site agonist that targets the N-methyl-d-aspartate (NMDA) receptor, was shown to improve sociability in both Balb/c and BTBR mouse models of ASDs. Importantly, NMDA receptor activation regulates mTOR signaling activity. The current study investigated the ability of rapamycin (10mg/kg, i.p.×four days), an mTORC1 inhibitor, to improve sociability and stereotypic behavior in BTBR mice. Using a standard paradigm to assess mouse social behavior, rapamycin improved several measures of sociability in the BTBR mouse, suggesting that mTOR overactivation represents a therapeutic target that mediates or contributes to impaired sociability in the BTBR mouse model of ASDs. Interestingly, there was no effect of rapamycin on stereotypic behaviors in this mouse model. Copyright © 2013 Elsevier Inc. All rights reserved.

In Vivo Hyperthermic Stress Model: An Easy Tool to Study the Effects of Oxidative Stress on Neuronal Tau Functionality in Mouse Brain.

PubMed

Chauderlier, Alban; Delattre, Lucie; Buée, Luc; Galas, Marie-Christine

2017-01-01

Oxidative damage is an early event in neurodegenerative disorders such as Alzheimer disease. To increase oxidative stress in AD-related mouse models is essential to study early mechanisms involved in the physiopathology of these diseases. In this chapter, we describe an experimental mouse model of transient and acute hyperthermic stress to induce in vivo an increase of oxidative stress in the brain of any kind of wild-type or transgenic mouse.

Evolving hard problems: Generating human genetics datasets with a complex etiology.

PubMed

Himmelstein, Daniel S; Greene, Casey S; Moore, Jason H

2011-07-07

A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/.

Tularosa Basin Play Fairway Analysis Data and Models

DOE Data Explorer

Nash, Greg

2017-07-11

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler.

Revision and proposed modification for a total maximum daily load model for Upper Klamath Lake, Oregon

USGS Publications Warehouse

Wherry, Susan A.; Wood, Tamara M.; Anderson, Chauncey W.

2015-01-01

Using the extended 1991–2010 external phosphorus loading dataset, the lake TMDL model was recalibrated following the same procedures outlined in the Phase 1 review. The version of the model selected for further development incorporated an updated sediment initial condition, a numerical solution method for the chlorophyll a model, changes to light and phosphorus factors limiting algal growth, and a new pH-model regression, which removed Julian day dependence in order to avoid discontinuities in pH at year boundaries. This updated lake TMDL model was recalibrated using the extended dataset in order to compare calibration parameters to those obtained from a calibration with the original 7.5-year dataset. The resulting algal settling velocity calibrated from the extended dataset was more than twice the value calibrated with the original dataset, and, because the calibrated values of algal settling velocity and recycle rate are related (more rapid settling required more rapid recycling), the recycling rate also was larger than that determined with the original dataset. These changes in calibration parameters highlight the uncertainty in critical rates in the Upper Klamath Lake TMDL model and argue for their direct measurement in future data collection to increase confidence in the model predictions.

Implementation of a manual for working with wobbler mice and criteria for discontinuation of the experiment.

PubMed

Ott, Bastian; Dahlke, Carolin; Meller, Karl; Napirei, Markus; Schmitt-John, Thomas; Brand-Saberi, Beate; Theiss, Carsten; Saberi, Darius

2015-07-01

Mouse breeding is of importance to a whole range of medical and biological research. There are many known mouse models for motor neuron diseases. However, it must be kept in mind that especially mouse models for amyotrophic lateral sclerosis develop severe symptoms causing intense stress. This article is designed to summarize conscientious work with the wobbler mouse, a model for the sporadic form of amyotrophic lateral sclerosis. This mouse model is characterized by a degeneration of α-motor-neurons leading to head tremor, loss of body weight and rapidly progressive paralysis. Although this mouse model has been known since 1956, there are no guidelines for breeding wobbler mice. Due to the lack of such guidelines the present study tries to close this gap and implements a manual for further studies. It includes the whole workflow in regard to wobbler mice from breeding and animal care taking, genotyping and phenotype analysis, but also gives some examples for the use of various neuronal tissues for histological investigation. Beside the progress in research a second aim should always be the enhancement of mouse welfare and reduction of stress for the laboratory animals. Copyright © 2015 Elsevier GmbH. All rights reserved.

Recent improvements in SPE3D: a VR-based surgery planning environment

NASA Astrophysics Data System (ADS)

Witkowski, Marcin; Sitnik, Robert; Verdonschot, Nico

2014-02-01

SPE3D is a surgery planning environment developed within TLEMsafe project [1] (funded by the European Commission FP7). It enables the operator to plan a surgical procedure on the customized musculoskeletal (MS) model of the patient's lower limbs, send the modified model to the biomechanical analysis module, and export the scenario's parameters to the surgical navigation system. The personalized patient-specific three-dimensional (3-D) MS model is registered with 3-D MRI dataset of lower limbs and the two modalities may be visualized simultaneously. Apart from main planes, any arbitrary MRI cross-section can be rendered on the 3-D MS model in real time. The interface provides tools for: bone cutting, manipulating and removal, repositioning muscle insertion points, modifying muscle force, removing muscles and placing implants stored in the implant library. SPE3D supports stereoscopic viewing as well as natural inspection/manipulation with use of haptic devices. Alternatively, it may be controlled with use of a standard computer keyboard, mouse and 2D display or a touch screen (e.g. in an operating room). The interface may be utilized in two main fields. Experienced surgeons may use it to simulate their operative plans and prepare input data for a surgical navigation system while student or novice surgeons can use it for training.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers

PubMed Central

Mann, Michael B

2018-01-01

Abstract Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization. PMID:29059366

Rational Design of Mouse Models for Cancer Research.

PubMed

Landgraf, Marietta; McGovern, Jacqui A; Friedl, Peter; Hutmacher, Dietmar W

2018-03-01

The laboratory mouse is widely considered as a valid and affordable model organism to study human disease. Attempts to improve the relevance of murine models for the investigation of human pathologies led to the development of various genetically engineered, xenograft and humanized mouse models. Nevertheless, most preclinical studies in mice suffer from insufficient predictive value when compared with cancer biology and therapy response of human patients. We propose an innovative strategy to improve the predictive power of preclinical cancer models. Combining (i) genomic, tissue engineering and regenerative medicine approaches for rational design of mouse models with (ii) rapid prototyping and computational benchmarking against human clinical data will enable fast and nonbiased validation of newly generated models. Copyright © 2017 Elsevier Ltd. All rights reserved.

EMAGE mouse embryo spatial gene expression database: 2010 update

PubMed Central

Richardson, Lorna; Venkataraman, Shanmugasundaram; Stevenson, Peter; Yang, Yiya; Burton, Nicholas; Rao, Jianguo; Fisher, Malcolm; Baldock, Richard A.; Davidson, Duncan R.; Christiansen, Jeffrey H.

2010-01-01

EMAGE (http://www.emouseatlas.org/emage) is a freely available online database of in situ gene expression patterns in the developing mouse embryo. Gene expression domains from raw images are extracted and integrated spatially into a set of standard 3D virtual mouse embryos at different stages of development, which allows data interrogation by spatial methods. An anatomy ontology is also used to describe sites of expression, which allows data to be queried using text-based methods. Here, we describe recent enhancements to EMAGE including: the release of a completely re-designed website, which offers integration of many different search functions in HTML web pages, improved user feedback and the ability to find similar expression patterns at the click of a button; back-end refactoring from an object oriented to relational architecture, allowing associated SQL access; and the provision of further access by standard formatted URLs and a Java API. We have also increased data coverage by sourcing from a greater selection of journals and developed automated methods for spatial data annotation that are being applied to spatially incorporate the genome-wide (∼19 000 gene) ‘EURExpress’ dataset into EMAGE. PMID:19767607

Indian-Ink Perfusion Based Method for Reconstructing Continuous Vascular Networks in Whole Mouse Brain

PubMed Central

Xue, Songchao; Gong, Hui; Jiang, Tao; Luo, Weihua; Meng, Yuanzheng; Liu, Qian; Chen, Shangbin; Li, Anan

2014-01-01

The topology of the cerebral vasculature, which is the energy transport corridor of the brain, can be used to study cerebral circulatory pathways. Limited by the restrictions of the vascular markers and imaging methods, studies on cerebral vascular structure now mainly focus on either observation of the macro vessels in a whole brain or imaging of the micro vessels in a small region. Simultaneous vascular studies of arteries, veins and capillaries have not been achieved in the whole brain of mammals. Here, we have combined the improved gelatin-Indian ink vessel perfusion process with Micro-Optical Sectioning Tomography for imaging the vessel network of an entire mouse brain. With 17 days of work, an integral dataset for the entire cerebral vessels was acquired. The voxel resolution is 0.35×0.4×2.0 µm3 for the whole brain. Besides the observations of fine and complex vascular networks in the reconstructed slices and entire brain views, a representative continuous vascular tracking has been demonstrated in the deep thalamus. This study provided an effective method for studying the entire macro and micro vascular networks of mouse brain simultaneously. PMID:24498247

Identification of Candidate B-Lymphoma Genes by Cross-Species Gene Expression Profiling

PubMed Central

Tompkins, Van S.; Han, Seong-Su; Olivier, Alicia; Syrbu, Sergei; Bair, Thomas; Button, Anna; Jacobus, Laura; Wang, Zebin; Lifton, Samuel; Raychaudhuri, Pradip; Morse, Herbert C.; Weiner, George; Link, Brian; Smith, Brian J.; Janz, Siegfried

2013-01-01

Comparative genome-wide expression profiling of malignant tumor counterparts across the human-mouse species barrier has a successful track record as a gene discovery tool in liver, breast, lung, prostate and other cancers, but has been largely neglected in studies on neoplasms of mature B-lymphocytes such as diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma (BL). We used global gene expression profiles of DLBCL-like tumors that arose spontaneously in Myc-transgenic C57BL/6 mice as a phylogenetically conserved filter for analyzing the human DLBCL transcriptome. The human and mouse lymphomas were found to have 60 concordantly deregulated genes in common, including 8 genes that Cox hazard regression analysis associated with overall survival in a published landmark dataset of DLBCL. Genetic network analysis of the 60 genes followed by biological validation studies indicate FOXM1 as a candidate DLBCL and BL gene, supporting a number of studies contending that FOXM1 is a therapeutic target in mature B cell tumors. Our findings demonstrate the value of the “mouse filter” for genomic studies of human B-lineage neoplasms for which a vast knowledge base already exists. PMID:24130802

Review of DoD Malaria Research Programs,

DTIC Science & Technology

1992-05-01

the irraliated sporozoite vaccine. Work in the mouse model system and then extrapolate to human malarias. Study naturally acquired immune ...recombinant vaccines. Work simultaneously in the mouse model system and with human malarias. 3. Identify targets and mechanisms of protective immunity not...multivalent vaccines that attack these same targets. 3. Working again in the mouse model, non- human primate model, andI human systems we

Animal models for prenatal gene therapy: rodent models for prenatal gene therapy.

PubMed

Roybal, Jessica L; Endo, Masayuki; Buckley, Suzanne M K; Herbert, Bronwen R; Waddington, Simon N; Flake, Alan W

2012-01-01

Fetal gene transfer has been studied in various animal models, including rabbits, guinea pigs, cats, dogs, and nonhuman primate; however, the most common model is the rodent, particularly the mouse. There are numerous advantages to mouse models, including a short gestation time of around 20 days, large litter size usually of more than six pups, ease of colony maintenance due to the small physical size, and the relatively low expense of doing so. Moreover, the mouse genome is well defined, there are many transgenic models particularly of human monogenetic disorders, and mouse-specific biological reagents are readily available. One criticism has been that it is difficult to perform procedures on the fetal mouse with suitable accuracy. Over the past decade, accumulation of technical expertise and development of technology such as high-frequency ultrasound have permitted accurate vector delivery to organs and tissues. Here, we describe our experiences of gene transfer to the fetal mouse with and without ultrasound guidance from mid to late gestation. Depending upon the vector type, the route of delivery and the age of the fetus, specific or widespread gene transfer can be achieved, making fetal mice excellent models for exploratory biodistribution studies.

Bioinformatic Integration of Molecular Networks and Major Pathways Involved in Mice Cochlear and Vestibular Supporting Cells.

PubMed

Requena, Teresa; Gallego-Martinez, Alvaro; Lopez-Escamez, Jose A

2018-01-01

Background : Cochlear and vestibular epithelial non-hair cells (ENHCs) are the supporting elements of the cellular architecture in the organ of Corti and the vestibular neuroepithelium in the inner ear. Intercellular and cell-extracellular matrix interactions are essential to prevent an abnormal ion redistribution leading to hearing and vestibular loss. The aim of this study is to define the main pathways and molecular networks in the mouse ENHCs. Methods : We retrieved microarray and RNA-seq datasets from mouse epithelial sensory and non-sensory cells from gEAR portal (http://umgear.org/index.html) and obtained gene expression fold-change between ENHCs and non-epithelial cells (NECs) against HCs for each gene. Differentially expressed genes (DEG) with a log2 fold change between 1 and -1 were discarded. The remaining genes were selected to search for interactions using Ingenuity Pathway Analysis and STRING platform. Specific molecular networks for ENHCs in the cochlea and the vestibular organs were generated and significant pathways were identified. Results : Between 1723 and 1559 DEG were found in the mouse cochlear and vestibular tissues, respectively. Six main pathways showed enrichment in the supporting cells in both tissues: (1) "Inhibition of Matrix Metalloproteases"; (2) "Calcium Transport I"; (3) "Calcium Signaling"; (4) "Leukocyte Extravasation Signaling"; (5) "Signaling by Rho Family GTPases"; and (6) "Axonal Guidance Si". In the mouse cochlea, ENHCs showed a significant enrichment in 18 pathways highlighting "axonal guidance signaling (AGS)" ( p = 4.37 × 10 -8 ) and "RhoGDI Signaling" ( p = 3.31 × 10 -8 ). In the vestibular dataset, there were 20 enriched pathways in ENHCs, the most significant being "Leukocyte Extravasation Signaling" ( p = 8.71 × 10 -6 ), "Signaling by Rho Family GTPases" ( p = 1.20 × 10 -5 ) and "Calcium Signaling" ( p = 1.20 × 10 -5 ). Among the top ranked networks, the most biologically significant network contained the "auditory and vestibular system development and function" terms. We also found 108 genes showing tonotopic gene expression in the cochlear ENHCs. Conclusions : We have predicted the main pathways and molecular networks for ENHCs in the organ of Corti and vestibular neuroepithelium. These pathways will facilitate the design of molecular maps to select novel candidate genes for hearing or vestibular loss to conduct functional studies.

Treatment of d-galactose induced mouse aging with Lycium barbarum polysaccharides and its mechanism study.

PubMed

Tang, Tao; He, Bixiu

2013-01-01

We evaluated the effects of Lycium barbarum polysaccharides LBP) on D-galactose aging model mouse, and explored its possible mechanism. Kunming mice were randomly divided into the control group, the model group, the high-dose LBP group, and the low-dose LBP group. Except the control group, D-galactose was used for modelling. The drug was administrated when modelling. Mouse behavioural, learning and memory changes were observed, and the contents of lipid peroxidation (LPO), lipofuscin (LF) and monoamine oxidase B (MAO-B) in mouse brain tissue and the weight of immune organs were measured after 6 weeks. Compared with the control group, mouse weight gain in the model group reduced significantly. Compared with model group, after mice drank LBP, the times of electric shock was less than aging mice (in which, the high-dose LBP group, P<0.05), and electric shock incubation period was longer (P<0.01). On Day 45 after modelling and drug administration, the contents of LPO, LF and MAO-B in mouse brain tissue in the model group increased significantly, while those in the drug administration groups decreased significantly. The thymus index in the aging model group decreased significantly; the thymus index and the spleen index in the high-dose LBP group and the low-dose LBP group rebounded significantly (P<0.01). We concluded that LBP has an anti-aging effect on D-galactose induced aging model mouse, and its mechanism may be related with the alleviation of glucose metabolism disorder and the resistance of the generation of lipid peroxide and other substances, which damage cell membrane lipid.

Automated Morphological Analysis of Microglia After Stroke.

PubMed

Heindl, Steffanie; Gesierich, Benno; Benakis, Corinne; Llovera, Gemma; Duering, Marco; Liesz, Arthur

2018-01-01

Microglia are the resident immune cells of the brain and react quickly to changes in their environment with transcriptional regulation and morphological changes. Brain tissue injury such as ischemic stroke induces a local inflammatory response encompassing microglial activation. The change in activation status of a microglia is reflected in its gradual morphological transformation from a highly ramified into a less ramified or amoeboid cell shape. For this reason, the morphological changes of microglia are widely utilized to quantify microglial activation and studying their involvement in virtually all brain diseases. However, the currently available methods, which are mainly based on manual rating of immunofluorescent microscopic images, are often inaccurate, rater biased, and highly time consuming. To address these issues, we created a fully automated image analysis tool, which enables the analysis of microglia morphology from a confocal Z-stack and providing up to 59 morphological features. We developed the algorithm on an exploratory dataset of microglial cells from a stroke mouse model and validated the findings on an independent data set. In both datasets, we could demonstrate the ability of the algorithm to sensitively discriminate between the microglia morphology in the peri-infarct and the contralateral, unaffected cortex. Dimensionality reduction by principal component analysis allowed to generate a highly sensitive compound score for microglial shape analysis. Finally, we tested for concordance of results between the novel automated analysis tool and the conventional manual analysis and found a high degree of correlation. In conclusion, our novel method for the fully automatized analysis of microglia morphology shows excellent accuracy and time efficacy compared to traditional analysis methods. This tool, which we make openly available, could find application to study microglia morphology using fluorescence imaging in a wide range of brain disease models.

Being an honest broker of hydrology: Uncovering, communicating and addressing model error in a climate change streamflow dataset

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Pytlak, E.

2017-12-01

Any model simulation has errors, including errors in meteorological data, process understanding, model structure, and model parameters. These errors may express themselves as bias, timing lags, and differences in sensitivity between the model and the physical world. The evaluation and handling of these errors can greatly affect the legitimacy, validity and usefulness of the resulting scientific product. In this presentation we will discuss a case study of handling and communicating model errors during the development of a hydrologic climate change dataset for the Pacific Northwestern United States. The dataset was the result of a four-year collaboration between the University of Washington, Oregon State University, the Bonneville Power Administration, the United States Army Corps of Engineers and the Bureau of Reclamation. Along the way, the partnership facilitated the discovery of multiple systematic errors in the streamflow dataset. Through an iterative review process, some of those errors could be resolved. For the errors that remained, honest communication of the shortcomings promoted the dataset's legitimacy. Thoroughly explaining errors also improved ways in which the dataset would be used in follow-on impact studies. Finally, we will discuss the development of the "streamflow bias-correction" step often applied to climate change datasets that will be used in impact modeling contexts. We will describe the development of a series of bias-correction techniques through close collaboration among universities and stakeholders. Through that process, both universities and stakeholders learned about the others' expectations and workflows. This mutual learning process allowed for the development of methods that accommodated the stakeholders' specific engineering requirements. The iterative revision process also produced a functional and actionable dataset while preserving its scientific merit. We will describe how encountering earlier techniques' pitfalls allowed us to develop improved methods for scientists and practitioners alike.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

The Balance-Scale Task Revisited: A Comparison of Statistical Models for Rule-Based and Information-Integration Theories of Proportional Reasoning

PubMed Central

Hofman, Abe D.; Visser, Ingmar; Jansen, Brenda R. J.; van der Maas, Han L. J.

2015-01-01

We propose and test three statistical models for the analysis of children’s responses to the balance scale task, a seminal task to study proportional reasoning. We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted Sum Model, following from an information-integration approach. Moreover, a hybrid LCM using item covariates is proposed, combining aspects of both a rule-based and information-integration perspective. These models are applied to two different datasets, a standard paper-and-pencil test dataset (N = 779), and a dataset collected within an online learning environment that included direct feedback, time-pressure, and a reward system (N = 808). For the paper-and-pencil dataset the RB LCM resulted in the best fit, whereas for the online dataset the hybrid LCM provided the best fit. The standard paper-and-pencil dataset yielded more evidence for distinct solution rules than the online data set in which quantitative item characteristics are more prominent in determining responses. These results shed new light on the discussion on sequential rule-based and information-integration perspectives of cognitive development. PMID:26505905

Mutagenicity testing with transgenic mice. Part II: Comparison with the mouse spot test

PubMed Central

Wahnschaffe, Ulrich; Bitsch, Annette; Kielhorn, Janet; Mangelsdorf, Inge

2005-01-01

The mouse spot test, an in vivo mutation assay, has been used to assess a number of chemicals. It is at present the only in vivo mammalian test system capable of detecting somatic gene mutations according to OECD guidelines (OECD guideline 484). It is however rather insensitive, animal consuming and expensive type of test. More recently several assays using transgenic animals have been developed. From data in the literature, the present study compares the results of in vivo testing of over twenty chemicals using the mouse spot test and compares them with results from the two transgenic mouse models with the best data base available, the lacI model (commercially available as the Big Blue® mouse), and the lacZ model (commercially available as the Muta™ Mouse). There was agreement in the results from the majority of substances. No differences were found in the predictability of the transgenic animal assays and the mouse spot test for carcinogenicity. However, from the limited data available, it seems that the transgenic mouse assay has several advantages over the mouse spot test and may be a suitable test system replacing the mouse spot test for detection of gene but not chromosome mutations in vivo. PMID:15676065

The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease.

PubMed

Eppig, Janan T; Blake, Judith A; Bult, Carol J; Kadin, James A; Richardson, Joel E

2015-01-01

The Mouse Genome Database (MGD, http://www.informatics.jax.org) serves the international biomedical research community as the central resource for integrated genomic, genetic and biological data on the laboratory mouse. To facilitate use of mouse as a model in translational studies, MGD maintains a core of high-quality curated data and integrates experimentally and computationally generated data sets. MGD maintains a unified catalog of genes and genome features, including functional RNAs, QTL and phenotypic loci. MGD curates and provides functional and phenotype annotations for mouse genes using the Gene Ontology and Mammalian Phenotype Ontology. MGD integrates phenotype data and associates mouse genotypes to human diseases, providing critical mouse-human relationships and access to repositories holding mouse models. MGD is the authoritative source of nomenclature for genes, genome features, alleles and strains following guidelines of the International Committee on Standardized Genetic Nomenclature for Mice. A new addition to MGD, the Human-Mouse: Disease Connection, allows users to explore gene-phenotype-disease relationships between human and mouse. MGD has also updated search paradigms for phenotypic allele attributes, incorporated incidental mutation data, added a module for display and exploration of genes and microRNA interactions and adopted the JBrowse genome browser. MGD resources are freely available to the scientific community. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Genetically engineered mouse models of craniopharyngioma: an opportunity for therapy development and understanding of tumor biology

PubMed Central

Martinez‐Barbera, Juan Pedro

2017-01-01

Abstract Adamantinomatous craniopharyngioma (ACP) is the commonest tumor of the sellar region in childhood. Two genetically engineered mouse models have been developed and are giving valuable insights into ACP biology. These models have identified novel pathways activated in tumors, revealed an important function of paracrine signalling and extended conventional theories about the role of organ‐specific stem cells in tumorigenesis. In this review, we summarize these mouse models, what has been learnt, their limitations and open questions for future research. We then discussed how these mouse models may be used to test novel therapeutics against potentially targetable pathways recently identified in human ACP. PMID:28414891

Mouse neuroblastoma cell based model and the effect of epileptic events on calcium oscillations and neural spikes

NASA Astrophysics Data System (ADS)

Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

2013-05-01

Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.

Brokering technologies to realize the hydrology scenario in NSF BCube

NASA Astrophysics Data System (ADS)

Boldrini, Enrico; Easton, Zachary; Fuka, Daniel; Pearlman, Jay; Nativi, Stefano

2015-04-01

In the National Science Foundation (NSF) BCube project an international team composed of cyber infrastructure experts, geoscientists, social scientists and educators are working together to explore the use of brokering technologies, initially focusing on four domains: hydrology, oceans, polar, and weather. In the hydrology domain, environmental models are fundamental to understand the behaviour of hydrological systems. A specific model usually requires datasets coming from different disciplines for its initialization (e.g. elevation models from Earth observation, weather data from Atmospheric sciences, etc.). Scientific datasets are usually available on heterogeneous publishing services, such as inventory and access services (e.g. OGC Web Coverage Service, THREDDS Data Server, etc.). Indeed, datasets are published according to different protocols, moreover they usually come in different formats, resolutions, Coordinate Reference Systems (CRSs): in short different grid environments depending on the original data and the publishing service processing capabilities. Scientists can thus be impeded by the burden of discovery, access and normalize the desired datasets to the grid environment required by the model. These technological tasks of course divert scientists from their main, scientific goals. The use of GI-axe brokering framework has been experimented in a hydrology scenario where scientists needed to compare a particular hydrological model with two different input datasets (digital elevation models): - the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) dataset, v.2. - the Shuttle Radar Topography Mission (SRTM) dataset, v.3. These datasets were published by means of Hyrax Server technology, which can provide NetCDF files at their original resolution and CRS. Scientists had their model running on ArcGIS, so the main goal was to import the datasets using the available ArcPy library and have EPSG:4326 with the same resolution grid as the reference system, so that model outputs could be compared. ArcPy however is able to access only GeoTIff datasets that are published by a OGC Web Coverage Service (WCS). The GI-axe broker has then been deployed between the client application and the data providers. It has been configured to broker the two different Hyrax service endpoints and republish the data content through a WCS interface for the use of the ArcPy library. Finally, scientists were able to easily run the model, and to concentrate on the comparison of the different results obtained according to the selected input dataset. The use of a third party broker to perform such technological tasks has also shown to have the potential advantage of increasing the repeatability of a study among different researchers.

On the mutual relationship between conceptual models and datasets in geophysical monitoring of volcanic systems

NASA Astrophysics Data System (ADS)

Neuberg, J. W.; Thomas, M.; Pascal, K.; Karl, S.

2012-04-01

Geophysical datasets are essential to guide particularly short-term forecasting of volcanic activity. Key parameters are derived from these datasets and interpreted in different ways, however, the biggest impact on the interpretation is not determined by the range of parameters but controlled through the parameterisation and the underlying conceptual model of the volcanic process. On the other hand, the increasing number of sophisticated geophysical models need to be constrained by monitoring data, to transform a merely numerical exercise into a useful forecasting tool. We utilise datasets from the "big three", seismology, deformation and gas emissions, to gain insight in the mutual relationship between conceptual models and constraining data. We show that, e.g. the same seismic dataset can be interpreted with respect to a wide variety of different models with very different implications to forecasting. In turn, different data processing procedures lead to different outcomes even though they are based on the same conceptual model. Unsurprisingly, the most reliable interpretation will be achieved by employing multi-disciplinary models with overlapping constraints.

Dynamics of circulating gamma delta T cell activity in an immunocompetent mouse model of high-grade glioma

USDA-ARS?s Scientific Manuscript database

Human gamma delta T cells are potent effectors against glioma cell lines in vitro and in human/mouse xenograft models of glioblastoma, however, this effect has not been investigated in an immunocompetent mouse model. In this report, we established GL261 intracranial gliomas in syngeneic WT C57BL/6 m...

Lack of species-specific difference in pulmonary function when using mouse versus human plasma in a mouse model of hemorrhagic shock.

PubMed

Peng, Zhanglong; Pati, Shibani; Fontaine, Magali J; Hall, Kelly; Herrera, Anthony V; Kozar, Rosemary A

2016-11-01

Clinical studies have demonstrated that the early and empiric use of plasma improves survival after hemorrhagic shock. We have demonstrated in rodent models of hemorrhagic shock that resuscitation with plasma is protective to the lungs compared with lactated Ringer's solution. As our long-term objective is to determine the molecular mechanisms that modulate plasma's protective effects in injured bleeding patients, we have used human plasma in a mouse model of hemorrhagic shock. The goal of the current experiments is to determine if there are significant adverse effects on lung injury when using human versus mouse plasma in an established murine model of hemorrhagic shock and laparotomy. Mice underwent laparotomy and 90 minutes of hemorrhagic shock to a mean arterial pressure (MAP) of 35 ± 5 mm Hg followed by resuscitation at 1× shed blood using either mouse fresh frozen plasma (FFP), human FFP, or human lyophilized plasma. Mean arterial pressure was recorded during shock and for the first 30 minutes of resuscitation. After 3 hours, animals were killed, and lungs collected for analysis. There was a significant increase in early MAP when mouse FFP was used to resuscitate animals compared with human FFP or human lyophilized plasma. However, despite these differences, analysis of the mouse lungs revealed no significant differences in pulmonary histopathology, lung permeability, or lung edema between all three plasma groups. Analysis of neutrophil infiltration in the lungs revealed that mouse FFP decreased neutrophil influx as measured by neutrophil staining; however, myeloperoxidase immunostaining revealed no significant differences in between groups. The study of human plasma in a mouse model of hemorrhagic shock is feasible but does reveal some differences compared with mouse plasma-based resuscitation in physiologic measures such as MAP postresuscitation. Measures of end organ function such as lung injury appear to be comparable in this acute model of hemorrhagic shock and resuscitation.

In vivo quantitative bioluminescence tomography using heterogeneous and homogeneous mouse models.

PubMed

Liu, Junting; Wang, Yabin; Qu, Xiaochao; Li, Xiangsi; Ma, Xiaopeng; Han, Runqiang; Hu, Zhenhua; Chen, Xueli; Sun, Dongdong; Zhang, Rongqing; Chen, Duofang; Chen, Dan; Chen, Xiaoyuan; Liang, Jimin; Cao, Feng; Tian, Jie

2010-06-07

Bioluminescence tomography (BLT) is a new optical molecular imaging modality, which can monitor both physiological and pathological processes by using bioluminescent light-emitting probes in small living animal. Especially, this technology possesses great potential in drug development, early detection, and therapy monitoring in preclinical settings. In the present study, we developed a dual modality BLT prototype system with Micro-computed tomography (MicroCT) registration approach, and improved the quantitative reconstruction algorithm based on adaptive hp finite element method (hp-FEM). Detailed comparisons of source reconstruction between the heterogeneous and homogeneous mouse models were performed. The models include mice with implanted luminescence source and tumor-bearing mice with firefly luciferase report gene. Our data suggest that the reconstruction based on heterogeneous mouse model is more accurate in localization and quantification than the homogeneous mouse model with appropriate optical parameters and that BLT allows super-early tumor detection in vivo based on tomographic reconstruction of heterogeneous mouse model signal.

Of Mice and Men: Comparative Analysis of Neuro-Inflammatory Mechanisms in Human and Mouse Using Cause-and-Effect Models.

PubMed

Kodamullil, Alpha Tom; Iyappan, Anandhi; Karki, Reagon; Madan, Sumit; Younesi, Erfan; Hofmann-Apitius, Martin

2017-01-01

Perturbance in inflammatory pathways have been identified as one of the major factors which leads to neurodegenerative diseases (NDD). Owing to the limited access of human brain tissues and the immense complexity of the brain, animal models, specifically mouse models, play a key role in advancing the NDD field. However, many of these mouse models fail to reproduce the clinical manifestations and end points of the disease. NDD drugs, which passed the efficacy test in mice, were repeatedly not successful in clinical trials. There are numerous studies which are supporting and opposing the applicability of mouse models in neuroinflammation and NDD. In this paper, we assessed to what extend a mouse can mimic the cellular and molecular interactions in humans at a mechanism level. Based on our mechanistic modeling approach, we investigate the failure of a neuroinflammation targeted drug in the late phases of clinical trials based on the comparative analyses between the two species.

NCI Mouse Repository | FNLCR Staging

Cancer.gov

The NCI Mouse Repository is an NCI-funded resource for mouse cancer models and associated strains. The repository makes strains available to all members of the scientific community (academic, non-profit, and commercial). NCI Mouse Repository strains

Using Graph Indices for the Analysis and Comparison of Chemical Datasets.

PubMed

Fourches, Denis; Tropsha, Alexander

2013-10-01

In cheminformatics, compounds are represented as points in multidimensional space of chemical descriptors. When all pairs of points found within certain distance threshold in the original high dimensional chemistry space are connected by distance-labeled edges, the resulting data structure can be defined as Dataset Graph (DG). We show that, similarly to the conventional description of organic molecules, many graph indices can be computed for DGs as well. We demonstrate that chemical datasets can be effectively characterized and compared by computing simple graph indices such as the average vertex degree or Randic connectivity index. This approach is used to characterize and quantify the similarity between different datasets or subsets of the same dataset (e.g., training, test, and external validation sets used in QSAR modeling). The freely available ADDAGRA program has been implemented to build and visualize DGs. The approach proposed and discussed in this report could be further explored and utilized for different cheminformatics applications such as dataset diversification by acquiring external compounds, dataset processing prior to QSAR modeling, or (dis)similarity modeling of multiple datasets studied in chemical genomics applications. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

High-throughput engineering of a mammalian genome reveals building principles of methylation states at CG rich regions.

PubMed

Krebs, Arnaud R; Dessus-Babus, Sophie; Burger, Lukas; Schübeler, Dirk

2014-09-26

The majority of mammalian promoters are CpG islands; regions of high CG density that require protection from DNA methylation to be functional. Importantly, how sequence architecture mediates this unmethylated state remains unclear. To address this question in a comprehensive manner, we developed a method to interrogate methylation states of hundreds of sequence variants inserted at the same genomic site in mouse embryonic stem cells. Using this assay, we were able to quantify the contribution of various sequence motifs towards the resulting DNA methylation state. Modeling of this comprehensive dataset revealed that CG density alone is a minor determinant of their unmethylated state. Instead, these data argue for a principal role for transcription factor binding sites, a prediction confirmed by testing synthetic mutant libraries. Taken together, these findings establish the hierarchy between the two cis-encoded mechanisms that define the DNA methylation state and thus the transcriptional competence of CpG islands.

Estimating replicate time shifts using Gaussian process regression

PubMed Central

Liu, Qiang; Andersen, Bogi; Smyth, Padhraic; Ihler, Alexander

2010-01-01

Motivation: Time-course gene expression datasets provide important insights into dynamic aspects of biological processes, such as circadian rhythms, cell cycle and organ development. In a typical microarray time-course experiment, measurements are obtained at each time point from multiple replicate samples. Accurately recovering the gene expression patterns from experimental observations is made challenging by both measurement noise and variation among replicates' rates of development. Prior work on this topic has focused on inference of expression patterns assuming that the replicate times are synchronized. We develop a statistical approach that simultaneously infers both (i) the underlying (hidden) expression profile for each gene, as well as (ii) the biological time for each individual replicate. Our approach is based on Gaussian process regression (GPR) combined with a probabilistic model that accounts for uncertainty about the biological development time of each replicate. Results: We apply GPR with uncertain measurement times to a microarray dataset of mRNA expression for the hair-growth cycle in mouse back skin, predicting both profile shapes and biological times for each replicate. The predicted time shifts show high consistency with independently obtained morphological estimates of relative development. We also show that the method systematically reduces prediction error on out-of-sample data, significantly reducing the mean squared error in a cross-validation study. Availability: Matlab code for GPR with uncertain time shifts is available at http://sli.ics.uci.edu/Code/GPRTimeshift/ Contact: ihler@ics.uci.edu PMID:20147305

An extended Kalman filter for mouse tracking.

PubMed

Choi, Hongjun; Kim, Mingi; Lee, Onseok

2018-05-19

Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse. Graphical abstract.

Mouse Models for Down Syndrome-Associated Developmental Cognitive Disabilities

PubMed Central

Liu, Chunhong; Belichenko, Pavel V.; Zhang, Li; Fu, Dawei; Kleschevnikov, Alexander M.; Baldini, Antonio; Antonarakis, Stylianos E.; Mobley, William C.; Yu, Y. Eugene

2011-01-01

Down syndrome (DS) is mainly caused by the presence of an extra copy of human chromosome 21 (Hsa21) and is a leading genetic cause for developmental cognitive disabilities in humans. The mouse is a premier model organism for DS because the regions on Hsa21 are syntenically conserved with three regions in the mouse genome, which are located on mouse chromosome 10 (Mmu10), Mmu16 and Mmu17. With the advance of chromosomal manipulation technologies, new mouse mutants have been generated to mimic DS at both the genotypic and phenotypic levels. Further mouse-based molecular genetic studies in the future may lead to the unraveling of the mechanisms underlying DS-associated developmental cognitive disabilities, which would lay the groundwork for developing effective treatments for this phenotypic manifestation. In this review, we will discuss recent progress and future challenges in modeling DS-associated developmental cognitive disability in mice with an emphasis on hippocampus-related phenotypes. PMID:21865664

Convective - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

LANL - Convective - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

LANL - Neutral - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

Validation of Short-Term Noise Assessment Procedures: FY16 Summary of Procedures, Progress, and Preliminary Results

DTIC Science & Technology

Validation project. This report describes the procedure used to generate the noise models output dataset , and then it compares that dataset to the...benchmark, the Engineer Research and Development Centers Long-Range Sound Propagation dataset . It was found that the models consistently underpredict the

Methods in Molecular Biology Mouse Genetics: Methods and Protocols | Center for Cancer Research

Cancer.gov

Mouse Genetics: Methods and Protocols provides selected mouse genetic techniques and their application in modeling varieties of human diseases. The chapters are mainly focused on the generation of different transgenic mice to accomplish the manipulation of genes of interest, tracing cell lineages, and modeling human diseases.

The Long Non-Coding RNA Transcriptome Landscape in CHO Cells Under Batch and Fed-Batch Conditions.

PubMed

Vito, Davide; Smales, C Mark

2018-05-21

The role of non-coding RNAs in determining growth, productivity and recombinant product quality attributes in Chinese hamster ovary (CHO) cells has received much attention in recent years, exemplified by studies into microRNAs in particular. However, other classes of non-coding RNAs have received less attention. One such class are the non-coding RNAs known collectively as long non-coding RNAs (lncRNAs). We have undertaken the first landscape analysis of the lncRNA transcriptome in CHO using a mouse based microarray that also provided for the surveillance of the coding transcriptome. We report on those lncRNAs present in a model host CHO cell line under batch and fed-batch conditions on two different days and relate the expression of different lncRNAs to each other. We demonstrate that the mouse microarray was suitable for the detection and analysis of thousands of CHO lncRNAs and validated a number of these by qRT-PCR. We then further analysed the data to identify those lncRNAs whose expression changed the most between growth and stationary phases of culture or between batch and fed-batch culture to identify potential lncRNA targets for further functional studies with regard to their role in controlling growth of CHO cells. We discuss the implications for the publication of this rich dataset and how this may be used by the community. This article is protected by copyright. All rights reserved.

Use of mouse models to study the mechanisms and consequences of RBC clearance

PubMed Central

Hod, E. A.; Arinsburg, S. A.; Francis, R. O.; Hendrickson, J. E.; Zimring, J. C.; Spitalnik, S. L.

2013-01-01

Mice provide tractable animal models for studying the pathophysiology of various human disorders. This review discusses the use of mouse models for understanding red-blood-cell (RBC) clearance. These models provide important insights into the pathophysiology of various clinically relevant entities, such as autoimmune haemolytic anaemia, haemolytic transfusion reactions, other complications of RBC transfusions and immunomodulation by Rh immune globulin therapy. Mouse models of both antibody- and non-antibody-mediated RBC clearance are reviewed. Approaches for exploring unanswered questions in transfusion medicine using these models are also discussed. PMID:20345515

Generation Of A Mouse Model For Schwannomatosis

DTIC Science & Technology

2010-09-01

TITLE: Generation of a Mouse Model for Schwannomatosis PRINCIPAL INVESTIGATOR: Long-Sheng Chang, Ph.D. CONTRACTING ORGANIZATION: The...Annual 3. DATES COVERED (From - To) 1 Sep 2009 - 31 Aug 2010 4. TITLE AND SUBTITLE Generation of a Mouse Model for Schwannomatosis 5a. CONTRACT...hypothesis involving inactivation of both the INI1/SNF5 and NF2 tumor suppressor genes in the formation of schwannomatosis -associated tumors. To

Mouse Genome Database: From sequence to phenotypes and disease models

PubMed Central

Richardson, Joel E.; Kadin, James A.; Smith, Cynthia L.; Blake, Judith A.; Bult, Carol J.

2015-01-01

Summary The Mouse Genome Database (MGD, www.informatics.jax.org) is the international scientific database for genetic, genomic, and biological data on the laboratory mouse to support the research requirements of the biomedical community. To accomplish this goal, MGD provides broad data coverage, serves as the authoritative standard for mouse nomenclature for genes, mutants, and strains, and curates and integrates many types of data from literature and electronic sources. Among the key data sets MGD supports are: the complete catalog of mouse genes and genome features, comparative homology data for mouse and vertebrate genes, the authoritative set of Gene Ontology (GO) annotations for mouse gene functions, a comprehensive catalog of mouse mutations and their phenotypes, and a curated compendium of mouse models of human diseases. Here, we describe the data acquisition process, specifics about MGD's key data areas, methods to access and query MGD data, and outreach and user help facilities. genesis 53:458–473, 2015. © 2015 The Authors. Genesis Published by Wiley Periodicals, Inc. PMID:26150326

QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR

PubMed Central

Chauhan, Jagat Singh; Dhanda, Sandeep Kumar; Singla, Deepak; Agarwal, Subhash M.; Raghava, Gajendra P. S.

2014-01-01

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs. PMID:24992720

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

PubMed

Nie, Zhi; Vairavan, Srinivasan; Narayan, Vaibhav A; Ye, Jieping; Li, Qingqin S

2018-01-01

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70-0.78 and 0.72-0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

NASA Astrophysics Data System (ADS)

Kim, In-Won; Oh, Jaiho; Woo, Sumin; Kripalani, R. H.

2018-04-01

In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and low-resolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

Establishing a laboratory animal model from a transgenic animal: RasH2 mice as a model for carcinogenicity studies in regulatory science.

PubMed

Urano, K; Tamaoki, N; Nomura, T

2012-01-01

Transgenic animal models have been used in small numbers in gene function studies in vivo for a period of time, but more recently, the use of a single transgenic animal model has been approved as a second species, 6-month alternative (to the routine 2-year, 2-animal model) used in short-term carcinogenicity studies for generating regulatory application data of new drugs. This article addresses many of the issues associated with the creation and use of one of these transgenic models, the rasH2 mouse, for regulatory science. The discussion includes strategies for mass producing mice with the same stable phenotype, including constructing the transgene, choosing a founder mouse, and controlling both the transgene and background genes; strategies for developing the model for regulatory science, including measurements of carcinogen susceptibility, stability of a large-scale production system, and monitoring for uniform carcinogenicity responses; and finally, efficient use of the transgenic animal model on study. Approximately 20% of mouse carcinogenicity studies for new drug applications in the United States currently use transgenic models, typically the rasH2 mouse. The rasH2 mouse could contribute to animal welfare by reducing the numbers of animals used as well as reducing the cost of carcinogenicity studies. A better understanding of the advantages and disadvantages of the transgenic rasH2 mouse will result in greater and more efficient use of this animal model in the future.

NCI Mouse Repository | Frederick National Laboratory for Cancer Research

Cancer.gov

The NCI Mouse Repository is an NCI-funded resource for mouse cancer models and associated strains. The repository makes strains available to all members of the scientific community (academic, non-profit, and commercial). NCI Mouse Repository strains

R-Spondins Are Expressed by the Intestinal Stroma and are Differentially Regulated during Citrobacter rodentium- and DSS-Induced Colitis in Mice.

PubMed

Kang, Eugene; Yousefi, Mitra; Gruenheid, Samantha

2016-01-01

The R-spondin family of proteins has recently been described as secreted enhancers of β-catenin activation through the canonical Wnt signaling pathway. We previously reported that Rspo2 is a major determinant of susceptibility to Citrobacter rodentium-mediated colitis in mice and recent genome-wide association studies have revealed RSPO3 as a candidate Crohn's disease-specific inflammatory bowel disease susceptibility gene in humans. However, there is little information on the endogenous expression and cellular source of R-spondins in the colon at steady state and during intestinal inflammation. RNA sequencing and qRT-PCR were used to assess the expression of R-spondins at steady state and in two mouse models of colonic inflammation. The cellular source of R-spondins was assessed in specific colonic cell populations isolated by cell sorting. Data mining from publicly available datasets was used to assess the expression of R-spondins in the human colon. At steady state, colonic expression of R-spondins was found to be exclusive to non-epithelial CD45- lamina propria cells, and Rspo3/RSPO3 was the most highly expressed R-spondin in both mouse and human colon. R-spondin expression was found to be highly dynamic and differentially regulated during C. rodentium infection and dextran sodium sulfate (DSS) colitis, with notably high levels of Rspo3 expression during DSS colitis, and high levels of Rspo2 expression during C. rodentium infection, specifically in susceptible mice. Our data are consistent with the hypothesis that in the colon, R-spondins are expressed by subepithelial stromal cells, and that Rspo3/RSPO3 is the family member most implicated in colonic homeostasis. The differential regulation of the R-spondins in different models of intestinal inflammation indicate they respond to specific pathogenic and inflammatory signals that differ in the two models and provides further evidence that this family of proteins plays a key role in linking intestinal inflammation and homeostasis.

Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images.

PubMed

Chiang, Michael; Hallman, Sam; Cinquin, Amanda; de Mochel, Nabora Reyes; Paz, Adrian; Kawauchi, Shimako; Calof, Anne L; Cho, Ken W; Fowlkes, Charless C; Cinquin, Olivier

2015-11-25

Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge. Here we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels. High-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights - in particular with respect to the cell cycle - that would be difficult to derive otherwise.

Natural disease history of mouse models for limb girdle muscular dystrophy types 2D and 2F

PubMed Central

Putker, K.; Tanganyika-de Winter, C. L.; Boertje-van der Meulen, J. W.; van Vliet, L.; Overzier, M.; Plomp, J. J.; Aartsma-Rus, A.; van Putten, M.

2017-01-01

Limb-girdle muscular dystrophy types 2D and 2F (LGMD 2D and 2F) are autosomal recessive disorders caused by mutations in the alpha- and delta sarcoglycan genes, respectively, leading to severe muscle weakness and degeneration. The cause of the disease has been well characterized and a number of animal models are available for pre-clinical studies to test potential therapeutic interventions. To facilitate transition from drug discovery to clinical trials, standardized procedures and natural disease history data were collected for these mouse models. Implementing the TREAD-NMD standardized operating procedures, we here subjected LGMD2D (SGCA-null), LGMD2F (SGCD-null) and wild type (C57BL/6J) mice to five functional tests from the age of 4 to 32 weeks. To assess whether the functional test regime interfered with disease pathology, sedentary groups were taken along. Muscle physiology testing of tibialis anterior muscle was performed at the age of 34 weeks. Muscle histopathology and gene expression was analysed in skeletal muscles and heart. Muscle histopathology and gene expression was analysed in skeletal muscles and heart. Mice successfully accomplished the functional tests, which did not interfere with disease pathology. Muscle function of SGCA- and SGCD-null mice was impaired and declined over time. Interestingly, female SGCD-null mice outperformed males in the two and four limb hanging tests, which proved the most suitable non-invasive tests to assess muscle function. Muscle physiology testing of tibialis anterior muscle revealed lower specific force and higher susceptibility to eccentric-induced damage in LGMD mice. Analyzing muscle histopathology and gene expression, we identified the diaphragm as the most affected muscle in LGMD strains. Cardiac fibrosis was found in SGCD-null mice, being more severe in males than in females. Our study offers a comprehensive natural history dataset which will be useful to design standardized tests and future pre-clinical studies in LGMD2D and 2F mice. PMID:28797108

How many sightings to model rare marine species distributions

PubMed Central

Authier, Matthieu; Monestiez, Pascal; Ridoux, Vincent

2018-01-01

Despite large efforts, datasets with few sightings are often available for rare species of marine megafauna that typically live at low densities. This paucity of data makes modelling the habitat of these taxa particularly challenging. We tested the predictive performance of different types of species distribution models fitted to decreasing numbers of sightings. Generalised additive models (GAMs) with three different residual distributions and the presence only model MaxEnt were tested on two megafauna case studies differing in both the number of sightings and ecological niches. From a dolphin (277 sightings) and an auk (1,455 sightings) datasets, we simulated rarity with a sighting thinning protocol by random sampling (without replacement) of a decreasing fraction of sightings. Better prediction of the distribution of a rarely sighted species occupying a narrow habitat (auk dataset) was expected compared to the distribution of a rarely sighted species occupying a broad habitat (dolphin dataset). We used the original datasets to set up a baseline model and fitted additional models on fewer sightings but keeping effort constant. Model predictive performance was assessed with mean squared error and area under the curve. Predictions provided by the models fitted to the thinned-out datasets were better than a homogeneous spatial distribution down to a threshold of approximately 30 sightings for a GAM with a Tweedie distribution and approximately 130 sightings for the other models. Thinning the sighting data for the taxon with narrower habitats seemed to be less detrimental to model predictive performance than for the broader habitat taxon. To generate reliable habitat modelling predictions for rarely sighted marine predators, our results suggest (1) using GAMs with a Tweedie distribution with presence-absence data and (2) implementing, as a conservative empirical measure, at least 50 sightings in the models. PMID:29529097

Therapeutic effects of autologous lymphocytes activated with trastuzumab for xenograft mouse models of human breast cancer.

PubMed

Nakagawa, Shinichiro; Matsuoka, Yusuke; Ichihara, Hideaki; Yoshida, Hitoji; Yoshida, Kenshi; Ueoka, Ryuichi

2013-01-01

Trastuzumab (TTZ) is molecular targeted drug used for metastatic breast cancer patients overexpressing human epidermal growth factor receptor 2 (HER2). Therapeutic effects of lymphocytes activated with TTZ (TTZ-LAK) using xenograft mouse models of human breast cancer (MDA-MB-453) cells were examined in vivo. Remarkable reduction of tumor volume in a xenograft mouse models intravenously treated with TTZ-LAK cells after the subcutaneously inoculated of MDA-MB-453 cells was verified in vivo. The migration of TTZ-LAK cells in tumor of mouse models subcutaneously inoculated MDA-MB-453 cells was observed on the basis of histological analysis using immunostaining with CD-3. Induction of apoptosis in tumor of xenograft mice treated with TTZ-LAK cells was observed in micrographs using terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling (TUNEL) method. It was noteworthy that the therapeutic effects of TTZ-LAK cells along with apoptosis were obtained for xenograft mouse models of human breast tumor in vivo.

Linguistic Extensions of Topic Models

ERIC Educational Resources Information Center

Boyd-Graber, Jordan

2010-01-01

Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable…

NREL - SOWFA - Neutral - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

PNNL - WRF-LES - Convective - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

ANL - WRF-LES - Convective - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

LLNL - WRF-LES - Neutral - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

ANL - WRF-LES - Neutral - TTU

DOE Data Explorer

Kosovic, Branko

2018-06-20

This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

LANL - WRF-LES - Neutral - TTU

DOE Data Explorer

Kosovic, Branko

2018-06-20

This dataset includes large-eddy simulation (LES) output from a neutrally stratified atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on Aug. 17, 2012. The dataset was used to assess LES models for simulation of canonical neutral ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

LANL - WRF-LES - Convective - TTU

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

Kosovic, Branko

This dataset includes large-eddy simulation (LES) output from a convective atmospheric boundary layer (ABL) simulation of observations at the SWIFT tower near Lubbock, Texas on July 4, 2012. The dataset was used to assess the LES models for simulation of canonical convective ABL. The dataset can be used for comparison with other LES and computational fluid dynamics model outputs.

Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases.

PubMed

Gstir, Ronald; Schafferer, Simon; Scheideler, Marcel; Misslinger, Matthias; Griehl, Matthias; Daschil, Nina; Humpel, Christian; Obermair, Gerald J; Schmuckermair, Claudia; Striessnig, Joerg; Flucher, Bernhard E; Hüttenhofer, Alexander

2014-12-01

We have generated a novel, neuro-specific ncRNA microarray, covering 1472 ncRNA species, to investigate their expression in different mouse models for central nervous system diseases. Thereby, we analyzed ncRNA expression in two mouse models with impaired calcium channel activity, implicated in Epilepsy or Parkinson's disease, respectively, as well as in a mouse model mimicking pathophysiological aspects of Alzheimer's disease. We identified well over a hundred differentially expressed ncRNAs, either from known classes of ncRNAs, such as miRNAs or snoRNAs or which represented entirely novel ncRNA species. Several differentially expressed ncRNAs in the calcium channel mouse models were assigned as miRNAs and target genes involved in calcium signaling, thus suggesting feedback regulation of miRNAs by calcium signaling. In the Alzheimer mouse model, we identified two snoRNAs, whose expression was deregulated prior to amyloid plaque formation. Interestingly, the presence of snoRNAs could be detected in cerebral spine fluid samples in humans, thus potentially serving as early diagnostic markers for Alzheimer's disease. In addition to known ncRNAs species, we also identified 63 differentially expressed, entirely novel ncRNA candidates, located in intronic or intergenic regions of the mouse genome, genomic locations, which previously have been shown to harbor the majority of functional ncRNAs. © 2014 Gstir et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

PDX-1 Is a Therapeutic Target for Pancreatic Cancer, Insulinoma and Islet Neoplasia Using a Novel RNA Interference Platform

PubMed Central

Liu, Shi-He; Rao, Donald D.; Nemunaitis, John; Senzer, Neil; Zhou, Guisheng; Dawson, David; Gingras, Marie-Claude; Wang, Zhaohui; Gibbs, Richard; Norman, Michael; Templeton, Nancy S.; DeMayo, Francesco J.; O'Malley, Bert; Sanchez, Robbi; Fisher, William E.; Brunicardi, F. Charles

2012-01-01

Pancreatic and duodenal homeobox-1 (PDX-1) is a transcription factor that regulates insulin expression and islet maintenance in the adult pancreas. Our recent studies demonstrate that PDX-1 is an oncogene for pancreatic cancer and is overexpressed in pancreatic cancer. The purpose of this study was to demonstrate that PDX-1 is a therapeutic target for both hormonal symptoms and tumor volume in mouse models of pancreatic cancer, insulinoma and islet neoplasia. Immunohistochemistry of human pancreatic and islet neoplasia specimens revealed marked PDX-1 overexpression, suggesting PDX-1 as a “drugable” target within these diseases. To do so, a novel RNA interference effector platform, bifunctional shRNAPDX-1, was developed and studied in mouse and human cell lines as well as in mouse models of pancreatic cancer, insulinoma and islet neoplasia. Systemic delivery of bi-shRNAhumanPDX-1 lipoplexes resulted in marked reduction of tumor volume and improved survival in a human pancreatic cancer xenograft mouse model. bi-shRNAmousePDX-1 lipoplexes prevented death from hyperinsulinemia and hypoglycemia in an insulinoma mouse model. shRNAmousePDX-1 lipoplexes reversed hyperinsulinemia and hypoglycemia in an immune-competent mouse model of islet neoplasia. PDX-1 was overexpressed in pancreatic neuroendocrine tumors and nesidioblastosis. These data demonstrate that PDX-1 RNAi therapy controls hormonal symptoms and tumor volume in mouse models of pancreatic cancer, insulinoma and islet neoplasia, therefore, PDX-1 is a potential therapeutic target for these pancreatic diseases. PMID:22905092

Subsampling for dataset optimisation

NASA Astrophysics Data System (ADS)

Ließ, Mareike

2017-04-01

Soil-landscapes have formed by the interaction of soil-forming factors and pedogenic processes. In modelling these landscapes in their pedodiversity and the underlying processes, a representative unbiased dataset is required. This concerns model input as well as output data. However, very often big datasets are available which are highly heterogeneous and were gathered for various purposes, but not to model a particular process or data space. As a first step, the overall data space and/or landscape section to be modelled needs to be identified including considerations regarding scale and resolution. Then the available dataset needs to be optimised via subsampling to well represent this n-dimensional data space. A couple of well-known sampling designs may be adapted to suit this purpose. The overall approach follows three main strategies: (1) the data space may be condensed and de-correlated by a factor analysis to facilitate the subsampling process. (2) Different methods of pattern recognition serve to structure the n-dimensional data space to be modelled into units which then form the basis for the optimisation of an existing dataset through a sensible selection of samples. Along the way, data units for which there is currently insufficient soil data available may be identified. And (3) random samples from the n-dimensional data space may be replaced by similar samples from the available dataset. While being a presupposition to develop data-driven statistical models, this approach may also help to develop universal process models and identify limitations in existing models.

Obs4MIPS: Satellite Observations for Model Evaluation

NASA Astrophysics Data System (ADS)

Ferraro, R.; Waliser, D. E.; Gleckler, P. J.

2017-12-01

This poster will review the current status of the obs4MIPs project, whose purpose is to provide a limited collection of well-established and documented datasets for comparison with Earth system models (https://www.earthsystemcog.org/projects/obs4mips/). These datasets have been reformatted to correspond with the CMIP5 model output requirements, and include technical documentation specifically targeted for their use in model output evaluation. The project holdings now exceed 120 datasets with observations that directly correspond to CMIP5 model output variables, with new additions in response to the CMIP6 experiments. With the growth in climate model output data volume, it is increasing more difficult to bring the model output and the observations together to do evaluations. The positioning of the obs4MIPs datasets within the Earth System Grid Federation (ESGF) allows for the use of currently available and planned online tools within the ESGF to perform analysis using model output and observational datasets without necessarily downloading everything to a local workstation. This past year, obs4MIPs has updated its submission guidelines to closely align with changes in the CMIP6 experiments, and is implementing additional indicators and ancillary data to allow users to more easily determine the efficacy of an obs4MIPs dataset for specific evaluation purposes. This poster will present the new guidelines and indicators, and update the list of current obs4MIPs holdings and their connection to the ESGF evaluation and analysis tools currently available, and being developed for the CMIP6 experiments.

Development and Characterization of a Mouse Model for Marburg Hemorrhagic Fever

DTIC Science & Technology

2009-07-01

Microbiology. All Rights Reserved. Development and Characterization of a Mouse Model for Marburg Hemorrhagic Fever Kelly L. Warfield,* Steven B...mouse model has hampered an understanding of the pathogenesis and immunity of Marburg hemorrhagic fever (MHF), the disease caused by marburgvirus (MARV...cause severe hemorrhagic fevers in humans and non- human primates (27). The incubation time is estimated to be 3 to 21 days, with human case fatality

Producing a Mouse Model to Explore the Linkages Between Tocopherol Biology and Prostate Cancer

DTIC Science & Technology

2005-07-01

Edwards, Prostate cancer and supplementation with alpha-tocopherol and beta -carotene: incidence and mortality in a controlled trial. J Natl Cancer ...1-0153 TITLE: Producing a Mouse Model to Explore the Linkages Between Tocopherol Biology and Prostate Cancer ...TITLE AND SUBTITLE Producing a Mouse Model to Explore the Linkages Between Tocopherol 5a. CONTRACT NUMBER Biology and Prostate Cancer 5b. GRANT

Synergistic Action of FOXP3 and TSC1 Pathways During Tumor Progression

DTIC Science & Technology

2015-10-01

invasive carcinoma and, ultimately, metastatic disease [1-3]. Mouse models of PIN (mPIN) generated by a single- mutant gene in prostate do not progress...downstream target) is sufficient to significantly reduce the initiation of prostate cancer in the Pten conditional knockout mouse model [19-21...the possibility that these two genetic hits cooperate to promote tumor progression, and mouse models show that this cooperation accelerates

Designing Mouse Behavioral Tasks Relevant to Autistic-Like Behaviors

ERIC Educational Resources Information Center

Crawley, Jacqueline N.

2004-01-01

The importance of genetic factors in autism has prompted the development of mutant mouse models to advance our understanding of biological mechanisms underlying autistic behaviors. Mouse models of human neuropsychiatric diseases are designed to optimize (1) face validity, i.e., resemblance to the human symptoms; (2) construct validity, i.e.,…

Behavioral phenotypes of genetic mouse models of autism

PubMed Central

Kazdoba, T. M.; Leach, P. T.; Crawley, J. N.

2016-01-01

More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. PMID:26403076

Defining the role of polyamines in colon carcinogenesis using mouse models

PubMed Central

Ignatenko, Natalia A.; Gerner, Eugene W.; Besselsen, David G.

2011-01-01

Genetics and diet are both considered important risk determinants for colorectal cancer, a leading cause of death in the US and worldwide. Genetically engineered mouse (GEM) models have made a significant contribution to the characterization of colorectal cancer risk factors. Reliable, reproducible, and clinically relevant animal models help in the identification of the molecular events associated with disease progression and in the development of effictive treatment strategies. This review is focused on the use of mouse models for studying the role of polyamines in colon carcinogenesis. We describe how the available mouse models of colon cancer such as the multiple intestinal neoplasia (Min) mice and knockout genetic models facilitate understanding of the role of polyamines in colon carcinogenesis and help in the development of a rational strategy for colon cancer chemoprevention. PMID:21712957

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

PubMed Central

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-01-01

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets. PMID:26147731

Targeting Cancer Protein Profiles with Split-Enzyme Reporter Fragments to Achieve Chemical Resolution for Molecular Imaging

DTIC Science & Technology

2013-08-01

We next tested the utility of the construct to accumulate in tumors expressing EGFR using an orthotopic mouse model for brain tumors. Glioma cells...filament tumor marker, identified implanted cells within the orthotopic mouse model which were of human origin, i.e. Gli36Δ5 cells, and demonstrated that...forward into in vivo animal tumor model studies. • In vivo imaging of EGFR targeted-complex in orthotopic mouse model of brain tumor. • Ex vivo validation

Genetically engineered mouse models of melanoma.

PubMed

Pérez-Guijarro, Eva; Day, Chi-Ping; Merlino, Glenn; Zaidi, M Raza

2017-06-01

Melanoma is a complex disease that exhibits highly heterogeneous etiological, histopathological, and genetic features, as well as therapeutic responses. Genetically engineered mouse (GEM) models provide powerful tools to unravel the molecular mechanisms critical for melanoma development and drug resistance. Here, we expound briefly the basis of the mouse modeling design, the available technology for genetic engineering, and the aspects influencing the use of GEMs to model melanoma. Furthermore, we describe in detail the currently available GEM models of melanoma. Cancer 2017;123:2089-103. © 2017 American Cancer Society. © 2017 American Cancer Society.

Hepatic SILAC proteomic data from PANDER transgenic model.

PubMed

Athanason, Mark G; Stevens, Stanley M; Burkhardt, Brant R

2016-12-01

This article contains raw and processed data related to research published in "Quantitative Proteomic Profiling Reveals Hepatic Lipogenesis and Liver X Receptor Activation in the PANDER Transgenic Model" (M.G. Athanason, W.A. Ratliff, D. Chaput, C.B. MarElia, M.N. Kuehl, S.M., Jr. Stevens, B.R. Burkhardt (2016)) [1], and was generated by "spike-in" SILAC-based proteomic analysis of livers obtained from the PANcreatic-Derived factor (PANDER) transgenic mouse (PANTG) under various metabolic conditions [1]. The mass spectrometry output of the PANTG and wild-type B6SJLF mice liver tissue and resulting proteome search from MaxQuant 1.2.2.5 employing the Andromeda search algorithm against the UniprotKB reference database for Mus musculus has been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE partner repository with dataset identifiers PRIDE: PXD004171 and doi:10.6019/PXD004171. Protein ratio values representing PANTG/wild-type obtained by MaxQuant analysis were input into the Perseus processing suite to determine statistical significance using the Significance A outlier test (p<0.05). Differentially expressed proteins using this approach were input into Ingenuity Pathway Analysis to determined altered pathways and upstream regulators that were altered in PANTG mice.

CRISPR-Mediated Knockout of Cybb in NSG Mice Establishes a Model of Chronic Granulomatous Disease for Human Stem-Cell Gene Therapy Transplants.

PubMed

Sweeney, Colin L; Choi, Uimook; Liu, Chengyu; Koontz, Sherry; Ha, Seung-Kwon; Malech, Harry L

2017-07-01

Chronic granulomatous disease (CGD) is characterized by defects in the production of microbicidal reactive oxygen species (ROS) by phagocytes. Testing of gene and cell therapies for the treatment of CGD in human hematopoietic cells requires preclinical transplant models. The use of the lymphocyte-deficient NOD.Cg-Prkdc scid Il2rg tm1Wjl/ SzJ (NSG) mouse strain for human hematopoietic cell xenografts to test CGD therapies is complicated by the presence of functional mouse granulocytes capable of producing ROS for subsequent bacterial and fungal killing. To establish a phagocyte-defective mouse model of X-linked CGD (X-CGD) in NSG mice, clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 was utilized for targeted knockout of mouse Cybb on the X-chromosome by microinjection of NSG mouse zygotes with Cas9 mRNA and CRISPR single-guide RNA targeting Cybb exon 1 or exon 3. This resulted in a high incidence of indel formation at the CRISPR target site, with all mice exhibiting deletions in at least one Cybb allele based on sequence analysis of tail snip DNA. A female mouse heterozygous for a 235-bp deletion in Cybb exon 1 was bred to an NSG male to establish the X-CGD NSG mouse strain, NSG.Cybb[KO]. Resulting male offspring with the 235 bp deletion were found to be defective for production of ROS by neutrophils and other phagocytes, and demonstrated increased susceptibility to spontaneous bacterial and fungal infections with granulomatous inflammation. The establishment of the phagocyte-defective NSG.Cybb[KO] mouse model enables the in vivo assessment of gene and cell therapy strategies for treating CGD in human hematopoietic cell transplants without obfuscation by functional mouse phagocytes, and may also be useful for modeling other phagocyte disorders in humanized NSG mouse xenografts.

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Genetically engineered mouse models of craniopharyngioma: an opportunity for therapy development and understanding of tumor biology.

PubMed

Apps, John Richard; Martinez-Barbera, Juan Pedro

2017-05-01

Adamantinomatous craniopharyngioma (ACP) is the commonest tumor of the sellar region in childhood. Two genetically engineered mouse models have been developed and are giving valuable insights into ACP biology. These models have identified novel pathways activated in tumors, revealed an important function of paracrine signalling and extended conventional theories about the role of organ-specific stem cells in tumorigenesis. In this review, we summarize these mouse models, what has been learnt, their limitations and open questions for future research. We then discussed how these mouse models may be used to test novel therapeutics against potentially targetable pathways recently identified in human ACP. © 2017 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

High-Frequency Chirp Ultrasound Imaging with an Annular-array for Ophthalmologic and Small-Animal Imaging

PubMed Central

Mamou, Jonathan; Aristizábal, Orlando; Silverman, Ronald H.; Ketterling, Jeffrey A.; Turnbull, Daniel H.

2009-01-01

High-frequency ultrasound (HFU, > 20 MHz) is an attractive means of obtaining fine-resolution images of biological tissues for ophthalmologic, dermatological, and small-animal imaging applications. Even with current improvements in circuit designs and high-frequency equipment, HFU suffers from two inherent limitations. First, HFU images have a limited depth of field (DOF) because of the short wavelength and the low fixed F-number of conventional HFU transducers. Second, HFU is usually limited to shallow imaging because of the significant attenuation in most tissues. In a previous study, a five-element annular array with a 17-MHz center frequency was excited using chirp-coded signals, and a synthetic-focusing algorithm was used to extend the DOF and increase penetration depth. In the present study, a similar approach with two different five-element annular arrays operating near a center frequency of 35-MHz is implemented and validated. Following validation studies, the chirp-imaging methods were applied to imaging vitreous-hemorrhage mimicking phantoms and mouse embryos. Images of the vitreous phantom showed increased sensitivity using the chirp method compared to a standard monocycle imaging method, and blood droplets could be visualized 4 mm deeper into the phantom. Three-dimensional datasets of 12.5-day-old, mouse-embryo heads were acquired in utero using chirp and conventional excitations. Images were formed and brains ventricles were segmented and reconstructed in three dimensions. The brain-ventricle volumes for the monocycle excitation exhibited artifacts that were not apparent on the chirp-based dataset reconstruction. PMID:19394754

Compensatory Limb Use and Behavioral Assessment of Motor Skill Learning Following Sensorimotor Cortex Injury in a Mouse Model of Ischemic Stroke

PubMed Central

Kerr, Abigail L.; Tennant, Kelly A.

2014-01-01

Mouse models have become increasingly popular in the field of behavioral neuroscience, and specifically in studies of experimental stroke. As models advance, it is important to develop sensitive behavioral measures specific to the mouse. The present protocol describes a skilled motor task for use in mouse models of stroke. The Pasta Matrix Reaching Task functions as a versatile and sensitive behavioral assay that permits experimenters to collect accurate outcome data and manipulate limb use to mimic human clinical phenomena including compensatory strategies (i.e., learned non-use) and focused rehabilitative training. When combined with neuroanatomical tools, this task also permits researchers to explore the mechanisms that support behavioral recovery of function (or lack thereof) following stroke. The task is both simple and affordable to set up and conduct, offering a variety of training and testing options for numerous research questions concerning functional outcome following injury. Though the task has been applied to mouse models of stroke, it may also be beneficial in studies of functional outcome in other upper extremity injury models. PMID:25045916

The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database.

PubMed

Hayman, G Thomas; Laulederkind, Stanley J F; Smith, Jennifer R; Wang, Shur-Jen; Petri, Victoria; Nigam, Rajni; Tutaj, Marek; De Pons, Jeff; Dwinell, Melinda R; Shimoyama, Mary

2016-01-01

The Rat Genome Database (RGD;http://rgd.mcw.edu/) provides critical datasets and software tools to a diverse community of rat and non-rat researchers worldwide. To meet the needs of the many users whose research is disease oriented, RGD has created a series of Disease Portals and has prioritized its curation efforts on the datasets important to understanding the mechanisms of various diseases. Gene-disease relationships for three species, rat, human and mouse, are annotated to capture biomarkers, genetic associations, molecular mechanisms and therapeutic targets. To generate gene-disease annotations more effectively and in greater detail, RGD initially adopted the MEDIC disease vocabulary from the Comparative Toxicogenomics Database and adapted it for use by expanding this framework with the addition of over 1000 terms to create the RGD Disease Ontology (RDO). The RDO provides the foundation for, at present, 10 comprehensive disease area-related dataset and analysis platforms at RGD, the Disease Portals. Two major disease areas are the focus of data acquisition and curation efforts each year, leading to the release of the related Disease Portals. Collaborative efforts to realize a more robust disease ontology are underway. Database URL:http://rgd.mcw.edu. © The Author(s) 2016. Published by Oxford University Press.

Twente spine model: A complete and coherent dataset for musculo-skeletal modeling of the thoracic and cervical regions of the human spine.

PubMed

Bayoglu, Riza; Geeraedts, Leo; Groenen, Karlijn H J; Verdonschot, Nico; Koopman, Bart; Homminga, Jasper

2017-06-14

Musculo-skeletal modeling could play a key role in advancing our understanding of the healthy and pathological spine, but the credibility of such models are strictly dependent on the accuracy of the anatomical data incorporated. In this study, we present a complete and coherent musculo-skeletal dataset for the thoracic and cervical regions of the human spine, obtained through detailed dissection of an embalmed male cadaver. We divided the muscles into a number of muscle-tendon elements, digitized their attachments at the bones, and measured morphological muscle parameters. In total, 225 muscle elements were measured over 39 muscles. For every muscle element, we provide the coordinates of its attachments, fiber length, tendon length, sarcomere length, optimal fiber length, pennation angle, mass, and physiological cross-sectional area together with the skeletal geometry of the cadaver. Results were consistent with similar anatomical studies. Furthermore, we report new data for several muscles such as rotatores, multifidus, levatores costarum, spinalis, semispinalis, subcostales, transversus thoracis, and intercostales muscles. This dataset complements our previous study where we presented a consistent dataset for the lumbar region of the spine (Bayoglu et al., 2017). Therefore, when used together, these datasets enable a complete and coherent dataset for the entire spine. The complete dataset will be used to develop a musculo-skeletal model for the entire human spine to study clinical and ergonomic applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

PubMed

Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

2014-03-01

We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset. The collection of frequent subgraphs (FSG) forms a dataset-specific descriptors whose values for each molecule are defined by the number of times each frequent fragment occurs in this molecule. We have employed the FSG descriptors to develop variable selection k Nearest Neighbor (kNN) QSAR models of several datasets with binary target property including Maximum Recommended Therapeutic Dose (MRTD), Salmonella Mutagenicity (Ames Genotoxicity), and P-Glycoprotein (PGP) data. Each dataset was divided into training, test, and validation sets to establish the statistical figures of merit reflecting the model validated predictive power. The classification accuracies of models for both training and test sets for all datasets exceeded 75 %, and the accuracy for the external validation sets exceeded 72 %. The model accuracies were comparable or better than those reported earlier in the literature for the same datasets. Furthermore, the use of fragment-based descriptors affords mechanistic interpretation of validated QSAR models in terms of essential chemical fragments responsible for the compounds' target property. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In-depth characterization of the secretome of mouse CNS cell lines by LC-MS/MS without prefractionation.

PubMed

Woo, Jongmin; Han, Dohyun; Park, Joonho; Kim, Sang Jeong; Kim, Youngsoo

2015-11-01

Microglia, astrocytes, and neurons, which have important functions in the central nervous system (CNS), communicate mutually to generate a signal through secreted proteins or small molecules, but many of which have not been identified. Because establishing a reference for the secreted proteins from CNS cells could be invaluable in examining cell-to-cell communication in the brain, we analyzed the secretome of three murine CNS cell lines without prefractionation by high-resolution mass spectrometry. In this study, 2795 proteins were identified from conditioned media of the three cell lines, and 2125 proteins were annotated as secreted proteins by bioinformatics analysis. Further, approximately 500 secreted proteins were quantifiable as differentially expressed proteins by label-free quantitation. As a result, our secretome references are useful datasets for the future study of neuronal diseases. All MS data have been deposited in the ProteomeXchange with identifier PXD001597 (http://proteomecentral.proteomexchange.org/dataset/PXD001597). © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An interactive environment for agile analysis and visualization of ChIP-sequencing data.

PubMed

Lerdrup, Mads; Johansen, Jens Vilstrup; Agrawal-Singh, Shuchi; Hansen, Klaus

2016-04-01

To empower experimentalists with a means for fast and comprehensive chromatin immunoprecipitation sequencing (ChIP-seq) data analyses, we introduce an integrated computational environment, EaSeq. The software combines the exploratory power of genome browsers with an extensive set of interactive and user-friendly tools for genome-wide abstraction and visualization. It enables experimentalists to easily extract information and generate hypotheses from their own data and public genome-wide datasets. For demonstration purposes, we performed meta-analyses of public Polycomb ChIP-seq data and established a new screening approach to analyze more than 900 datasets from mouse embryonic stem cells for factors potentially associated with Polycomb recruitment. EaSeq, which is freely available and works on a standard personal computer, can substantially increase the throughput of many analysis workflows, facilitate transparency and reproducibility by automatically documenting and organizing analyses, and enable a broader group of scientists to gain insights from ChIP-seq data.

A comparison of some organizational characteristics of the mouse central retina and the human macula.

PubMed

Volland, Stefanie; Esteve-Rudd, Julian; Hoo, Juyea; Yee, Claudine; Williams, David S

2015-01-01

Mouse models have greatly assisted our understanding of retinal degenerations. However, the mouse retina does not have a macula, leading to the question of whether the mouse is a relevant model for macular degeneration. In the present study, a quantitative comparison between the organization of the central mouse retina and the human macula was made, focusing on some structural characteristics that have been suggested to be important in predisposing the macula to stresses leading to degeneration: photoreceptor density, phagocytic load on the RPE, and the relative thinness of Bruch's membrane. Light and electron microscopy measurements from retinas of two strains of mice, together with published data on human retinas, were used for calculations and subsequent comparisons. As in the human retina, the central region of the mouse retina possesses a higher photoreceptor cell density and a thinner Bruch's membrane than in the periphery; however, the magnitudes of these periphery to center gradients are larger in the human. Of potentially greater relevance is the actual photoreceptor cell density, which is much greater in the mouse central retina than in the human macula, underlying a higher phagocytic load for the mouse RPE. Moreover, at eccentricities that correspond to the peripheral half of the human macula, the rod to cone ratio is similar between mouse and human. Hence, with respect to photoreceptor density and phagocytic load of the RPE, the central mouse retina models at least the more peripheral part of the macula, where macular degeneration is often first evident.

A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN

DTIC Science & Technology

2014-09-01

AWARD NUMBER: W81XWH-13-1-0220 TITLE: A Genetically Engineered Mouse Model of Neuroblastoma ...CONTRACT NUMBER A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN 5b. GRANT NUMBER W81XWH-13-1-0220 5c...common ALK mutations in neuroblastoma , F1174L and R1275Q. We have determined that in tumors cells expressing mutated ALK, different downstream

Developing Novel Therapeutic Approaches in Small Cell Lung Carcinoma Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells

DTIC Science & Technology

2014-10-01

AD_________________ Award Number: W81XWH-13-1-0325 TITLE: Developing Novel Therapeutic Approaches in Small Cell Lung Carcinoma Using ...Genetically Engineered Mouse Models and Human Circulating Tumor Cells PRINCIPAL INVESTIGATOR: Jeffrey Engelman MD PhD CONTRACTING ORGANIZATION ...Novel Therapeutic Approaches in Small Cell Lung 5a. CONTRACT NUMBER W81XWH-13-1-0325 Carcinoma Using Genetically Engineered Mouse Models and 5b

Behavioural phenotyping assays for mouse models of autism

PubMed Central

Silverman, Jill L.; Yang, Mu; Lord, Catherine; Crawley, Jacqueline N.

2011-01-01

Autism is a heterogeneous neurodevelopmental disorder of unknown aetiology that affects 1 in 100–150 individuals. Diagnosis is based on three categories of behavioural criteria: abnormal social interactions, communication deficits and repetitive behaviours. Strong evidence for a genetic basis has prompted the development of mouse models with targeted mutations in candidate genes for autism. As the diagnostic criteria for autism are behavioural, phenotyping these mouse models requires behavioural assays with high relevance to each category of the diagnostic symptoms. Behavioural neuroscientists are generating a comprehensive set of assays for social interaction, communication and repetitive behaviours to test hypotheses about the causes of austism. Robust phenotypes in mouse models hold great promise as translational tools for discovering effective treatments for components of autism spectrum disorders. PMID:20559336

Twin Data That Made a Big Difference, and That Deserve to Be Better-Known and Used in Teaching

ERIC Educational Resources Information Center

Campbell, Harlan; Hanley, James A.

2017-01-01

Because of their efficiency and ability to keep many other factors constant, twin studies have a special appeal for investigators. Just as with any teaching dataset, a "matched-sets" dataset used to illustrate a statistical model should be compelling, still relevant, and valid. Indeed, such a "model dataset" should meet the…

Layer-specific chromatin accessibility landscapes reveal regulatory networks in adult mouse visual cortex

PubMed Central

Gray, Lucas T; Yao, Zizhen; Nguyen, Thuc Nghi; Kim, Tae Kyung; Zeng, Hongkui; Tasic, Bosiljka

2017-01-01

Mammalian cortex is a laminar structure, with each layer composed of a characteristic set of cell types with different morphological, electrophysiological, and connectional properties. Here, we define chromatin accessibility landscapes of major, layer-specific excitatory classes of neurons, and compare them to each other and to inhibitory cortical neurons using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq). We identify a large number of layer-specific accessible sites, and significant association with genes that are expressed in specific cortical layers. Integration of these data with layer-specific transcriptomic profiles and transcription factor binding motifs enabled us to construct a regulatory network revealing potential key layer-specific regulators, including Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb. This dataset is a valuable resource for identifying candidate layer-specific cis-regulatory elements in adult mouse cortex. DOI: http://dx.doi.org/10.7554/eLife.21883.001 PMID:28112643

SBR-Blood: systems biology repository for hematopoietic cells.

PubMed

Lichtenberg, Jens; Heuston, Elisabeth F; Mishra, Tejaswini; Keller, Cheryl A; Hardison, Ross C; Bodine, David M

2016-01-04

Extensive research into hematopoiesis (the development of blood cells) over several decades has generated large sets of expression and epigenetic profiles in multiple human and mouse blood cell types. However, there is no single location to analyze how gene regulatory processes lead to different mature blood cells. We have developed a new database framework called hematopoietic Systems Biology Repository (SBR-Blood), available online at http://sbrblood.nhgri.nih.gov, which allows user-initiated analyses for cell type correlations or gene-specific behavior during differentiation using publicly available datasets for array- and sequencing-based platforms from mouse hematopoietic cells. SBR-Blood organizes information by both cell identity and by hematopoietic lineage. The validity and usability of SBR-Blood has been established through the reproduction of workflows relevant to expression data, DNA methylation, histone modifications and transcription factor occupancy profiles. Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

PubMed

Qin, Zijian; Wang, Maolin; Yan, Aixia

2017-07-01

In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

A developmentally plastic adult mouse kidney cell line spontaneously generates multiple adult kidney structures

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

Webb, Carol F., E-mail: carol-webb@omrf.org; Immunobiology and Cancer Research, Oklahoma Medical Research Foundation, Oklahoma City, OK; Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK

Despite exciting new possibilities for regenerative therapy posed by the ability to induce pluripotent stem cells, recapitulation of three-dimensional kidneys for repair or replacement has not been possible. ARID3a-deficient mouse tissues generated multipotent, developmentally plastic cells. Therefore, we assessed the adult mouse ARID3a−/− kidney cell line, KKPS5, which expresses renal progenitor surface markers as an alternative cell source for modeling kidney development. Remarkably, these cells spontaneously developed into multicellular nephron-like structures in vitro, and engrafted into immunocompromised medaka mesonephros, where they formed mouse nephron structures. These data implicate KKPS5 cells as a new model system for studying kidney development. - Highlights:more » • An ARID3a-deficient mouse kidney cell line expresses multiple progenitor markers. • This cell line spontaneously forms multiple nephron-like structures in vitro. • This cell line formed mouse kidney structures in immunocompromised medaka fish kidneys. • Our data identify a novel model system for studying kidney development.« less

Mouse phenotyping.

PubMed

Fuchs, Helmut; Gailus-Durner, Valérie; Adler, Thure; Aguilar-Pimentel, Juan Antonio; Becker, Lore; Calzada-Wack, Julia; Da Silva-Buttkus, Patricia; Neff, Frauke; Götz, Alexander; Hans, Wolfgang; Hölter, Sabine M; Horsch, Marion; Kastenmüller, Gabi; Kemter, Elisabeth; Lengger, Christoph; Maier, Holger; Matloka, Mikolaj; Möller, Gabriele; Naton, Beatrix; Prehn, Cornelia; Puk, Oliver; Rácz, Ildikó; Rathkolb, Birgit; Römisch-Margl, Werner; Rozman, Jan; Wang-Sattler, Rui; Schrewe, Anja; Stöger, Claudia; Tost, Monica; Adamski, Jerzy; Aigner, Bernhard; Beckers, Johannes; Behrendt, Heidrun; Busch, Dirk H; Esposito, Irene; Graw, Jochen; Illig, Thomas; Ivandic, Boris; Klingenspor, Martin; Klopstock, Thomas; Kremmer, Elisabeth; Mempel, Martin; Neschen, Susanne; Ollert, Markus; Schulz, Holger; Suhre, Karsten; Wolf, Eckhard; Wurst, Wolfgang; Zimmer, Andreas; Hrabě de Angelis, Martin

2011-02-01

Model organisms like the mouse are important tools to learn more about gene function in man. Within the last 20 years many mutant mouse lines have been generated by different methods such as ENU mutagenesis, constitutive and conditional knock-out approaches, knock-down, introduction of human genes, and knock-in techniques, thus creating models which mimic human conditions. Due to pleiotropic effects, one gene may have different functions in different organ systems or time points during development. Therefore mutant mouse lines have to be phenotyped comprehensively in a highly standardized manner to enable the detection of phenotypes which might otherwise remain hidden. The German Mouse Clinic (GMC) has been established at the Helmholtz Zentrum München as a phenotyping platform with open access to the scientific community (www.mousclinic.de; [1]). The GMC is a member of the EUMODIC consortium which created the European standard workflow EMPReSSslim for the systemic phenotyping of mouse models (http://www.eumodic.org/[2]). Copyright © 2010 Elsevier Inc. All rights reserved.

Downscaling global precipitation for local applications - a case for the Rhine basin

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-236, accepted for final publication. - Hijmans, R.J. et al., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. - Schellekens, J. et al., 2016. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, under review. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Weedon, G.P. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, doi:10.1002/2014WR015638.

A high-resolution European dataset for hydrologic modeling

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as inputs to the hydrological calibration and validation of EFAS as well as for establishing long-term discharge "proxy" climatologies which can then in turn be used for statistical analysis to derive return periods or other time series derivatives. In addition, this dataset will be used to assess climatological trends in Europe. Unfortunately, to date no baseline dataset at the European scale exists to test the quality of the herein presented data. Hence, a comparison against other existing datasets can therefore only be an indication of data quality. Due to availability, a comparison was made for precipitation and temperature only, arguably the most important meteorological drivers for hydrologic models. A variety of analyses was undertaken at country scale against data reported to EUROSTAT and E-OBS datasets. The comparison revealed that while the datasets showed overall similar temporal and spatial patterns, there were some differences in magnitudes especially for precipitation. It is not straightforward to define the specific cause for these differences. However, in most cases the comparatively low observation station density appears to be the principal reason for the differences in magnitude.

The 3D Reference Earth Model: Status and Preliminary Results

NASA Astrophysics Data System (ADS)

Moulik, P.; Lekic, V.; Romanowicz, B. A.

2017-12-01

In the 20th century, seismologists constructed models of how average physical properties (e.g. density, rigidity, compressibility, anisotropy) vary with depth in the Earth's interior. These one-dimensional (1D) reference Earth models (e.g. PREM) have proven indispensable in earthquake location, imaging of interior structure, understanding material properties under extreme conditions, and as a reference in other fields, such as particle physics and astronomy. Over the past three decades, new datasets motivated more sophisticated efforts that yielded models of how properties vary both laterally and with depth in the Earth's interior. Though these three-dimensional (3D) models exhibit compelling similarities at large scales, differences in the methodology, representation of structure, and dataset upon which they are based, have prevented the creation of 3D community reference models. As part of the REM-3D project, we are compiling and reconciling reference seismic datasets of body wave travel-time measurements, fundamental mode and overtone surface wave dispersion measurements, and normal mode frequencies and splitting functions. These reference datasets are being inverted for a long-wavelength, 3D reference Earth model that describes the robust long-wavelength features of mantle heterogeneity. As a community reference model with fully quantified uncertainties and tradeoffs and an associated publically available dataset, REM-3D will facilitate Earth imaging studies, earthquake characterization, inferences on temperature and composition in the deep interior, and be of improved utility to emerging scientific endeavors, such as neutrino geoscience. Here, we summarize progress made in the construction of the reference long period dataset and present a preliminary version of REM-3D in the upper-mantle. In order to determine the level of detail warranted for inclusion in REM-3D, we analyze the spectrum of discrepancies between models inverted with different subsets of the reference dataset. This procedure allows us to evaluate the extent of consistency in imaging heterogeneity at various depths and between spatial scales.

Deep learning-based fine-grained car make/model classification for visual surveillance

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Parıldı, Enes Sinan; Solmaz, Berkan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

Fine-grained object recognition is a potential computer vision problem that has been recently addressed by utilizing deep Convolutional Neural Networks (CNNs). Nevertheless, the main disadvantage of classification methods relying on deep CNN models is the need for considerably large amount of data. In addition, there exists relatively less amount of annotated data for a real world application, such as the recognition of car models in a traffic surveillance system. To this end, we mainly concentrate on the classification of fine-grained car make and/or models for visual scenarios by the help of two different domains. First, a large-scale dataset including approximately 900K images is constructed from a website which includes fine-grained car models. According to their labels, a state-of-the-art CNN model is trained on the constructed dataset. The second domain that is dealt with is the set of images collected from a camera integrated to a traffic surveillance system. These images, which are over 260K, are gathered by a special license plate detection method on top of a motion detection algorithm. An appropriately selected size of the image is cropped from the region of interest provided by the detected license plate location. These sets of images and their provided labels for more than 30 classes are employed to fine-tune the CNN model which is already trained on the large scale dataset described above. To fine-tune the network, the last two fully-connected layers are randomly initialized and the remaining layers are fine-tuned in the second dataset. In this work, the transfer of a learned model on a large dataset to a smaller one has been successfully performed by utilizing both the limited annotated data of the traffic field and a large scale dataset with available annotations. Our experimental results both in the validation dataset and the real field show that the proposed methodology performs favorably against the training of the CNN model from scratch.

Context-Aware Generative Adversarial Privacy

NASA Astrophysics Data System (ADS)

Huang, Chong; Kairouz, Peter; Chen, Xiao; Sankar, Lalitha; Rajagopal, Ram

2017-12-01

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

Human androgen deficiency: insights gained from androgen receptor knockout mouse models

PubMed Central

Rana, Kesha; Davey, Rachel A; Zajac, Jeffrey D

2014-01-01

The mechanism of androgen action is complex. Recently, significant advances have been made into our understanding of how androgens act via the androgen receptor (AR) through the use of genetically modified mouse models. A number of global and tissue-specific AR knockout (ARKO) models have been generated using the Cre-loxP system which allows tissue- and/or cell-specific deletion. These ARKO models have examined a number of sites of androgen action including the cardiovascular system, the immune and hemopoetic system, bone, muscle, adipose tissue, the prostate and the brain. This review focuses on the insights that have been gained into human androgen deficiency through the use of ARKO mouse models at each of these sites of action, and highlights the strengths and limitations of these Cre-loxP mouse models that should be considered to ensure accurate interpretation of the phenotype. PMID:24480924

Modelling clinical systemic lupus erythematosus: similarities, differences and success stories

PubMed Central

Celhar, Teja

2017-01-01

Abstract Mouse models of SLE have been indispensable tools to study disease pathogenesis, to identify genetic susceptibility loci and targets for drug development, and for preclinical testing of novel therapeutics. Recent insights into immunological mechanisms of disease progression have boosted a revival in SLE drug development. Despite promising results in mouse studies, many novel drugs have failed to meet clinical end points. This is probably because of the complexity of the disease, which is driven by polygenic predisposition and diverse environmental factors, resulting in a heterogeneous clinical presentation. Each mouse model recapitulates limited aspects of lupus, especially in terms of the mechanism underlying disease progression. The main mouse models have been fairly successful for the evaluation of broad-acting immunosuppressants. However, the advent of targeted therapeutics calls for a selection of the most appropriate model(s) for testing and, ultimately, identification of patients who will be most likely to respond. PMID:28013204

Mouse Models of Gastric Cancer

PubMed Central

Hayakawa, Yoku; Fox, James G.; Gonda, Tamas; Worthley, Daniel L.; Muthupalani, Sureshkumar; Wang, Timothy C.

2013-01-01

Animal models have greatly enriched our understanding of the molecular mechanisms of numerous types of cancers. Gastric cancer is one of the most common cancers worldwide, with a poor prognosis and high incidence of drug-resistance. However, most inbred strains of mice have proven resistant to gastric carcinogenesis. To establish useful models which mimic human gastric cancer phenotypes, investigators have utilized animals infected with Helicobacter species and treated with carcinogens. In addition, by exploiting genetic engineering, a variety of transgenic and knockout mouse models of gastric cancer have emerged, such as INS-GAS mice and TFF1 knockout mice. Investigators have used the combination of carcinogens and gene alteration to accelerate gastric cancer development, but rarely do mouse models show an aggressive and metastatic gastric cancer phenotype that could be relevant to preclinical studies, which may require more specific targeting of gastric progenitor cells. Here, we review current gastric carcinogenesis mouse models and provide our future perspectives on this field. PMID:24216700

Phenotyping male infertility in the mouse: how to get the most out of a 'non-performer'.

PubMed

Borg, Claire L; Wolski, Katja M; Gibbs, Gerard M; O'Bryan, Moira K

2010-01-01

Functional male gametes are produced through complex processes that take place within the testis, epididymis and female reproductive tract. A breakdown at any of these phases can result in male infertility. The production of mutant mouse models often yields an unexpected male infertility phenotype. It is with this in mind that the current review has been written. The review aims to act as a guide to the 'non-reproductive biologist' to facilitate a systematic analysis of sterile or subfertile mice and to assist in extracting the maximum amount of information from each model. This is a review of the original literature on defects in the processes that take a mouse spermatogonial stem cell through to a fully functional spermatozoon, which result in male infertility. Based on literature searches and personal experience, we have outlined a step-by-step strategy for the analysis of an infertile male mouse line. A wide range of methods can be used to define the phenotype of an infertile male mouse. These methods range from histological methods such as electron microscopy and immunohistochemistry, to hormone analyses and methods to assess sperm maturation status and functional competence. With the increased rate of genetically modified mouse production, the generation of mouse models with unexpected male infertility is increasing. This manuscript will help to ensure that the maximum amount of information is obtained from each mouse model and, by extension, will facilitate the knowledge of both normal fertility processes and the causes of human infertility.

A surgical approach appropriate for targeted cochlear gene therapy in the mouse.

PubMed

Jero, J; Tseng, C J; Mhatre, A N; Lalwani, A K

2001-01-01

Therapeutic manipulations of the mammalian cochlea, including cochlear gene transfer, have been predominantly studied using the guinea pig as the experimental model. With the significant developments in mouse genomics and the availability of mutant strains of mice with well-characterized hearing loss, the mouse justifiably will be the preferred animal model for therapeutic manipulations. However, the potential advantages of the mouse model have not been fully realized due to the surgical difficulty of accessing its small cochlea. This study describes a ventral approach, instead of the routinely used postauricular approach in other rodents, for accessing the mouse middle and inner ear, and its application in cochlear gene transfer. This ventral approach enabled rapid and direct delivery of liposome-transgene complex to the mouse inner ear while avoiding blood loss, facial nerve morbidity, and mortality. Transgene expression at 3 days was detected in Reissner's membrane, spiral limbus, spiral ligament, and spiral ganglion cells, in a pattern similar to that previously described in the guinea pig. The successful access and delivery of material to the mouse cochlea and the replication of gene expression seen in the guinea pig demonstrated in this study should promote the use of the mouse in future studies investigating targeted cochlear therapy.

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

Wide-Area Cooperative Biometric Tagging, Tracking and Locating in a Multimodal Sensor Network

DTIC Science & Technology

2014-12-04

12] 89.3% 2.7% 7 5 50s Our Model 90.7% 2.7% 6 5 4.6s TABLE III: Comparison of tracking results on CAVIAR dataset. The number of trajectories in...other. We evaluate our approach on two widely used public single-camera pedestrian tracking datasets: the CAVIAR dataset [1] and the TownCentre dataset...collaborators at Progeny. It is also being provided to ONR along with datasets on which it has been tested. REFERENCES [1] Caviar dataset. http

Behavioral phenotypes of genetic mouse models of autism.

PubMed

Kazdoba, T M; Leach, P T; Crawley, J N

2016-01-01

More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Using Genetic Mouse Models to Gain Insight into Glaucoma: Past Results and Future Possibilities

PubMed Central

Fernandes, Kimberly A.; Harder, Jeffrey M.; Williams, Pete A.; Rausch, Rebecca L.; Kiernan, Amy E.; Nair, K. Saidas; Anderson, Michael G.; John, Simon W.; Howell, Gareth R.; Libby, Richard T.

2015-01-01

While all forms of glaucoma are characterized by a specific pattern of retinal ganglion cell death, they are clinically divided into several distinct subclasses, including normal tension glaucoma, primary open angle glaucoma, congenital glaucoma, and secondary glaucoma. For each type of glaucoma there are likely numerous molecular pathways that control susceptibility to the disease. Given this complexity, a single animal model will never precisely model all aspects of all the different types of human glaucoma. Therefore, multiple animal models have been utilized to study glaucoma but more are needed. Because of the powerful genetic tools available to use in the laboratory mouse, it has proven to be a highly useful mammalian system for studying the pathophysiology of human disease. The similarity between human and mouse eyes coupled with the ability to use a combination of advanced cell biological and genetic tools in mice have led to a large increase in the number of studies using mice to model specific glaucoma phenotypes. Over the last decade, numerous new mouse models and genetic tools have emerged, providing important insight into the cell biology and genetics of glaucoma. In this review, we describe available mouse genetic models that can be used to study glaucoma-relevant disease/pathobiology. Furthermore, we discuss how these models have been used to gain insights into ocular hypertension (a major risk factor for glaucoma) and glaucomatous retinal ganglion cell death. Finally, the potential for developing new mouse models and using advanced genetic tools and resources for studying glaucoma are discussed. PMID:26116903

Neurocognitive endophenotypes in CGG KI and Fmr1 KO mouse models of Fragile X-Associated disorders: an analysis of the state of the field

PubMed Central

Hunsaker, Michael R.

2013-01-01

It has become increasingly important that the field of behavioral genetics identifies not only the gross behavioral phenotypes associated with a given mutation, but also the behavioral endophenotypes that scale with the dosage of the particular mutation being studied. Over the past few years, studies evaluating the effects of the polymorphic CGG trinucleotide repeat on the FMR1 gene underlying Fragile X-Associated Disorders have reported preliminary evidence for a behavioral endophenotype in human Fragile X Premutation carrier populations as well as the CGG knock-in (KI) mouse model. More recently, the behavioral experiments used to test the CGG KI mouse model have been extended to the Fmr1 knock-out (KO) mouse model. When combined, these data provide compelling evidence for a clear neurocognitive endophenotype in the mouse models of Fragile X-Associated Disorders such that behavioral deficits scale predictably with genetic dosage. Similarly, it appears that the CGG KI mouse effectively models the histopathology in Fragile X-Associated Disorders across CGG repeats well into the full mutation range, resulting in a reliable histopathological endophenotype. These endophenotypes may influence future research directions into treatment strategies for not only Fragile X Syndrome, but also the Fragile X Premutation and Fragile X-Associated Tremor/Ataxia Syndrome (FXTAS). PMID:24627796

Long non-coding RNA expression patterns in lung tissues of chronic cigarette smoke induced COPD mouse model.

PubMed

Zhang, Haiyun; Sun, Dejun; Li, Defu; Zheng, Zeguang; Xu, Jingyi; Liang, Xue; Zhang, Chenting; Wang, Sheng; Wang, Jian; Lu, Wenju

2018-05-15

Long non-coding RNAs (lncRNAs) have critical regulatory roles in protein-coding gene expression. Aberrant expression profiles of lncRNAs have been observed in various human diseases. In this study, we investigated transcriptome profiles in lung tissues of chronic cigarette smoke (CS)-induced COPD mouse model. We found that 109 lncRNAs and 260 mRNAs were significantly differential expressed in lungs of chronic CS-induced COPD mouse model compared with control animals. GO and KEGG analyses indicated that differentially expressed lncRNAs associated protein-coding genes were mainly involved in protein processing of endoplasmic reticulum pathway, and taurine and hypotaurine metabolism pathway. The combination of high throughput data analysis and the results of qRT-PCR validation in lungs of chronic CS-induced COPD mouse model, 16HBE cells with CSE treatment and PBMC from patients with COPD revealed that NR_102714 and its associated protein-coding gene UCHL1 might be involved in the development of COPD both in mouse and human. In conclusion, our study demonstrated that aberrant expression profiles of lncRNAs and mRNAs existed in lungs of chronic CS-induced COPD mouse model. From animal models perspective, these results might provide further clues to investigate biological functions of lncRNAs and their potential target protein-coding genes in the pathogenesis of COPD.

Modeling bladder cancer in mice: opportunities and challenges

PubMed Central

Kobayashi, Takashi; Owczarek, Tomasz B.; McKiernan, James M.; Abate-Shen, Cory

2015-01-01

The prognosis and treatment of bladder cancer have hardly improved in the last 20 years. Bladder cancer remains a debilitating and often fatal disease, and among the most costly cancers to treat. The generation of informative mouse models has the potential to improve our understanding of bladder cancer progression, as well as impact its diagnosis and treatment. However, relatively few mouse models of bladder cancer have been described and particularly few that develop invasive cancer phenotypes. This review focuses on opportunities for improving the landscape of mouse models of bladder cancer. PMID:25533675

Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments.

PubMed

Ionescu, Catalin; Papava, Dragos; Olaru, Vlad; Sminchisescu, Cristian

2014-07-01

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.

Development and function of human innate immune cells in a humanized mouse model.

PubMed

Rongvaux, Anthony; Willinger, Tim; Martinek, Jan; Strowig, Till; Gearty, Sofia V; Teichmann, Lino L; Saito, Yasuyuki; Marches, Florentina; Halene, Stephanie; Palucka, A Karolina; Manz, Markus G; Flavell, Richard A

2014-04-01

Mice repopulated with human hematopoietic cells are a powerful tool for the study of human hematopoiesis and immune function in vivo. However, existing humanized mouse models cannot support development of human innate immune cells, including myeloid cells and natural killer (NK) cells. Here we describe two mouse strains called MITRG and MISTRG, in which human versions of four genes encoding cytokines important for innate immune cell development are knocked into their respective mouse loci. The human cytokines support the development and function of monocytes, macrophages and NK cells derived from human fetal liver or adult CD34(+) progenitor cells injected into the mice. Human macrophages infiltrated a human tumor xenograft in MITRG and MISTRG mice in a manner resembling that observed in tumors obtained from human patients. This humanized mouse model may be used to model the human immune system in scenarios of health and pathology, and may enable evaluation of therapeutic candidates in an in vivo setting relevant to human physiology.

Development and function of human innate immune cells in a humanized mouse model

PubMed Central

Rongvaux, Anthony; Willinger, Tim; Martinek, Jan; Strowig, Till; Gearty, Sofia V.; Teichmann, Lino L.; Saito, Yasuyuki; Marches, Florentina; Halene, Stephanie; Palucka, A. Karolina; Manz, Markus G.; Flavell, Richard A.

2014-01-01

Mice repopulated with human hematopoietic cells are a powerful tool for the study of human hematopoiesis and immune function in vivo. However, existing humanized mouse models are unable to support development of human innate immune cells, including myeloid cells and NK cells. Here we describe a mouse strain, called MI(S)TRG, in which human versions of four genes encoding cytokines important for innate immune cell development are knocked in to their respective mouse loci. The human cytokines support the development and function of monocytes/macrophages and natural killer cells derived from human fetal liver or adult CD34+ progenitor cells injected into the mice. Human macrophages infiltrated a human tumor xenograft in MI(S)TRG mice in a manner resembling that observed in tumors obtained from human patients. This humanized mouse model may be used to model the human immune system in scenarios of health and pathology, and may enable evaluation of therapeutic candidates in an in vivo setting relevant to human physiology. PMID:24633240

Setting up a hydrological model based on global data for the Ayeyarwady basin in Myanmar

NASA Astrophysics Data System (ADS)

ten Velden, Corine; Sloff, Kees; Nauta, Tjitte

2017-04-01

The use of global datasets in local hydrological modelling can be of great value. It opens up the possibility to include data for areas where local data is not or only sparsely available. In hydrological modelling the existence of both static physical data such as elevation and land use, and dynamic meteorological data such as precipitation and temperature, is essential for setting up a hydrological model, but often such data is difficult to obtain at the local level. For the Ayeyarwady catchment in Myanmar a distributed hydrological model (Wflow: https://github.com/openstreams/wflow) was set up with only global datasets, as part of a water resources study. Myanmar is an emerging economy, which has only recently become more receptive to foreign influences. It has a very limited hydrometeorological measurement network, with large spatial and temporal gaps, and data that are of uncertain quality and difficult to obtain. The hydrological model was thus set up based on resampled versions of the SRTM digital elevation model, the GlobCover land cover dataset and the HWSD soil dataset. Three global meteorological datasets were assessed and compared for use in the hydrological model: TRMM, WFDEI and MSWEP. The meteorological datasets were assessed based on their conformity with several precipitation station measurements, and the overall model performance was assessed by calculating the NSE and RVE based on discharge measurements of several gauging stations. The model was run for the period 1979-2012 on a daily time step, and the results show an acceptable applicability of the used global datasets in the hydrological model. The WFDEI forcing dataset gave the best results, with a NSE of 0.55 at the outlet of the model and a RVE of 8.5%, calculated over the calibration period 2006-2012. As a general trend the modelled discharge at the upstream stations tends to be underestimated, and at the downstream stations slightly overestimated. The quality of the discharge measurements that form the basis for the performance calculations is uncertain; data analysis suggests that rating curves are not frequently updated. The modelling results are not perfect and there is ample room for improvement, but the results are reasonable given the notion that setting up a hydrological model for this area would not have been possible without the use of global datasets due to the lack of available local data. The resulting hydrological model then enabled the set-up of the RIBASIM water allocation model for the Ayeyarwady basin in order to assess its water resources. The study discussed here is a first step; ideally this is followed up by a more thorough calibration and validation with the limited local measurements available, e.g. a precipitation correction based on the available rainfall measurements, to ensure the integration of global and local data.

Effect of electroacupuncture on brain-derived neurotrophic factor mRNA expression in mouse hippocampus following cerebral ischemia-reperfusion injury.

PubMed

Zhao, Jianxin; Xu, Huazhou; Tian, Yuanxiang; Hu, Manxiang; Xiao, Hongling

2013-04-01

This work aims to observe the effects of electroacupuncture on brain-derived neurotrophic factor (BDNF) mRNA expression in mouse hippocampus following cerebral ischemia-reperfusion injury. The models of mouse cerebral ischemia-reperfusion injury were established. A total of 96 healthy mice were randomly assigned into 4 groups, namely, the sham surgery, model, model + electroacupuncture, and mode + hydergine groups. Mice in the model + electroacupuncture group were treated through electroacupuncture at the Shenshu (BL 23), Geshu (BL 17), and Baihui (GV 20) acupoints. Mice in the model+hydergine group were intragastrically administered with hydergine (0.77 mg/kg(-1) x day(-1)). The levels of BDNF mRNA expressions in the hippocampus were ana lyzed through a semi-quantitative reverse transcription-polymerase chain reaction assay on days 1 and 7 after the surgeries. BDNF mRNA expressions in the mouse hippocampus of the model group on days 1 and 7 after the surgery were higher than those of the sham surgery group (both P < 0.01). On days 1 and 7 of the electroacupuncture treatment, BDNF mRNA expression in the mouse hippocampus of the model + electroacupuncture group was significantly elevated compared with the model group (both P < 0.01) or the model + hydergine group (both P < 0.01). On days 1 and 7 of the hydergine treatment, BDNF mRNA expression in the mouse hippocampus of the model + hydergine group tended to increase compared with the model group; however, statistical significance was not achieved (both P > 0.05). Electroacupuncture treatment enhances endogenous BDNF expression, which may improve the survival environment for intracerebral neurons and inhibit the apoptosis of hippocampal cells.

Topographical effects of climate dataset and their impacts on the estimation of regional net primary productivity

NASA Astrophysics Data System (ADS)

Sun, L. Qing; Feng, Feng X.

2014-11-01

In this study, we first built and compared two different climate datasets for Wuling mountainous area in 2010, one of which considered topographical effects during the ANUSPLIN interpolation was referred as terrain-based climate dataset, while the other one did not was called ordinary climate dataset. Then, we quantified the topographical effects of climatic inputs on NPP estimation by inputting two different climate datasets to the same ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS), to evaluate the importance of considering relief when estimating NPP. Finally, we found the primary contributing variables to the topographical effects through a series of experiments given an overall accuracy of the model output for NPP. The results showed that: (1) The terrain-based climate dataset presented more reliable topographic information and had closer agreements with the station dataset than the ordinary climate dataset at successive time series of 365 days in terms of the daily mean values. (2) On average, ordinary climate dataset underestimated NPP by 12.5% compared with terrain-based climate dataset over the whole study area. (3) The primary climate variables contributing to the topographical effects of climatic inputs for Wuling mountainous area were temperatures, which suggest that it is necessary to correct temperature differences for estimating NPP accurately in such a complex terrain.

Intramyocardial Injection of siRNAs Can Efficiently Establish Myocardial Tissue-Specific Renalase Knockdown Mouse Model.

PubMed

Huang, Kun; Liu, Ju; Zhang, Hui; Wang, Jiliang; Li, Huili

2016-01-01

Ischaemia/reperfusion (I/R) injury will cause additional death of cardiomyocytes in ischaemic heart disease. Recent studies revealed that renalase was involved in the I/R injury. So, the myocardial tissue-specific knockdown mouse models were needed for the investigations of renalase. To establish the mouse models, intramyocardial injection of siRNAs targeting renalase was performed in mice. The wild distribution and high transfection efficiency of the siRNAs were approved. And the renalase expression was efficiently suppressed in myocardial tissue. Compared with the high cost, time consumption, and genetic compensation risk of the Cre/loxP technology, RNA interference (RNAi) technology is much cheaper and less time-consuming. Among the RNAi technologies, injection of siRNAs is safer than virus. And considering the properties of the I/R injury mouse models, the efficiency and durability of injection with siRNAs are acceptable for the studies. Altogether, intramyocardial injection of siRNAs targeting renalase is an economical, safe, and efficient method to establish myocardial tissue-specific renalase knockdown mouse models.

Improvements and Limitations of Humanized Mouse Models for HIV Research: NIH/NIAID "Meet the Experts" 2015 Workshop Summary.

PubMed

Akkina, Ramesh; Allam, Atef; Balazs, Alejandro B; Blankson, Joel N; Burnett, John C; Casares, Sofia; Garcia, J Victor; Hasenkrug, Kim J; Kashanchi, Fatah; Kitchen, Scott G; Klein, Florian; Kumar, Priti; Luster, Andrew D; Poluektova, Larisa Y; Rao, Mangala; Sanders-Beer, Brigitte E; Shultz, Leonard D; Zack, Jerome A

2016-02-01

The number of humanized mouse models for the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) and other infectious diseases has expanded rapidly over the past 8 years. Highly immunodeficient mouse strains, such as NOD/SCID/gamma chain(null) (NSG, NOG), support better human hematopoietic cell engraftment. Another improvement is the derivation of highly immunodeficient mice, transgenic with human leukocyte antigens (HLAs) and cytokines that supported development of HLA-restricted human T cells and heightened human myeloid cell engraftment. Humanized mice are also used to study the HIV reservoir using new imaging techniques. Despite these advances, there are still limitations in HIV immune responses and deficits in lymphoid structures in these models in addition to xenogeneic graft-versus-host responses. To understand and disseminate the improvements and limitations of humanized mouse models to the scientific community, the NIH sponsored and convened a meeting on April 15, 2015 to discuss the state of knowledge concerning these questions and best practices for selecting a humanized mouse model for a particular scientific investigation. This report summarizes the findings of the NIH meeting.

Oral recombinant human or mouse lactoferrin reduces Mycobacterium tuberculosis TDM induced granulomatous lung pathology.

PubMed

Hwang, Shen-An; Kruzel, Marian L; Actor, Jeffrey K

2017-02-01

Trehalose 6'6-dimycolate (TDM) is the most abundant glycolipid on the cell wall of Mycobacterium tuberculosis (MTB). TDM is capable of inducing granulomatous pathology in mouse models that resembles those induced by MTB infection. Using the acute TDM model, this work investigates the effect of recombinant human and mouse lactoferrin to reduce granulomatous pathology. C57BL/6 mice were injected intravenously with TDM at a dose of 25 μg·mouse -1 . At day 4 and 6, recombinant human or mouse lactoferrin (1 mg·(100 μL) -1 ·mouse -1 ) were delivered by gavage. At day 7 after TDM injection, mice were evaluated for lung pathology, cytokine production, and leukocyte populations. Mice given human or mouse lactoferrin had reduced production of IL-12p40 in their lungs. Mouse lactoferrin increased IL-6 and KC (CXCL1) in lung tissue. Increased numbers of macrophages were observed in TDM-injected mice given human or mouse lactoferrin. Granulomatous pathology, composed of mainly migrated leukocytes, was visually reduced in mice that received human or mouse lactoferrin. Quantitation of granulomatous pathology demonstrated a significant decrease in mice given human or mouse lactoferrin compared with TDM control mice. This report is the first to directly compare the immune modulatory effects of both heterologous recombinant human and homologous mouse lactoferrin on the development of TDM-induced granulomas.

Mouse neuroblastoma cell-based model and the effect of epileptic events on calcium oscillations and neural spikes

NASA Astrophysics Data System (ADS)

Kim, Suhwan; Jung, Unsang; Baek, Juyoung; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

2013-01-01

Recently, mouse neuroblastoma cells have been considered as an attractive model for the study of human neurological and prion diseases, and they have been intensively used as a model system in different areas. For example, the differentiation of neuro2a (N2A) cells, receptor-mediated ion current, and glutamate-induced physiological responses have been actively investigated with these cells. These mouse neuroblastoma N2A cells are of interest because they grow faster than other cells of neural origin and have a number of other advantages. The calcium oscillations and neural spikes of mouse neuroblastoma N2A cells in epileptic conditions are evaluated. Based on our observations of neural spikes in these cells with our proposed imaging modality, we reported that they can be an important model in epileptic activity studies. We concluded that mouse neuroblastoma N2A cells produce epileptic spikes in vitro in the same way as those produced by neurons or astrocytes. This evidence suggests that increased levels of neurotransmitter release due to the enhancement of free calcium from 4-aminopyridine causes the mouse neuroblastoma N2A cells to produce epileptic spikes and calcium oscillations.

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Goldberg, Richard; Chryssanthacopoulos, James

2014-01-01

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

76 FR 35856 - Fisheries of the South Atlantic; Southeast Data, Assessment, and Review (SEDAR); Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-20

... evaluates potential datasets and recommends which datasets are appropriate for assessment analyses. The... Series Using datasets recommended from the Data Workshop, Panelists will employ assessment models to...

76 FR 47564 - Fisheries of the South Atlantic; Southeast Data, Assessment, and Review (SEDAR); Assessment...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-05

... compiles and evaluates potential datasets and recommends which datasets are appropriate for assessment... Series Using datasets recommended from the Data Workshop, Panelists will employ assessment models to...

High-fertility phenotypes: two outbred mouse models exhibit substantially different molecular and physiological strategies warranting improved fertility.

PubMed

Langhammer, Martina; Michaelis, Marten; Hoeflich, Andreas; Sobczak, Alexander; Schoen, Jennifer; Weitzel, Joachim M

2014-01-01

Animal models are valuable tools in fertility research. Worldwide, there are more than 400 transgenic or knockout mouse models available showing a reproductive phenotype; almost all of them exhibit an infertile or at least subfertile phenotype. By contrast, animal models revealing an improved fertility phenotype are barely described. This article summarizes data on two outbred mouse models exhibiting a 'high-fertility' phenotype. These mouse lines were generated via selection over a time period of more than 40 years and 161 generations. During this selection period, the number of offspring per litter and the total birth weight of the entire litter nearly doubled. Concomitantly with the increased fertility phenotype, several endocrine parameters (e.g. serum testosterone concentrations in male animals), physiological parameters (e.g. body weight, accelerated puberty, and life expectancy), and behavioral parameters (e.g. behavior in an open field and endurance fitness on a treadmill) were altered. We demonstrate that the two independently bred high-fertility mouse lines warranted their improved fertility phenotype using different molecular and physiological strategies. The fertility lines display female- as well as male-specific characteristics. These genetically heterogeneous mouse models provide new insights into molecular and cellular mechanisms that enhance fertility. In view of decreasing fertility in men, these models will therefore be a precious information source for human reproductive medicine. Translated abstract A German translation of abstract is freely available at http://www.reproduction-online.org/content/147/4/427/suppl/DC1.

Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes

PubMed Central

Pantazatos, Spiro P.; Li, Jianrong; Pavlidis, Paul; Lussier, Yves A.

2009-01-01

An approach towards heterogeneous neuroscience dataset integration is proposed that uses Natural Language Processing (NLP) and a knowledge-based phenotype organizer system (PhenOS) to link ontology-anchored terms to underlying data from each database, and then maps these terms based on a computable model of disease (SNOMED CT®). The approach was implemented using sample datasets from fMRIDC, GEO, The Whole Brain Atlas and Neuronames, and allowed for complex queries such as “List all disorders with a finding site of brain region X, and then find the semantically related references in all participating databases based on the ontological model of the disease or its anatomical and morphological attributes”. Precision of the NLP-derived coding of the unstructured phenotypes in each dataset was 88% (n = 50), and precision of the semantic mapping between these terms across datasets was 98% (n = 100). To our knowledge, this is the first example of the use of both semantic decomposition of disease relationships and hierarchical information found in ontologies to integrate heterogeneous phenotypes across clinical and molecular datasets. PMID:20495688

Ultrasonic vocalizations: a tool for behavioural phenotyping of mouse models of neurodevelopmental disorders

PubMed Central

Scattoni, Maria Luisa; Crawley, Jacqueline; Ricceri, Laura

2009-01-01

In neonatal mice ultrasonic vocalizations have been studied both as an early communicative behavior of the pup-mother dyad and as a sign of an aversive affective state. Adult mice of both sexes produce complex ultrasonic vocalization patterns in different experimental/social contexts. All these vocalizations are becoming an increasingly valuable assay for behavioral phenotyping throughout the mouse life-span and alterations of the ultrasound patterns have been reported in several mouse models of neurodevelopmental disorders. Here we also show that the modulation of vocalizations by maternal cues (maternal potentiation paradigm) – originally identified and investigated in rats - can be measured in C57Bl/6 mouse pups with appropriate modifications of the rat protocol and can likely be applied to mouse behavioral phenotyping. In addition we suggest that a detailed qualitative evaluation of neonatal calls together with analysis of adult mouse vocalization patterns in both sexes in social settings, may lead to a greater understanding of the communication value of vocalizations in mice. Importantly, both neonatal and adult USV altered patterns can be determined during the behavioural phenotyping of mouse models of human neurodevelopmental and neuropsychiatric disorders, starting from those in which deficits in communication are a primary symptom. PMID:18771687

A Comparison of Some Organizational Characteristics of the Mouse Central Retina and the Human Macula

PubMed Central

Hoo, Juyea; Yee, Claudine; Williams, David S.

2015-01-01

Mouse models have greatly assisted our understanding of retinal degenerations. However, the mouse retina does not have a macula, leading to the question of whether the mouse is a relevant model for macular degeneration. In the present study, a quantitative comparison between the organization of the central mouse retina and the human macula was made, focusing on some structural characteristics that have been suggested to be important in predisposing the macula to stresses leading to degeneration: photoreceptor density, phagocytic load on the RPE, and the relative thinness of Bruch’s membrane. Light and electron microscopy measurements from retinas of two strains of mice, together with published data on human retinas, were used for calculations and subsequent comparisons. As in the human retina, the central region of the mouse retina possesses a higher photoreceptor cell density and a thinner Bruch’s membrane than in the periphery; however, the magnitudes of these periphery to center gradients are larger in the human. Of potentially greater relevance is the actual photoreceptor cell density, which is much greater in the mouse central retina than in the human macula, underlying a higher phagocytic load for the mouse RPE. Moreover, at eccentricities that correspond to the peripheral half of the human macula, the rod to cone ratio is similar between mouse and human. Hence, with respect to photoreceptor density and phagocytic load of the RPE, the central mouse retina models at least the more peripheral part of the macula, where macular degeneration is often first evident. PMID:25923208

Potential for using regional and global datasets for national scale ecosystem service modelling

NASA Astrophysics Data System (ADS)

Maxwell, Deborah; Jackson, Bethanna

2016-04-01

Ecosystem service models are increasingly being used by planners and policy makers to inform policy development and decisions about national-level resource management. Such models allow ecosystem services to be mapped and quantified, and subsequent changes to these services to be identified and monitored. In some cases, the impact of small scale changes can be modelled at a national scale, providing more detailed information to decision makers about where to best focus investment and management interventions that could address these issues, while moving toward national goals and/or targets. National scale modelling often uses national (or local) data (for example, soils, landcover and topographical information) as input. However, there are some places where fine resolution and/or high quality national datasets cannot be easily obtained, or do not even exist. In the absence of such detailed information, regional or global datasets could be used as input to such models. There are questions, however, about the usefulness of these coarser resolution datasets and the extent to which inaccuracies in this data may degrade predictions of existing and potential ecosystem service provision and subsequent decision making. Using LUCI (the Land Utilisation and Capability Indicator) as an example predictive model, we examine how the reliability of predictions change when national datasets of soil, landcover and topography are substituted with coarser scale regional and global datasets. We specifically look at how LUCI's predictions of where water services, such as flood risk, flood mitigation, erosion and water quality, change when national data inputs are replaced by regional and global datasets. Using the Conwy catchment, Wales, as a case study, the land cover products compared are the UK's Land Cover Map (2007), the European CORINE land cover map and the ESA global land cover map. Soils products include the National Soil Map of England and Wales (NatMap) and the European Soils Database. NEXTmap elevation data, which covers the UK and parts of continental Europe, are compared to global AsterDEM and SRTM30 topographical products. While the regional and global datasets can be used to fill gaps in data requirements, the coarser resolution of these datasets means that there is greater aggregation of information over larger areas. This loss of detail impacts on the reliability of model output, particularly where significant discrepancies between datasets exist. The implications of this loss of detail in terms of spatial planning and decision making is discussed. Finally, in the context of broader development the need for better nationally and globally available data to allow LUCI and other ecosystem models to become more globally applicable is highlighted.

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

Ding, Ying; Adachi, Hiroaki, E-mail: hadachi-ns@umin.org; Department of Neurology, University of Occupational and Environmental Health School of Medicine, 1-1 Iseigaoka, Yahata-nishi-ku, Kitakyushu 807-8555

Spinal and bulbar muscular atrophy (SBMA) is an inherited motor neuron disease caused by the expansion of a polyglutamine (polyQ)-encoding tract within the androgen receptor (AR) gene. The pathologic features of SBMA are motor neuron loss in the spinal cord and brainstem and diffuse nuclear accumulation and nuclear inclusions of mutant AR in residual motor neurons and certain visceral organs. Hepatocyte growth factor (HGF) is a polypeptide growth factor which has neuroprotective properties. To investigate whether HGF overexpression can affect disease progression in a mouse model of SBMA, we crossed SBMA transgenic model mice expressing an AR gene with anmore » expanded CAG repeat with mice overexpressing HGF. Here, we report that high expression of HGF induces Akt phosphorylation and modestly ameliorated motor symptoms in an SBMA transgenic mouse model treated with or without castration. These findings suggest that HGF overexpression can provide a potential therapeutic avenue as a combination therapy with disease-modifying therapies in SBMA. - Highlights: • HGF overexpression ameliorates the motor phenotypes of the SBMA mouse model. • HGF overexpression induces Akt phosphorylation in the SBMA mouse model. • This is the first report of combination therapy in a mouse model of polyQ diseases.« less

Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

NASA Astrophysics Data System (ADS)

Guha, Rajarshi; Schürer, Stephan C.

2008-06-01

Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.

Transgenic and gene knockout mice in gastric cancer research

PubMed Central

Jiang, Yannan; Yu, Yingyan

2017-01-01

Mouse models are useful tool for carcinogenic study. They will greatly enrich the understanding of pathogenesis and molecular mechanisms for gastric cancer. However, only few of mice could develop gastric cancer spontaneously. With the development and improvement of gene transfer technology, investigators created a variety of transgenic and knockout/knockin mouse models of gastric cancer, such as INS-GAS mice and gastrin knockout mice. Combined with helicobacter infection and carcinogens treatment, these transgenic/knockout/knockin mice developed precancerous or cancerous lesions, which are proper for gene function study or experimental therapy. Here we review the progression of genetically engineered mouse models on gastric cancer research, and emphasize the effects of chemical carcinogens or infectious factors on carcinogenesis of genetically modified mouse. We also emphasize the histological examination on mouse stomach. We expect to provide researchers with some inspirations on this field. PMID:27713138

A Mouse Model to Investigate Postmenopausal Biology as an Etiology of Ovarian Cancer Risk

DTIC Science & Technology

2006-11-01

Wv mice and genetic alterations such as p53, pten, or p27kip1, which are found in human ovarian cancer. 2. Body: Research Progress In the first year...press (Yang et al., Am. J. Pathology 2007). To collaborate with the mouse model study, we have also examined human ovaries obtained from prophylactic...results in the coming years. Xu, Xiangxi, Ph.D. 8 3. Key Research Accomplishments (1) Further verify the relevance of the Wv mouse model to human

The Oncogenic Role of RhoGAPs in Basal-Like Breast Cancer

DTIC Science & Technology

2015-02-01

cell lines, and mouse models . c) In vivo tumorigenesis and metastasis assays. Milestones: Identify whether ArhGAP11A and RacGAP1 can promote tumor growth...also upregulated in basal (C3(I)-Tag) but not luminal (MMTV-Neu) genetically- engineered mouse models (Fig. 1B). At the protein level, RacGAP1 was...hypothesis that these RhoGAPs are indeed playing an oncogenic role in these cells. Human Tumors Mouse Model Tumors Normal Luminal A Basal-like Normal

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

75 FR 78679 - Fisheries of the Gulf of Mexico and South Atlantic; Southeast Data, Assessment, and Review (SEDAR...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-16

... evaluates potential datasets and recommends which datasets are appropriate for assessment analyses. The...: Using datasets recommended from the Data Workshop, participants will employ assessment models to...

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

A Physiologically Based Kinetic Model of Rat and Mouse Gestation: Disposition of a Weak Acid

EPA Science Inventory

A physiologically based toxicokinetic model of gestation in the rat mouse has been developed. The model is superimposed on the normal growth curve for nonpregnant females. It describes the entire gestation period including organogenesis. The model consists of uterus, mammary tiss...

Priceless GEMMs: genetically engineered mouse models for colorectal cancer drug development.

PubMed

Roper, Jatin; Hung, Kenneth E

2012-08-01

To establish effective drug development for colorectal cancer (CRC), preclinical models that are robust surrogates for human disease are crucial. Mouse models are an attractive platform because of their relatively low cost, short life span, and ease of use. There are two main categories of mouse CRC models: xenografts derived from implantation of CRC cells or tumors in immunodeficient mice; and genetically engineered mouse models (GEMMs) derived from modification of human cancer predisposition genes, resulting in spontaneous tumor formation. Here, we review xenografts and GEMMs and focus on their potential application in translational research. Furthermore, we describe newer GEMMs for sporadic CRC that are particularly suitable for drug testing. Finally, we discuss recent advances in small-animal imaging, such as optical colonoscopy, which allow in vivo assessment of tumors. With the increasing sophistication of GEMMs, our preclinical armamentarium provides new hope for the ongoing war against CRC. Copyright © 2012. Published by Elsevier Ltd.

A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer.

PubMed

Betzler, Alexander M; Kochall, Susan; Blickensdörfer, Linda; Garcia, Sebastian A; Thepkaysone, May-Linn; Nanduri, Lahiri K; Muders, Michael H; Weitz, Jürgen; Reissfelder, Christoph; Schölch, Sebastian

2017-07-06

Despite the advantages of easy applicability and cost-effectiveness, colorectal cancer mouse models based on tumor cell injection have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Genetically engineered mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in large organs such as the colon in which only a single tumor is desired. As a result, an immunocompetent, genetically engineered mouse model of colorectal cancer was developed which develops highly uniform tumors and can be used for tumor biology studies as well as therapeutic trials. Tumor development is initiated by surgical, segmental infection of the distal colon with adeno-cre virus in compound conditionally mutant mice. The tumors can be easily detected and monitored via colonoscopy. We here describe the surgical technique of segmental adeno-cre infection of the colon, the surveillance of the tumor via high-resolution colonoscopy and present the resulting colorectal tumors.

Absence of Prenatal Forebrain Defects in the Dp(16)1Yey/+ Mouse Model of Down Syndrome

PubMed Central

Goodliffe, Joseph W.; Olmos-Serrano, Jose Luis; Aziz, Nadine M.; Pennings, Jeroen L.A.; Guedj, Faycal; Bianchi, Diana W.

2016-01-01

Studies in humans with Down syndrome (DS) show that alterations in fetal brain development are followed by postnatal deficits in neuronal numbers, synaptic plasticity, and cognitive and motor function. This same progression is replicated in several mouse models of DS. Dp(16)1Yey/+ (hereafter called Dp16) is a recently developed mouse model of DS in which the entire region of mouse chromosome 16 that is homologous to human chromosome 21 has been triplicated. As such, Dp16 mice may more closely reproduce neurodevelopmental changes occurring in humans with DS. Here, we present the first comprehensive cellular and behavioral study of the Dp16 forebrain from embryonic to adult stages. Unexpectedly, our results demonstrate that Dp16 mice do not have prenatal brain defects previously reported in human fetal neocortex and in the developing forebrains of other mouse models, including microcephaly, reduced neurogenesis, and abnormal cell proliferation. Nevertheless, we found impairments in postnatal developmental milestones, fewer inhibitory forebrain neurons, and deficits in motor and cognitive performance in Dp16 mice. Therefore, although this new model does not express prenatal morphological phenotypes associated with DS, abnormalities in the postnatal period appear sufficient to produce significant cognitive deficits in Dp16. SIGNIFICANCE STATEMENT Down syndrome (DS) leads to intellectual disability. Several mouse models have increased our understanding of the neuropathology of DS and are currently being used to test therapeutic strategies. A new mouse model that contains an expanded number of DS-related genes, known as Dp(16)1Yey/+ (Dp16), has been generated recently. We sought to determine whether the extended triplication creates a better phenocopy of DS-related brain pathologies. We measured embryonic development, forebrain maturation, and perinatal/adult behavior and revealed an absence of prenatal phenotypes in Dp16 fetal brain, but specific cellular and behavioral deficits after the first 2 postnatal weeks. These results uncover important differences in prenatal phenotype between Dp16 animals and humans with DS and other DS mouse models. PMID:26961948

Modelling land cover change in the Ganga basin

NASA Astrophysics Data System (ADS)

Moulds, S.; Tsarouchi, G.; Mijic, A.; Buytaert, W.

2013-12-01

Over recent decades the green revolution in India has driven substantial environmental change. Modelling experiments have identified northern India as a 'hot spot' of land-atmosphere coupling strength during the boreal summer. However, there is a wide range of sensitivity of atmospheric variables to soil moisture between individual climate models. The lack of a comprehensive land cover change dataset to force climate models has been identified as a major contributor to model uncertainty. In this work a time series dataset of land cover change between 1970 and 2010 is constructed for northern India to improve the quantification of regional hydrometeorological feedbacks. The MODIS instrument on board the Aqua and Terra satellites provides near-continuous remotely sensed datasets from 2000 to the present day. However, the quality of satellite products before 2000 is poor. To complete the dataset MODIS images are extrapolated back in time using the Conversion of Land Use and its Effects at small regional extent (CLUE-s) modelling framework. Non-spatial estimates of land cover area from national agriculture and forest statistics, available on a state-wise, annual basis, are used as a direct model input. Land cover change is allocated spatially as a function of biophysical and socioeconomic drivers identified using logistic regression. This dataset will provide an essential input to a high resolution, physically based land surface model to generate the lower boundary condition to assess the impact of land cover change on regional climate.

Characterization and visualization of the accuracy of FIA's CONUS-wide tree species datasets

Treesearch

Rachel Riemann; Barry T. Wilson

2014-01-01

Modeled geospatial datasets have been created for 325 tree species across the contiguous United States (CONUS). Effective application of all geospatial datasets depends on their accuracy. Dataset error can be systematic (bias) or unsystematic (scatter), and their magnitude can vary by region and scale. Each of these characteristics affects the locations, scales, uses,...

Use of a highly sensitive strand-specific quantitative PCR to identify abortive replication in the mouse model of respiratory syncytial virus disease

PubMed Central

2010-01-01

Background The BALB/c mouse is commonly used to study RSV infection and disease. However, despite the many advantages of this well-characterised model, the inoculum is large, viral replication is restricted and only a very small amount of virus can be recovered from infected animals. A key question in this model is the fate of the administered virus. Is replication really being measured or is the model measuring the survival of the virus over time? To answer these questions we developed a highly sensitive strand-specific quantitative PCR (QPCR) able to accurately quantify the amount of RSV replication in the BALB/c mouse lung, allowing characterisation of RSV negative and positive strand RNA dynamics. Results In the mouse lung, no increase in RSV genome was seen above the background of the original inoculum whilst only a limited transient increase (< 1 log) in positive strand, replicative intermediate (RI) RNA occurred. This RNA did however persist at detectable levels for 59 days post infection. As expected, ribavirin therapy reduced levels of infectious virus and RI RNA in the mouse lung. However, whilst Palivizumab therapy was also able to reduce levels of infectious virus, it failed to prevent production of intracellular RI RNA. A comparison of RSV RNA kinetics in human (A549) and mouse (KLN205) cell lines demonstrated that RSV replication was also severely delayed and impaired in vitro in the mouse cells. Conclusions This is the first time that such a sensitive strand-specific QPCR technique has been to the RSV mouse system. We have accurately quantified the restricted and abortive nature of RSV replication in the mouse. Further in vitro studies in human and mouse cells suggest this restricted replication is due at least in part to species-specific host cell-viral interactions. PMID:20860795

Establishment of mouse neuron and microglial cell co-cultured models and its action mechanism.

PubMed

Zhang, Bo; Yang, Yunfeng; Tang, Jun; Tao, Yihao; Jiang, Bing; Chen, Zhi; Feng, Hua; Yang, Liming; Zhu, Gang

2017-06-27

The objective of this study is to establish a co-culture model of mouse neurons and microglial cells, and to analyze the mechanism of action of oxygen glucose deprivation (OGD) and transient oxygen glucose deprivation (tOGD) preconditioning cell models. Mouse primary neurons and BV2 microglial cells were successfully cultured, and the OGD and tOGD models were also established. In the co-culture of mouse primary neurons and microglial cells, the cell number of tOGD mouse neurons and microglial cells was larger than the OGD cell number, observed by a microscope. CCK-8 assay result showed that at 1h after treatment, the OD value in the control group is lower compared to all the other three groups (P < 0.05). The treatment group exhibited the highest OD value among the four groups. The results observed at 5h were consistent with the results at 1 h. Flow cytometry results showed that at 1h after treatment the apoptosis percentages is higher in the control group compared to other three groups (P < 0.05). Mouse brain tissues were collected and primary neurons cells were cultured. In the meantime mouse BV2 microglia cells were cultured. Two types of cells were co-cultured, and OGD and tOGD cell models were established. There were four groups in the experiment: control group (OGD), treatment group (tOGD+OGD), placebo group (tOGD+OGD+saline) and minocycline intervention group (tOGD+OGD+minocycline). CCK-8 kit was used to detect cell viability and flow cytometry was used to detect apoptosis. In this study, mouse primary neurons and microglial cells were co-cultured. The OGD and tOGD models were established successfully. tOGD was able to effectively protect neurons and microglial cells from damage, and inhibit the apoptosis caused by oxygen glucose deprivation.

BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.

PubMed

Spinelli, Lionel; Carpentier, Sabrina; Montañana Sanchis, Frédéric; Dalod, Marc; Vu Manh, Thien-Phong

2015-10-19

Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one's biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes. BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/applications/BubbleGUM/index.html .

Actinic keratosis modelling in mice: A translational study

PubMed Central

Vandenberghe, Isabelle; Cartron, Valérie; Cèbe, Patrick; Blanchet, Jean-Christophe; Sibaud, Vincent; Guilbaud, Nicolas; Audoly, Laurent; Lamant, Laurence; Kruczynski, Anna

2017-01-01

Background Actinic keratoses (AK) are pre-malignant cutaneous lesions caused by prolonged exposure to ultraviolet radiation. As AKs lesions are generally accepted to be the initial lesions in a disease continuum that progresses to squamous cell carcinoma (SCC), AK lesions have to be treated. They are also the second most common reason for visits to the dermatologist. Several treatments are available but their efficacy still needs to be improved. The UV-B-induced KA lesion mouse model is used in preclinical studies to assess the efficacy of novel molecules, even though it is often more representative of advanced AK or SCC. Objectives Here we report on a translational study, comparing the various stages of AK development in humans and in the UV-B irradiated mouse model, as well as the optimization of photograph acquisition of AK lesions on mouse skin. Methods Human and mouse skin lesions were analysed by histology and immunohistochemistry. Mouse lesions were also assessed using a digital dermatoscope. Results An histological and phenotypic analysis, including p53, Ki67 and CD3 expression detection, performed on human and mouse AK lesions, shows that overall AK modelling in mice is relevant in the clinical situation. Some differences are observed, such as disorganization of keratinocytes of the basal layer and a number of atypical nuclei which are more numerous in human AK, whereas much more pronounced acanthosis is observed in skin lesion in mice. Thanks to this translational study, we are able to select appropriate experimental conditions for establishing either early or advanced stage AK or an SCC model. Furthermore, we optimized photograph acquisition of AK lesions on mouse skin by using a digital dermatoscope which is also used in clinics and allows reproducible photograph acquisition for further reliable assessment of mouse lesions. Use of this camera is illustrated through a pharmacological study assessing the activity of CARAC®. Conclusion These data demonstrate that this mouse model of UV-B-induced skin lesions is predictive for the identification of novel therapeutic treatments for both early and advanced stages of the disease. PMID:28662116

Taltirelin alleviates fatigue-like behavior in mouse models of cancer-related fatigue.

PubMed

Dougherty, John P; Wolff, Brian S; Cullen, Mary J; Saligan, Leorey N; Gershengorn, Marvin C

2017-10-01

Fatigue affects most cancer patients and has numerous potential causes, including cancer itself and cancer treatment. Cancer-related fatigue (CRF) is not relieved by rest, can decrease quality of life, and has no FDA-approved therapy. Thyrotropin-releasing hormone (TRH) has been proposed as a potential novel treatment for CRF, but its efficacy against CRF remains largely untested. Thus, we tested the TRH analog, taltirelin (TAL), in mouse models of CRF. To model fatigue, we used a mouse model of chemotherapy, a mouse model of radiation therapy, and mice bearing colon 26 carcinoma tumors. We used the treadmill fatigue test to assess fatigue-like behavior after treatment with TAL. Additionally, we used wild-type and TRH receptor knockout mice to determine which TRH receptor was necessary for the actions of TAL. Tumor-bearing mice displayed muscle wasting and all models caused fatigue-like behavior, with mice running a shorter distance in the treadmill fatigue test than controls. TAL reversed fatigue-like behavior in all three models and the mouse TRH 1 receptor was necessary for the effects of TAL. These data suggest that TAL may be useful in alleviating fatigue in all cancer patients and provide further support for evaluating TAL as a potential therapy for CRF in humans. Published by Elsevier Ltd.

Resolution testing and limitations of geodetic and tsunami datasets for finite fault inversions along subduction zones

NASA Astrophysics Data System (ADS)

Williamson, A.; Newman, A. V.

2017-12-01

Finite fault inversions utilizing multiple datasets have become commonplace for large earthquakes pending data availability. The mixture of geodetic datasets such as Global Navigational Satellite Systems (GNSS) and InSAR, seismic waveforms, and when applicable, tsunami waveforms from Deep-Ocean Assessment and Reporting of Tsunami (DART) gauges, provide slightly different observations that when incorporated together lead to a more robust model of fault slip distribution. The merging of different datasets is of particular importance along subduction zones where direct observations of seafloor deformation over the rupture area are extremely limited. Instead, instrumentation measures related ground motion from tens to hundreds of kilometers away. The distance from the event and dataset type can lead to a variable degree of resolution, affecting the ability to accurately model the spatial distribution of slip. This study analyzes the spatial resolution attained individually from geodetic and tsunami datasets as well as in a combined dataset. We constrain the importance of distance between estimated parameters and observed data and how that varies between land-based and open ocean datasets. Analysis focuses on accurately scaled subduction zone synthetic models as well as analysis of the relationship between slip and data in recent large subduction zone earthquakes. This study shows that seafloor deformation sensitive datasets, like open-ocean tsunami waveforms or seafloor geodetic instrumentation, can provide unique offshore resolution for understanding most large and particularly tsunamigenic megathrust earthquake activity. In most environments, we simply lack the capability to resolve static displacements using land-based geodetic observations.

A multivariate model for predicting segmental body composition.

PubMed

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

NASA Technical Reports Server (NTRS)

Raschke, Ehrhard; Kinne, Stefan; Rossow, William B.; Stackhouse, Paul W. Jr.; Wild, Martin

2016-01-01

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

MR images of mouse brain using clinical 3T MR scanner and 4CH-Mouse coil

NASA Astrophysics Data System (ADS)

Lim, Soo Mee; Park, Eun Mi; Lyoo, In Kyoon; Lee, Junghyun; Han, Bo Mi; Lee, Jeong Kyong; Lee, Su Bin

2015-07-01

Objectives: Although small-bore high-field magnets are useful for research in small rodent models,this technology, however, has not been easily accessible to most researchers. This current study, thus,tried to evaluate the usability of 4CH-Mouse coil (Philips Healthcare, Best, the Netherlands) forpreclinical investigations in clinical 3T MR scan environment. We evaluated the effects of ischemicpreconditioning (IP) in the mouse stroke model with clinical 3T MR scanner and 4CH-Mouse coil. Materials and Methods: Experiments were performed on male C57BL/6 mice that either received the IP or sham operation (control). Three different MR sequences including diffusion weighted images (DWI), T2-weighted images (T2WI), and fluid attenuated inversion recovery (FLAIR) were performed on the mouse brains following 24, 72 hours of middle cerebral artery occlusion (MCAO) and analyzed for infarct lesions. Results: The images showed that the IP-treated mouse brains had significantly smaller infarct volumes compared to the control group. Of the MR sequences employed, the T2WI showed the highest level of correlations with postmortem infarct volume measurements. Conclusions: The clinical 3T MR scanner turned out to have a solid potential as a practical tool for imaging small animal brains. MR sequences including DWI, T2WI, FLAIR were obtained with acceptable resolution and in a reasonable time constraint in evaluating a mouse stroke model brain.

Geospatial datasets for assessing the effects of rangeland conditions on dissolved-solids yields in the Upper Colorado River Basin

USGS Publications Warehouse

Tillman, Fred D.; Flynn, Marilyn E.; Anning, David W.

2015-01-01

In 2009, the U.S. Geological Survey (USGS) developed a Spatially Referenced Regressions on Watershed Attributes (SPARROW) surface-water quality model for the Upper Colorado River Basin (UCRB) relating dissolved-solids sources and transport in the 1991 water year to upstream catchment characteristics. The SPARROW model focused on geologic and agricultural sources of dissolved solids in the UCRB and was calibrated using water-year 1991 dissolved-solids loads from 218 monitoring sites. A new UCRB SPARROW model is planned that will update the investigation of dissolved-solids sources and transport in the basin to circa 2010 conditions and will improve upon the 2009 model by incorporating more detailed information about agricultural-irrigation and rangeland-management practices, among other improvements. Geospatial datasets relating to circa 2010 rangeland conditions are required for the new UCRB SPARROW modeling effort. This study compiled geospatial datasets for the UCRB that relate to the biotic alterations and rangeland conditions of grazing, fire and other land disturbance, and vegetation type and cover. Datasets representing abiotic alterations of access control (off-highway vehicles) and sediment generation and transport in general, were also compiled. These geospatial datasets may be tested in the upcoming SPARROW model to better understand the potential contribution of rangelands to dissolved-solids loading in UCRB streams.

Mutagenicity testing with transgenic mice. Part I: Comparison with the mouse bone marrow micronucleus test

PubMed Central

Wahnschaffe, U; Bitsch, A; Kielhorn, J; Mangelsdorf, I

2005-01-01

As part of a larger literature study on transgenic animals in mutagenicity testing, test results from the transgenic mutagenicity assays (lacI model; commercially available as the Big Blue® mouse, and the lacZ model; commercially available as the Muta™Mouse), were compared with the results on the same substances in the more traditional mouse bone marrow micronucleus test. 39 substances were found which had been tested in the micronucleus assay and in the above transgenic mouse systems. Although, the transgenic animal mutation assay is not directly comparable with the micronucleus test, because different genetic endpoints are examined: chromosome aberration versus gene mutation, the results for the majority of substances were in agreement. Both test systems, the transgenic mouse assay and the mouse bone marrow micronucleus test, have advantages and they complement each other. However, the transgenic animal assay has some distinct advantages over the micronucleus test: it is not restricted to one target organ and detects systemic as well as local mutagenic effects. PMID:15655069

In vivo characterization of a bigenic fluorescent mouse model of Alzheimer's disease with neurodegeneration.

PubMed

Crowe, Sarah E; Ellis-Davies, Graham C R

2013-07-01

The loss of cognitive function in Alzheimer's disease (AD) patients is strongly correlated with the loss of neurons in various regions of the brain. We have created a new fluorescent bigenic mouse model of AD by crossing "H-line" yellow fluorescent protein (YFP) mice with the 5xFAD mouse model, which we call the 5XY mouse model. The 5xFAD mouse has been shown to have significant loss of L5 pyramidal neurons by 12 months of age. These neurons are transgenically labeled with YFP in the 5XY mouse, which enable longitudinal imaging of structural changes. In the 5XY mice, we observed an appearance of axonal dystrophies, with two distinct morphologies in the early stages of the disease progression. Simple swelling dystrophies are transient in nature and are not directly associated with amyloid plaques. Rosette dystrophies are more complex structures that remained stable throughout all imaging sessions, and always surrounded an amyloid plaque. Plaque growth was followed over 4 weeks, and significant growth was seen between weekly imaging sessions. In addition to axonal dystrophy appearance and plaque growth, we were able to follow spine stability in 4-month old 5XY mice, which revealed no significant loss of spines. 5XY mice also showed a striking shrinkage of the neocortex at older ages (12-14 months). The 5XY mouse model may be a valuable tool for studying specific events in the degeneration of the neocortex, and may suggest new avenues for therapeutic intervention. Copyright © 2013 Wiley Periodicals, Inc.

Geospatial datasets for watershed delineation and characterization used in the Hawaii StreamStats web application

USGS Publications Warehouse

Rea, Alan; Skinner, Kenneth D.

2012-01-01

The U.S. Geological Survey Hawaii StreamStats application uses an integrated suite of raster and vector geospatial datasets to delineate and characterize watersheds. The geospatial datasets used to delineate and characterize watersheds on the StreamStats website, and the methods used to develop the datasets are described in this report. The datasets for Hawaii were derived primarily from 10 meter resolution National Elevation Dataset (NED) elevation models, and the National Hydrography Dataset (NHD), using a set of procedures designed to enforce the drainage pattern from the NHD into the NED, resulting in an integrated suite of elevation-derived datasets. Additional sources of data used for computing basin characteristics include precipitation, land cover, soil permeability, and elevation-derivative datasets. The report also includes links for metadata and downloads of the geospatial datasets.

A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection.

PubMed

Li, Jia; Xia, Changqun; Chen, Xiaowu

2017-10-12

Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos. From the user data, we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for videobased salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliencyguided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at the pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are constructed in an unsupervised manner that automatically infers a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. In experiments, the proposed unsupervised approach is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 imagebased classic (unsupervised or non-deep learning) models, six image-based deep learning models, and five video-based unsupervised models. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.

Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

NASA Astrophysics Data System (ADS)

Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

2017-12-01

Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

The ISLSCP initiative I global datasets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies

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

Sellers, P.J.; Collatz, J.; Koster, R.

1996-09-01

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`s land surface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

USGS Publications Warehouse

Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick

2014-01-01

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.

Technique Selectively Represses Immune System

MedlinePlus

... from attacking myelin in a mouse model of multiple sclerosis. Dr David Furness, Wellcome Images. All rights reserved ... devised a way to successfully treat symptoms resembling multiple sclerosis in a mouse model. With further development, the ...

Lipopolysaccharide-induced endotoxemia in corn oil-preloaded mice causes an extended course of lung injury and repair and pulmonary fibrosis: A translational mouse model of acute respiratory distress syndrome.

PubMed

Wu, Chaomin; Evans, Colin E; Dai, Zhiyu; Huang, Xiaojia; Zhang, Xianming; Jin, Hua; Hu, Guochang; Song, Yuanlin; Zhao, You-Yang

2017-01-01

Acute respiratory distress syndrome (ARDS) is characterized by acute hypoxemia respiratory failure, bilateral pulmonary infiltrates, and pulmonary edema of non-cardiac origin. Effective treatments for ARDS patients may arise from experimental studies with translational mouse models of this disease that aim to delineate the mechanisms underlying the disease pathogenesis. Mouse models of ARDS, however, can be limited by their rapid progression from injured to recovery state, which is in contrast to the course of ARDS in humans. Furthermore, current mouse models of ARDS do not recapitulate certain prominent aspects of the pathogenesis of ARDS in humans. In this study, we developed an improved endotoxemic mouse model of ARDS resembling many features of clinical ARDS including extended courses of injury and recovery as well as development of fibrosis following i.p. injection of lipopolysaccharide (LPS) to corn oil-preloaded mice. Compared with mice receiving LPS alone, those receiving corn oil and LPS exhibited extended course of lung injury and repair that occurred over a period of >2 weeks instead of 3-5days. Importantly, LPS challenge of corn oil-preloaded mice resulted in pulmonary fibrosis during the repair phase as often seen in ARDS patients. In summary, this simple novel mouse model of ARDS could represent a valuable experimental tool to elucidate mechanisms that regulate lung injury and repair in ARDS patients.

Endometrial apoptosis and neutrophil infiltration during menstruation exhibits spatial and temporal dynamics that are recapitulated in a mouse model.

PubMed

Armstrong, Gregory M; Maybin, Jacqueline A; Murray, Alison A; Nicol, Moira; Walker, Catherine; Saunders, Philippa T K; Rossi, Adriano G; Critchley, Hilary O D

2017-12-12

Menstruation is characterised by synchronous shedding and restoration of tissue integrity. An in vivo model of menstruation is required to investigate mechanisms responsible for regulation of menstrual physiology and to investigate common pathologies such as heavy menstrual bleeding (HMB). We hypothesised that our mouse model of simulated menstruation would recapitulate the spatial and temporal changes in the inflammatory microenvironment of human menses. Three regulatory events were investigated: cell death (apoptosis), neutrophil influx and cytokine/chemokine expression. Well-characterised endometrial tissues from women were compared with uteri from a mouse model (tissue recovered 0, 4, 8, 24 and 48 h after removal of a progesterone-secreting pellet). Immunohistochemistry for cleaved caspase-3 (CC3) revealed significantly increased staining in human endometrium from late secretory and menstrual phases. In mice, CC3 was significantly increased at 8 and 24 h post-progesterone-withdrawal. Elastase + human neutrophils were maximal during menstruation; Ly6G + mouse neutrophils were maximal at 24 h. Human endometrial and mouse uterine cytokine/chemokine mRNA concentrations were significantly increased during menstrual phase and 24 h post-progesterone-withdrawal respectively. Data from dated human samples revealed time-dependent changes in endometrial apoptosis preceding neutrophil influx and cytokine/chemokine induction during active menstruation. These dynamic changes were recapitulated in the mouse model of menstruation, validating its use in menstrual research.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data.

PubMed

Hettne, Kristina M; Boorsma, André; van Dartel, Dorien A M; Goeman, Jelle J; de Jong, Esther; Piersma, Aldert H; Stierum, Rob H; Kleinjans, Jos C; Kors, Jan A

2013-01-29

Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

PubMed Central

2013-01-01

Background Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles. Methods We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals. Results Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals. Conclusions Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect. PMID:23356878

The STR/ort mouse model of spontaneous osteoarthritis - an update.

PubMed

Staines, K A; Poulet, B; Wentworth, D N; Pitsillides, A A

2017-06-01

Osteoarthritis is a degenerative joint disease and a world-wide healthcare burden. Characterized by cartilage degradation, subchondral bone thickening and osteophyte formation, osteoarthritis inflicts much pain and suffering, for which there are currently no disease-modifying treatments available. Mouse models of osteoarthritis are proving critical in advancing our understanding of the underpinning molecular mechanisms. The STR/ort mouse is a well-recognized model which develops a natural form of osteoarthritis very similar to the human disease. In this Review we discuss the use of the STR/ort mouse in understanding this multifactorial disease with an emphasis on recent advances in its genetics and its bone, endochondral and immune phenotypes. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease.

PubMed

Lin, Lan; Fu, Zhenrong; Xu, Xiaoting; Wu, Shuicai

2015-05-01

Over the past two decades, various Alzheimer's disease (AD) trangenetic mice models harboring genes with mutation known to cause familial AD have been created. Today, high-resolution magnetic resonance microscopy (MRM) technology is being widely used in the study of AD mouse models. It has greatly facilitated and advanced our knowledge of AD. In this review, most of the attention is paid to fundamental of MRM, the construction of standard mouse MRM brain template and atlas, the detection of amyloid plaques, following up on brain atrophy and the future applications of MRM in transgenic AD mice. It is believed that future testing of potential drugs in mouse models with MRM will greatly improve the predictability of drug effect in preclinical trials. © 2015 Wiley Periodicals, Inc.

Detection of drug active ingredients by chemometric processing of solid-state NMR spectrometry data -- the case of acetaminophen.

PubMed

Paradowska, Katarzyna; Jamróz, Marta Katarzyna; Kobyłka, Mariola; Gowin, Ewelina; Maczka, Paulina; Skibiński, Robert; Komsta, Łukasz

2012-01-01

This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

PubMed

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Better Utilization of Mouse Models of Neurodegenerative Diseases in Preclinical Studies: From the Bench to the Clinic.

PubMed

Janus, Christopher; Hernandez, Carolina; deLelys, Victoria; Roder, Hanno; Welzl, Hans

2016-01-01

The major symptom of Alzheimer's disease is dementia progressing with age. Its clinical diagnosis is preceded by a long prodromal period of brain pathology that encompasses both formation of extracellular amyloid and intraneuronal tau deposits in the brain and widespread neuronal death. At present, familial cases of dementia provide the most promising foundation for modeling neurodegenerative tauopathies, a group of heterogeneous disorders characterized by prominent intracellular accumulation of hyperphosphorylated tau protein. In this chapter, we describe major behavioral hallmarks of tauopathies, briefly outline the genetics underlying familial cases, and discuss the arising implications for modeling the disease in transgenic mouse systems. The selection of tests performed to evaluate the phenotype of a model should be guided by the key behavioral hallmarks that characterize human disorder and their homology to mouse cognitive systems. We attempt to provide general guidelines and establish criteria for modeling dementia in a mouse; however, interpretations of obtained results should avoid a reductionist "one gene, one disease" explanation of model characteristics. Rather, the focus should be directed to the question of how the mouse genome can cope with the over-expression of the protein coded by transgene(s). While each model is valuable within its own constraints and the experiments performed are guided by specific hypotheses, we seek to expand upon their methodology by offering guidance spanning from issues of mouse husbandry to choices of behavioral tests and routes of drug administration that might increase the external validity of studies and consequently optimize the translational aspect of preclinical research.

Rodent models of congenital and hereditary cataract in man.

PubMed

Tripathi, B J; Tripathi, R C; Borisuth, N S; Dhaliwal, R; Dhaliwal, D

1991-01-01

Because the organogenesis and physiology of the lens are essentially similar in various mammals, an understanding of the etiology and pathogenesis of the formation of cataract in an animal model will enhance our knowledge of cataractogenesis in man. In this review, we summarize the background, etiology, and pathogenesis of cataracts that occur in rodents. The main advantages of using rodent mutants include the well-researched genetics of the animals and the comparative ease of breeding of large litters. Numerous rodent models of congenital and hereditary cataracts have been studied extensively. In mice, the models include the Cts strain, Fraser mouse, lens opacity gene (Lop) strain, Lop-2 and Lop-3 strains, Philly mouse, Nakano mouse, Nop strain, Deer mouse, Emory mouse, Swiss Webster strain, Balb/c-nct/nct mouse, and SAM-R/3 strain. The rat models include BUdR, ICR, Sprague-Dawley, and Wistar rats, the spontaneously hypertensive rat (SHR), the John Rapp inbred strain of Dahl salt-sensitive rat, as well as WBN/Kob, Royal College of Surgeons (RCS), and Brown-Norway rats. Other proposed models for the study of hereditary cataract include the degu and the guinea pig. Because of the ease of making clinical observations in vivo and the subsequent availability of the intact lens for laboratory analyses at different stages of cataract formation, these animals provide excellent models for clinicopathologic correlations, for monitoring of the natural history of the aging process and of metabolic defects, as well as for investigations on the effect of cataract-modulating agents and drugs, including the prospect of gene therapy.

Consensus Modeling of Oral Rat Acute Toxicity

EPA Science Inventory

An acute toxicity dataset (oral rat LD50) with about 7400 compounds was compiled from the ChemIDplus database. This dataset was divided into a modeling set and a prediction set. The compounds in the prediction set were selected so that they were present in the modeling set used...

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

PubMed Central

Monk, Jacquomo; Ierodiaconou, Daniel; Harvey, Euan; Rattray, Alex; Versace, Vincent L.

2012-01-01

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions. PMID:22536325

LOD 1 VS. LOD 2 - Preliminary Investigations Into Differences in Mobile Rendering Performance

NASA Astrophysics Data System (ADS)

Ellul, C.; Altenbuchner, J.

2013-09-01

The increasing availability, size and detail of 3D City Model datasets has led to a challenge when rendering such data on mobile devices. Understanding the limitations to the usability of such models on these devices is particularly important given the broadening range of applications - such as pollution or noise modelling, tourism, planning, solar potential - for which these datasets and resulting visualisations can be utilized. Much 3D City Model data is created by extrusion of 2D topographic datasets, resulting in what is known as Level of Detail (LoD) 1 buildings - with flat roofs. However, in the UK the National Mapping Agency (the Ordnance Survey, OS) is now releasing test datasets to Level of Detail (LoD) 2 - i.e. including roof structures. These datasets are designed to integrate with the LoD 1 datasets provided by the OS, and provide additional detail in particular on larger buildings and in town centres. The availability of such integrated datasets at two different Levels of Detail permits investigation into the impact of the additional roof structures (and hence the display of a more realistic 3D City Model) on rendering performance on a mobile device. This paper describes preliminary work carried out to investigate this issue, for the test area of the city of Sheffield (in the UK Midlands). The data is stored in a 3D spatial database as triangles and then extracted and served as a web-based data stream which is queried by an App developed on the mobile device (using the Android environment, Java and OpenGL for graphics). Initial tests have been carried out on two dataset sizes, for the city centre and a larger area, rendering the data onto a tablet to compare results. Results of 52 seconds for rendering LoD 1 data, and 72 seconds for LoD 1 mixed with LoD 2 data, show that the impact of LoD 2 is significant.

Mouse Genome Informatics (MGI) Is the International Resource for Information on the Laboratory Mouse.

PubMed

Law, MeiYee; Shaw, David R

2018-01-01

Mouse Genome Informatics (MGI, http://www.informatics.jax.org/ ) web resources provide free access to meticulously curated information about the laboratory mouse. MGI's primary goal is to help researchers investigate the genetic foundations of human diseases by translating information from mouse phenotypes and disease models studies to human systems. MGI provides comprehensive phenotypes for over 50,000 mutant alleles in mice and provides experimental model descriptions for over 1500 human diseases. Curated data from scientific publications are integrated with those from high-throughput phenotyping and gene expression centers. Data are standardized using defined, hierarchical vocabularies such as the Mammalian Phenotype (MP) Ontology, Mouse Developmental Anatomy and the Gene Ontologies (GO). This chapter introduces you to Gene and Allele Detail pages and provides step-by-step instructions for simple searches and those that take advantage of the breadth of MGI data integration.

Differential biological effects of dehydroepiandrosterone (DHEA) between mouse (B16F10) and human melanoma (BLM) cell lines.

PubMed

Joshi, Kumud; Hassan, Sherif S; Ramaraj, Pandurangan

2017-01-01

Dehydroepiandrosterone (DHEA) is a weak androgen and had been shown to have anti-cancer, anti-adipogenic and anti-inflammatory effects on mouse and other rodent models, but not on humans, suggesting a systemic level difference between mouse and human. Our previous study on DHEA biological functions involving a variety of cell lines, suggested that the functional differences between mouse and human existed even at the cellular level. Hence, using mouse and human melanoma cell models, in-vitro effects of DHEA on cell growth, mechanism of cell death and mechanism of DHEA action were studied. Results indicated a differential biological effects of DHEA between mouse and human melanoma cell lines. These in-vitro studies also suggested that the differential biological effects observed between these two cell lines could be due to the difference in the way DHEA was processed or metabolized inside the cell.

Current State of Animal (Mouse) Modeling in Melanoma Research.

PubMed

Kuzu, Omer F; Nguyen, Felix D; Noory, Mohammad A; Sharma, Arati

2015-01-01

Despite the considerable progress in understanding the biology of human cancer and technological advancement in drug discovery, treatment failure remains an inevitable outcome for most cancer patients with advanced diseases, including melanoma. Despite FDA-approved BRAF-targeted therapies for advanced stage melanoma showed a great deal of promise, development of rapid resistance limits the success. Hence, the overall success rate of melanoma therapy still remains to be one of the worst compared to other malignancies. Advancement of next-generation sequencing technology allowed better identification of alterations that trigger melanoma development. As development of successful therapies strongly depends on clinically relevant preclinical models, together with the new findings, more advanced melanoma models have been generated. In this article, besides traditional mouse models of melanoma, we will discuss recent ones, such as patient-derived tumor xenografts, topically inducible BRAF mouse model and RCAS/TVA-based model, and their advantages as well as limitations. Although mouse models of melanoma are often criticized as poor predictors of whether an experimental drug would be an effective treatment, development of new and more relevant models could circumvent this problem in the near future.

Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models

PubMed Central

Stephens, Zachary D.; Hudson, Matthew E.; Mainzer, Liudmila S.; Taschuk, Morgan; Weber, Matthew R.; Iyer, Ravishankar K.

2016-01-01

An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the “ground truth” about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as well as sequencing artifacts. Read simulators can fulfill this requirement, but are often criticized for limited resemblance to true data and overall inflexibility. We present NEAT (NExt-generation sequencing Analysis Toolkit), a set of tools that not only includes an easy-to-use read simulator, but also scripts to facilitate variant comparison and tool evaluation. NEAT has a wide variety of tunable parameters which can be set manually on the default model or parameterized using real datasets. The software is freely available at github.com/zstephens/neat-genreads. PMID:27893777

Identification of novel mRNAs and lncRNAs associated with mouse experimental colitis and human inflammatory bowel disease.

PubMed

Rankin, Carl Robert; Theodorou, Evangelos; Law, Ivy Ka Man; Rowe, Lorraine; Kokkotou, Efi; Pekow, Joel; Wang, Jiafang; Martin, Martin G; Pothoulakis, Charalabos; Padua, David Miguel

2018-06-28

Inflammatory bowel disease (IBD) is a complex disorder that is associated with significant morbidity. While many recent advances have been made with new diagnostic and therapeutic tools, a deeper understanding of its basic pathophysiology is needed to continue this trend towards improving treatments. By utilizing an unbiased, high-throughput transcriptomic analysis of two well-established mouse models of colitis, we set out to uncover novel coding and non-coding RNAs that are differentially expressed in the setting of colonic inflammation. RNA-seq analysis was performed using colonic tissue from two mouse models of colitis, a dextran sodium sulfate induced model and a genetic-induced model in mice lacking IL-10. We identified 81 coding RNAs that were commonly altered in both experimental models. Of these coding RNAs, 12 of the human orthologs were differentially expressed in a transcriptomic analysis of IBD patients. Interestingly, 5 of the 12 of human differentially expressed genes have not been previously identified as IBD-associated genes, including ubiquitin D. Our analysis also identified 15 non-coding RNAs that were differentially expressed in either mouse model. Surprisingly, only three non-coding RNAs were commonly dysregulated in both of these models. The discovery of these new coding and non-coding RNAs expands our transcriptional knowledge of mouse models of IBD and offers additional targets to deepen our understanding of the pathophysiology of IBD.

Application of global datasets for hydrological modelling of a remote, snowmelt driven catchment in the Canadian Sub-Arctic

NASA Astrophysics Data System (ADS)

Casson, David; Werner, Micha; Weerts, Albrecht; Schellekens, Jaap; Solomatine, Dimitri

2017-04-01

Hydrological modelling in the Canadian Sub-Arctic is hindered by the limited spatial and temporal coverage of local meteorological data. Local watershed modelling often relies on data from a sparse network of meteorological stations with a rough density of 3 active stations per 100,000 km2. Global datasets hold great promise for application due to more comprehensive spatial and extended temporal coverage. A key objective of this study is to demonstrate the application of global datasets and data assimilation techniques for hydrological modelling of a data sparse, Sub-Arctic watershed. Application of available datasets and modelling techniques is currently limited in practice due to a lack of local capacity and understanding of available tools. Due to the importance of snow processes in the region, this study also aims to evaluate the performance of global SWE products for snowpack modelling. The Snare Watershed is a 13,300 km2 snowmelt driven sub-basin of the Mackenzie River Basin, Northwest Territories, Canada. The Snare watershed is data sparse in terms of meteorological data, but is well gauged with consistent discharge records since the late 1970s. End of winter snowpack surveys have been conducted every year from 1978-present. The application of global re-analysis datasets from the EU FP7 eartH2Observe project are investigated in this study. Precipitation data are taken from Multi-Source Weighted-Ensemble Precipitation (MSWEP) and temperature data from Watch Forcing Data applied to European Reanalysis (ERA)-Interim data (WFDEI). GlobSnow-2 is a global Snow Water Equivalent (SWE) measurement product funded by the European Space Agency (ESA) and is also evaluated over the local watershed. Downscaled precipitation, temperature and potential evaporation datasets are used as forcing data in a distributed version of the HBV model implemented in the WFLOW framework. Results demonstrate the successful application of global datasets in local watershed modelling, but that validation of actual frozen precipitation and snowpack conditions is very difficult. The distributed hydrological model shows good streamflow simulation performance based on statistical model evaluation techniques. Results are also promising for inter-annual variability, spring snowmelt onset and time to peak flows. It is expected that data assimilation of stream flow using an Ensemble Kalman Filter will further improve model performance. This study shows that global re-analysis datasets hold great potential for understanding the hydrology and snowpack dynamics of the expansive and data sparse sub-Arctic. However, global SWE products will require further validation and algorithm improvements, particularly over boreal forest and lake-rich regions.

So many genes, so little time: A practical approach to divergence-time estimation in the genomic era

PubMed Central

2018-01-01

Phylogenomic datasets have been successfully used to address questions involving evolutionary relationships, patterns of genome structure, signatures of selection, and gene and genome duplications. However, despite the recent explosion in genomic and transcriptomic data, the utility of these data sources for efficient divergence-time inference remains unexamined. Phylogenomic datasets pose two distinct problems for divergence-time estimation: (i) the volume of data makes inference of the entire dataset intractable, and (ii) the extent of underlying topological and rate heterogeneity across genes makes model mis-specification a real concern. “Gene shopping”, wherein a phylogenomic dataset is winnowed to a set of genes with desirable properties, represents an alternative approach that holds promise in alleviating these issues. We implemented an approach for phylogenomic datasets (available in SortaDate) that filters genes by three criteria: (i) clock-likeness, (ii) reasonable tree length (i.e., discernible information content), and (iii) least topological conflict with a focal species tree (presumed to have already been inferred). Such a winnowing procedure ensures that errors associated with model (both clock and topology) mis-specification are minimized, therefore reducing error in divergence-time estimation. We demonstrated the efficacy of this approach through simulation and applied it to published animal (Aves, Diplopoda, and Hymenoptera) and plant (carnivorous Caryophyllales, broad Caryophyllales, and Vitales) phylogenomic datasets. By quantifying rate heterogeneity across both genes and lineages we found that every empirical dataset examined included genes with clock-like, or nearly clock-like, behavior. Moreover, many datasets had genes that were clock-like, exhibited reasonable evolutionary rates, and were mostly compatible with the species tree. We identified overlap in age estimates when analyzing these filtered genes under strict clock and uncorrelated lognormal (UCLN) models. However, this overlap was often due to imprecise estimates from the UCLN model. We find that “gene shopping” can be an efficient approach to divergence-time inference for phylogenomic datasets that may otherwise be characterized by extensive gene tree heterogeneity. PMID:29772020

A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study

PubMed Central

Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo

2015-01-01

Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease. PMID:26106884

Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI.

PubMed

Kim, Ki Hwan; Park, Sung-Hong

2017-04-01

The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small. The purpose of this study is to develop a learning-based model to combine the multiple phase-cycled bSSFP datasets for better banding artifact suppression. Multilayer perceptron (MLP) is a feedforward artificial neural network consisting of three layers of input, hidden, and output layers. MLP models were trained by input bSSFP datasets acquired from human brain and knee at 3T, which were separately performed for two and four PC angles. Banding-free bSSFP images were generated by maximum-intensity projection (MIP) of 8 or 12 phase-cycled datasets and were used as targets for training the output layer. The trained MLP models were applied to another brain and knee datasets acquired with different scan parameters and also to multiple phase-cycled bSSFP functional MRI datasets acquired on rat brain at 9.4T, in comparison with the conventional MIP method. Simulations were also performed to validate the MLP approach. Both the simulations and human experiments demonstrated that MLP suppressed banding artifacts significantly, superior to MIP in both banding artifact suppression and SNR efficiency. MLP demonstrated superior performance over MIP for the 9.4T fMRI data as well, which was not used for training the models, while visually preserving the fMRI maps very well. Artificial neural network is a promising technique for combining multiple phase-cycled bSSFP datasets for banding artifact suppression. Copyright © 2016 Elsevier Inc. All rights reserved.

So many genes, so little time: A practical approach to divergence-time estimation in the genomic era.

PubMed

Smith, Stephen A; Brown, Joseph W; Walker, Joseph F

2018-01-01

Phylogenomic datasets have been successfully used to address questions involving evolutionary relationships, patterns of genome structure, signatures of selection, and gene and genome duplications. However, despite the recent explosion in genomic and transcriptomic data, the utility of these data sources for efficient divergence-time inference remains unexamined. Phylogenomic datasets pose two distinct problems for divergence-time estimation: (i) the volume of data makes inference of the entire dataset intractable, and (ii) the extent of underlying topological and rate heterogeneity across genes makes model mis-specification a real concern. "Gene shopping", wherein a phylogenomic dataset is winnowed to a set of genes with desirable properties, represents an alternative approach that holds promise in alleviating these issues. We implemented an approach for phylogenomic datasets (available in SortaDate) that filters genes by three criteria: (i) clock-likeness, (ii) reasonable tree length (i.e., discernible information content), and (iii) least topological conflict with a focal species tree (presumed to have already been inferred). Such a winnowing procedure ensures that errors associated with model (both clock and topology) mis-specification are minimized, therefore reducing error in divergence-time estimation. We demonstrated the efficacy of this approach through simulation and applied it to published animal (Aves, Diplopoda, and Hymenoptera) and plant (carnivorous Caryophyllales, broad Caryophyllales, and Vitales) phylogenomic datasets. By quantifying rate heterogeneity across both genes and lineages we found that every empirical dataset examined included genes with clock-like, or nearly clock-like, behavior. Moreover, many datasets had genes that were clock-like, exhibited reasonable evolutionary rates, and were mostly compatible with the species tree. We identified overlap in age estimates when analyzing these filtered genes under strict clock and uncorrelated lognormal (UCLN) models. However, this overlap was often due to imprecise estimates from the UCLN model. We find that "gene shopping" can be an efficient approach to divergence-time inference for phylogenomic datasets that may otherwise be characterized by extensive gene tree heterogeneity.

Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2012-01-01

Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model's assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models (M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects.

A Dynamic Simulation of Musculoskeletal Function in the Mouse Hindlimb During Trotting Locomotion

PubMed Central

Charles, James P.; Cappellari, Ornella; Hutchinson, John R.

2018-01-01

Mice are often used as animal models of various human neuromuscular diseases, and analysis of these models often requires detailed gait analysis. However, little is known of the dynamics of the mouse musculoskeletal system during locomotion. In this study, we used computer optimization procedures to create a simulation of trotting in a mouse, using a previously developed mouse hindlimb musculoskeletal model in conjunction with new experimental data, allowing muscle forces, activation patterns, and levels of mechanical work to be estimated. Analyzing musculotendon unit (MTU) mechanical work throughout the stride allowed a deeper understanding of their respective functions, with the rectus femoris MTU dominating the generation of positive and negative mechanical work during the swing and stance phases. This analysis also tested previous functional inferences of the mouse hindlimb made from anatomical data alone, such as the existence of a proximo-distal gradient of muscle function, thought to reflect adaptations for energy-efficient locomotion. The results do not strongly support the presence of this gradient within the mouse musculoskeletal system, particularly given relatively high negative net work output from the ankle plantarflexor MTUs, although more detailed simulations could test this further. This modeling analysis lays a foundation for future studies of the control of vertebrate movement through the development of neuromechanical simulations. PMID:29868576

Substituting values for censored data from Texas, USA, reservoirs inflated and obscured trends in analyses commonly used for water quality target development.

PubMed

Grantz, Erin; Haggard, Brian; Scott, J Thad

2018-06-12

We calculated four median datasets (chlorophyll a, Chl a; total phosphorus, TP; and transparency) using multiple approaches to handling censored observations, including substituting fractions of the quantification limit (QL; dataset 1 = 1QL, dataset 2 = 0.5QL) and statistical methods for censored datasets (datasets 3-4) for approximately 100 Texas, USA reservoirs. Trend analyses of differences between dataset 1 and 3 medians indicated percent difference increased linearly above thresholds in percent censored data (%Cen). This relationship was extrapolated to estimate medians for site-parameter combinations with %Cen > 80%, which were combined with dataset 3 as dataset 4. Changepoint analysis of Chl a- and transparency-TP relationships indicated threshold differences up to 50% between datasets. Recursive analysis identified secondary thresholds in dataset 4. Threshold differences show that information introduced via substitution or missing due to limitations of statistical methods biased values, underestimated error, and inflated the strength of TP thresholds identified in datasets 1-3. Analysis of covariance identified differences in linear regression models relating transparency-TP between datasets 1, 2, and the more statistically robust datasets 3-4. Study findings identify high-risk scenarios for biased analytical outcomes when using substitution. These include high probability of median overestimation when %Cen > 50-60% for a single QL, or when %Cen is as low 16% for multiple QL's. Changepoint analysis was uniquely vulnerable to substitution effects when using medians from sites with %Cen > 50%. Linear regression analysis was less sensitive to substitution and missing data effects, but differences in model parameters for transparency cannot be discounted and could be magnified by log-transformation of the variables.

Additive interactions between 1-methyl-1,2,3,4-tetrahydroisoquinoline and clobazam in the mouse maximal electroshock-induced tonic seizure model--an isobolographic analysis for parallel dose-response relationship curves.

PubMed

Andres-Mach, Marta; Haratym-Maj, Agnieszka; Zagaja, Mirosław; Luszczki, Jarogniew J

2014-01-01

The aim of this study was to characterize the anticonvulsant effect of 1-methyl-1,2,3,4-tetrahydroisoquinoline (1-MeTHIQ) in combination with clobazam (CLB) in the mouse maximal electroshock-induced seizure (MES) model. The anticonvulsant interaction profile between 1-MeTHIQ and CLB in the mouse MES model was determined using an isobolographic analysis for parallel dose-response relationship curves. Electroconvulsions were produced in albino Swiss mice by a current (sine wave, 25 mA, 500 V, 50 Hz, 0.2-second stimulus duration) delivered via auricular electrodes by a Hugo Sachs generator. There was an additive effect of the combination of 1-MeTHIQ with CLB (at the fixed ratios of 1:3, 1:1 and 3:1) in the mouse MES-induced tonic seizure model. The additive interaction of the combination of 1-MeTHIQ with CLB (at fixed-ratios of 1:3, 1:1 and 3:1) in the mouse MES model seems to be pharmacodynamic in nature and worth of considering in further clinical practice. © 2014 S. Karger AG, Basel.

Improvements and Limitations of Humanized Mouse Models for HIV Research: NIH/NIAID “Meet the Experts” 2015 Workshop Summary

PubMed Central

Akkina, Ramesh; Allam, Atef; Balazs, Alejandro B.; Blankson, Joel N.; Burnett, John C.; Casares, Sofia; Garcia, J. Victor; Hasenkrug, Kim J.; Kitchen, Scott G.; Klein, Florian; Kumar, Priti; Luster, Andrew D.; Poluektova, Larisa Y.; Rao, Mangala; Shultz, Leonard D.; Zack, Jerome A.

2016-01-01

Abstract The number of humanized mouse models for the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) and other infectious diseases has expanded rapidly over the past 8 years. Highly immunodeficient mouse strains, such as NOD/SCID/gamma chainnull (NSG, NOG), support better human hematopoietic cell engraftment. Another improvement is the derivation of highly immunodeficient mice, transgenic with human leukocyte antigens (HLAs) and cytokines that supported development of HLA-restricted human T cells and heightened human myeloid cell engraftment. Humanized mice are also used to study the HIV reservoir using new imaging techniques. Despite these advances, there are still limitations in HIV immune responses and deficits in lymphoid structures in these models in addition to xenogeneic graft-versus-host responses. To understand and disseminate the improvements and limitations of humanized mouse models to the scientific community, the NIH sponsored and convened a meeting on April 15, 2015 to discuss the state of knowledge concerning these questions and best practices for selecting a humanized mouse model for a particular scientific investigation. This report summarizes the findings of the NIH meeting. PMID:26670361

Behavioral assays with mouse models of Alzheimer’s disease: practical considerations and guidelines

PubMed Central

Puzzo, Daniela; Lee, Linda; Palmeri, Agostino; Calabrese, Giorgio; Arancio, Ottavio

2014-01-01

In Alzheimer’s disease (AD) basic research and drug discovery, mouse models are essential resources for uncovering biological mechanisms, validating molecular targets and screening potential compounds. Both transgenic and non-genetically modified mouse models enable access to different types of AD-like pathology in vivo. Although there is a wealth of genetic and biochemical studies on proposed AD pathogenic pathways, as a disease that centrally features cognitive failure, the ultimate readout for any interventions should be measures of learning and memory. This is particularly important given the lack of knowledge on disease etiology – assessment by cognitive assays offers the advantage of targeting relevant memory systems without requiring assumptions about pathogenesis. A multitude of behavioral assays are available for assessing cognitive functioning in mouse models, including ones specific for hippocampal-dependent learning and memory. Here we review the basics of available transgenic and non-transgenic AD mouse models and detail three well-established behavioral tasks commonly used for testing hippocampal-dependent cognition in mice – contextual fear conditioning, radial arm water maze and Morris water maze. In particular, we discuss the practical considerations, requirements and caveats of these behavioral testing paradigms. PMID:24462904

Icotinib inhibits EGFR signaling and alleviates psoriasis-like symptoms in animal models.

PubMed

Tan, Fenlai; Yang, Guiqun; Wang, Yanping; Chen, Haibo; Yu, Bo; Li, He; Guo, Jing; Huang, Xiaoling; Deng, Yifang; Yu, Pengxia; Ding, Lieming

2018-02-01

To investigate the effects of icotinib hydrochloride and a derivative cream on epidermal growth factor receptor (EGFR) signaling and within animal psoriasis models, respectively. The effect of icotinib on EGFR signaling was examined in HaCaT cells, while its effect on angiogenesis was tested in chick embryo chorioallantoic membranes (CAM). The effectiveness of icotinib in treating psoriasis was tested in three psoriasis models, including diethylstilbestrol-treated mouse vaginal epithelial cells, mouse tail granular cell layer formation, and propranolol-induced psoriasis-like features in guinea pig ear skin. Icotinib treatment blocked EGFR signaling and reduced HaCaT cell viability as well as suppressed CAM angiogenesis. Topical application of icotinib ameliorated psoriasis-like histological characteristics in mouse and guinea pig psoriasis models. Icotinib also significantly inhibited mouse vaginal epithelium mitosis, promoted mouse tail squamous epidermal granular layer formation, and reduced the thickness of the horny layer in propranolol treated auricular dorsal surface of guinea pig. We conclude that icotinib can effectively inhibit psoriasis in animal models. Future clinical studies should be conducted to explore the therapeutic effects of icotinb in humans. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Pathogenicity of swine influenza viruses possessing an avian or swine-origin PB2 polymerase gene evaluated in mouse and pig models.

PubMed

Ma, Wenjun; Lager, Kelly M; Li, Xi; Janke, Bruce H; Mosier, Derek A; Painter, Laura E; Ulery, Eva S; Ma, Jingqun; Lekcharoensuk, Porntippa; Webby, Richard J; Richt, Jürgen A

2011-02-05

PB2 627K is a determinant of influenza host range and contributes to the pathogenicity of human-, avian-, and mouse-adapted influenza viruses in the mouse model. Here we used mouse and pig models to analyze the contribution of a swine-origin and avian-origin PB2 carrying either 627K or 627E in the background of the classical swine H1N1 (A/Swine/Iowa/15/30; 1930) virus. The results showed PB2 627K is crucial for virulence in the mouse model, independent of whether PB2 is derived from an avian or swine influenza virus (SIV). In the pig model, PB2 627E decreases pathogenicity of the classical 1930 SIV when it contains the swine-origin PB2, but not when it possesses the avian-origin PB2. Our study suggests the pathogenicity of SIVs with different PB2 genes and mutation of codon 627 in mice does not correlate with the pathogenicity of the same SIVs in the natural host, the pig. Copyright © 2010 Elsevier Inc. All rights reserved.

An Immunocompetent Mouse Model of Zika Virus Infection.

PubMed

Gorman, Matthew J; Caine, Elizabeth A; Zaitsev, Konstantin; Begley, Matthew C; Weger-Lucarelli, James; Uccellini, Melissa B; Tripathi, Shashank; Morrison, Juliet; Yount, Boyd L; Dinnon, Kenneth H; Rückert, Claudia; Young, Michael C; Zhu, Zhe; Robertson, Shelly J; McNally, Kristin L; Ye, Jing; Cao, Bin; Mysorekar, Indira U; Ebel, Gregory D; Baric, Ralph S; Best, Sonja M; Artyomov, Maxim N; Garcia-Sastre, Adolfo; Diamond, Michael S

2018-05-09

Progress toward understanding Zika virus (ZIKV) pathogenesis is hindered by lack of immunocompetent small animal models, in part because ZIKV fails to effectively antagonize Stat2-dependent interferon (IFN) responses in mice. To address this limitation, we first passaged an African ZIKV strain (ZIKV-Dak-41525) through Rag1 -/- mice to obtain a mouse-adapted virus (ZIKV-Dak-MA) that was more virulent than ZIKV-Dak-41525 in mice treated with an anti-Ifnar1 antibody. A G18R substitution in NS4B was the genetic basis for the increased replication, and resulted in decreased IFN-β production, diminished IFN-stimulated gene expression, and the greater brain infection observed with ZIKV-Dak-MA. To generate a fully immunocompetent mouse model of ZIKV infection, human STAT2 was introduced into the mouse Stat2 locus (hSTAT2 KI). Subcutaneous inoculation of pregnant hSTAT2 KI mice with ZIKV-Dak-MA resulted in spread to the placenta and fetal brain. An immunocompetent mouse model of ZIKV infection may prove valuable for evaluating countermeasures to limit disease. Copyright © 2018 Elsevier Inc. All rights reserved.

Histologic scoring of gastritis and gastric cancer in mouse models.

PubMed

Rogers, Arlin B

2012-01-01

Histopathology is a defining endpoint in mouse models of experimental gastritis and gastric adenocarcinoma. Presented here is an overview of the histology of gastritis and gastric cancer in mice experimentally infected with Helicobacter pylori or H. felis. A modular histopathologic scoring scheme is provided that incorporates relevant disease-associated changes. Whereas the guide uses Helicobacter infection as the prototype challenge, features may be applied to chemical and genetically engineered mouse models of stomach cancer as well. Specific criteria included in the combined gastric histologic activity index (HAI) include inflammation, epithelial defects, oxyntic atrophy, hyperplasia, pseudopyloric metaplasia, and dysplasia or neoplasia. Representative photomicrographs accompany descriptions for each lesion grade. Differentiation of genuine tumor invasion from pseudoinvasion is highlighted. A brief comparison of normal rodent versus human stomach anatomy and physiology is accompanied by an introduction to mouse-specific lesions including mucous metaplasia and eosinophilic droplets (hyalinosis). In conjunction with qualified pathology support, this guide is intended to assist research scientists, postdoctoral fellows, graduate students, and medical professionals from affiliated disciplines in the interpretation and histologic grading of chronic gastritis and gastric carcinoma in mouse models.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

DOE PAGES

Slavinskaya, N. A.; Abbasi, M.; Starcke, J. H.; ...

2017-01-24

An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model–data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. Here, the initial H 2/CO reaction model, assembled frommore » 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.« less

Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

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

Slavinskaya, N. A.; Abbasi, M.; Starcke, J. H.

An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model–data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. Here, the initial H 2/CO reaction model, assembled frommore » 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.« less

Astonishing advances in mouse genetic tools for biomedical research.

PubMed

Kaczmarczyk, Lech; Jackson, Walker S

2015-01-01

The humble house mouse has long been a workhorse model system in biomedical research. The technology for introducing site-specific genome modifications led to Nobel Prizes for its pioneers and opened a new era of mouse genetics. However, this technology was very time-consuming and technically demanding. As a result, many investigators continued to employ easier genome manipulation methods, though resulting models can suffer from overlooked or underestimated consequences. Another breakthrough, invaluable for the molecular dissection of disease mechanisms, was the invention of high-throughput methods to measure the expression of a plethora of genes in parallel. However, the use of samples containing material from multiple cell types could obfuscate data, and thus interpretations. In this review we highlight some important issues in experimental approaches using mouse models for biomedical research. We then discuss recent technological advances in mouse genetics that are revolutionising human disease research. Mouse genomes are now easily manipulated at precise locations thanks to guided endonucleases, such as transcription activator-like effector nucleases (TALENs) or the CRISPR/Cas9 system, both also having the potential to turn the dream of human gene therapy into reality. Newly developed methods of cell type-specific isolation of transcriptomes from crude tissue homogenates, followed by detection with next generation sequencing (NGS), are vastly improving gene regulation studies. Taken together, these amazing tools simplify the creation of much more accurate mouse models of human disease, and enable the extraction of hitherto unobtainable data.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Abul Ehsan Bhuiyan, Md; Nikolopoulos, Efthymios I.; Anagnostou, Emmanouil N.; Quintana-Seguí, Pere; Barella-Ortiz, Anaïs

2018-02-01

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean.

Mutational landscape of a chemically-induced mouse model of liver cancer.

PubMed

Connor, Frances; Rayner, Tim F; Aitken, Sarah J; Feig, Christine; Lukk, Margus; Santoyo-Lopez, Javier; Odom, Duncan T

2018-06-26

Carcinogen-induced mouse models of liver cancer are used extensively to study pathogenesis of the disease and have a critical role in validating candidate therapeutics. These models can recapitulate molecular and histological features of human disease. However, it is not known if the genomic alterations driving these mouse tumour genomes are comparable to those found in human tumours. Here, we provide a detailed genomic characterisation of tumours from a commonly used mouse model of hepatocellular carcinoma (HCC). We analysed whole exome sequences of liver tumours arising in mice exposed to diethylnitrosamine (DEN). DEN-initiated tumours had a high, uniform number of somatic single nucleotide variants (SNVs), with few insertions, deletions or copy number alterations, consistent with the known genotoxic action of DEN. Exposure of hepatocytes to DEN left a reproducible mutational imprint in resulting tumour exomes which we could computationally reconstruct using six known COSMIC mutational signatures. The tumours carried a high diversity of low-incidence, non-synonymous point mutations in many oncogenes and tumour suppressors, reflecting the stochastic introduction of SNVs into the hepatocyte genome by the carcinogen. We identified four recurrently mutated genes that were putative oncogenic drivers of HCC in this model. Every neoplasm carried activating hotspot mutations either in codon 61 of Hras, in codon 584 of Braf or in codon 254 of Egfr. Truncating mutations of Apc occurred in 21% of neoplasms, which were exclusively carcinomas supporting a role for deregulation of Wnt/β-catenin signalling in cancer progression. Our study provides detailed insight into the mutational landscape of tumours arising in a commonly-used carcinogen model of HCC, facilitating the future use of this model to understand the human disease. Mouse models are widely used to study the biology of cancer and to test potential therapies. Here, we have described the mutational landscape of tumours arising in a carcinogen-induced mouse model of liver cancer. Since cancer is a disease caused by genomic alterations, information about the patterns and types of mutations in the tumours in this mouse model should facilitate its use to study human liver cancer. Copyright © 2018 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

PubMed Central

2012-01-01

Background ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles. Results We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately. Conclusions iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB. PMID:23194258

Chip Based Magnetic Imager for Molecular Profiling of Ovarian Cancer Cells

DTIC Science & Technology

2016-12-01

2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246-1260. PMC4380877, PMID:25748654. Acknowledgement of...Weissleder R, Lee H, Zhang F, Sharp PA (2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246-1260. 5. Im H, Shao H...Lett 32(10):1229–1231. 6 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1501815112 Im et al. Resource Genome-wide CRISPR Screen in a Mouse Model of Tumor

Using a Novel Transgenic Mouse Model to Study c-Myc Oncogenic Pathway in Castration Resistance and Chemoresistance of Prostate Cancer

DTIC Science & Technology

2017-12-01

AWARD NUMBER: W81XWH-13-1-0162 TITLE: Using a Novel Transgenic Mouse Model to Study c-Myc Oncogenic Pathway in Castration Resistance and...DATES COVERED 15Sept2013 - 14Sept2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Using a Novel Transgenic Mouse Model to Study c-Myc Oncogenic...for concisely studying castration response and CRPC. However, most mice never developed significant tumors. Here, we showed that ablation of p53 in this

RNA sequencing analysis reveals quiescent microglia isolation methods from postnatal mouse brains and limitations of BV2 cells.

PubMed

He, Yingbo; Yao, Xiang; Taylor, Natalie; Bai, Yuchen; Lovenberg, Timothy; Bhattacharya, Anindya

2018-05-22

Microglia play key roles in neuron-glia interaction, neuroinflammation, neural repair, and neurotoxicity. Currently, various microglial in vitro models including primary microglia derived from distinct isolation methods and immortalized microglial cell lines are extensively used. However, the diversity of these existing models raises difficulty in parallel comparison across studies since microglia are sensitive to environmental changes, and thus, different models are likely to show widely varied responses to the same stimuli. To better understand the involvement of microglia in pathophysiological situations, it is critical to establish a reliable microglial model system. With postnatal mouse brains, we isolated microglia using three general methods including shaking, mild trypsinization, and CD11b magnetic-associated cell sorting (MACS) and applied RNA sequencing to compare transcriptomes of the isolated cells. Additionally, we generated a genome-wide dataset by RNA sequencing of immortalized BV2 microglial cell line to compare with primary microglia. Furthermore, based on the outcomes of transcriptional analysis, we compared cellular functions between primary microglia and BV2 cells including immune responses to LPS by quantitative RT-PCR and Luminex Multiplex Assay, TGFβ signaling probed by Western blot, and direct migration by chemotaxis assay. We found that although the yield and purity of microglia were comparable among the three isolation methods, mild trypsinization drove microglia in a relatively active state, evidenced by high amount of amoeboid microglia, enhanced expression of microglial activation genes, and suppression of microglial quiescent genes. In contrast, CD11b MACS was the most reliable and consistent method, and microglia isolated by this method maintained a relatively resting state. Transcriptional and functional analyses revealed that as compared to primary microglia, BV2 cells remain most of the immune functions such as responses to LPS but showed limited TGFβ signaling and chemotaxis upon chemoattractant C5a. Collectively, we determined the optimal isolation methods for quiescent microglia and characterized the limitations of BV2 cells as an alternative of primary microglia. Considering transcriptional and functional differences, caution should be taken when extrapolating data from various microglial models. In addition, our RNA sequencing database serves as a valuable resource to provide novel insights for appropriate application of microglia as in vitro models.

Minimum datasets to establish a CAR-mediated mode of action for rodent liver tumors.

PubMed

Peffer, Richard C; LeBaron, Matthew J; Battalora, Michael; Bomann, Werner H; Werner, Christoph; Aggarwal, Manoj; Rowe, Rocky R; Tinwell, Helen

2018-04-16

Methods for investigating the Mode of Action (MoA) for rodent liver tumors via constitutive androstane receptor (CAR) activation are outlined here, based on current scientific knowledge about CAR and feedback from regulatory agencies globally. The key events (i.e., CAR activation, altered gene expression, cell proliferation, altered foci and increased adenomas/carcinomas) can be demonstrated by measuring a combination of key events and associative events that are markers for the key events. For crop protection products, a primary dataset typically should include a short-term study in the species/strain that showed the tumor response at dose levels that bracket the tumorigenic and non-tumorigenic dose levels. The dataset may vary depending on the species and the test compound. As examples, Case Studies with nitrapyrin (in mice) and metofluthrin (in rats) are described. Based on qualitative differences between the species, the key events leading to tumors in mice or rats by this MoA are not operative in humans. In the future, newer approaches such as a CAR biomarker signature approach and/or in vitro CAR3 reporter assays for mouse, rat and human CAR may eventually be used to demonstrate a CAR MoA is operative, without the need for extensive additional studies in laboratory animals. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Complex versus simple models: ion-channel cardiac toxicity prediction.

PubMed

Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

PubMed

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic

NASA Astrophysics Data System (ADS)

Skok, Gregor; Žagar, Nedjeljka; Honzak, Luka; Žabkar, Rahela; Rakovec, Jože; Ceglar, Andrej

2016-01-01

The study presents a precipitation intercomparison based on two satellite-derived datasets (TRMM 3B42, CMORPH), four raingauge-based datasets (GPCC, E-OBS, Willmott & Matsuura, CRU), ERA Interim reanalysis (ERAInt), and a single climate simulation using the WRF model. The comparison was performed for a domain encompassing parts of Europe and the North Atlantic over the 11-year period of 2000-2010. The four raingauge-based datasets are similar to the TRMM dataset with biases over Europe ranging from -7 % to +4 %. The spread among the raingauge-based datasets is relatively small over most of Europe, although areas with greater uncertainty (more than 30 %) exist, especially near the Alps and other mountainous regions. There are distinct differences between the datasets over the European land area and the Atlantic Ocean in comparison to the TRMM dataset. ERAInt has a small dry bias over the land; the WRF simulation has a large wet bias (+30 %), whereas CMORPH is characterized by a large and spatially consistent dry bias (-21 %). Over the ocean, both ERAInt and CMORPH have a small wet bias (+8 %) while the wet bias in WRF is significantly larger (+47 %). ERAInt has the highest frequency of low-intensity precipitation while the frequency of high-intensity precipitation is the lowest due to its lower native resolution. Both satellite-derived datasets have more low-intensity precipitation over the ocean than over the land, while the frequency of higher-intensity precipitation is similar or larger over the land. This result is likely related to orography, which triggers more intense convective precipitation, while the Atlantic Ocean is characterized by more homogenous large-scale precipitation systems which are associated with larger areas of lower intensity precipitation. However, this is not observed in ERAInt and WRF, indicating the insufficient representation of convective processes in the models. Finally, the Fraction Skill Score confirmed that both models perform better over the Atlantic Ocean with ERAInt outperforming the WRF at low thresholds and WRF outperforming ERAInt at higher thresholds. The diurnal cycle is simulated better in the WRF simulation than in ERAInt, although WRF could not reproduce well the amplitude of the diurnal cycle. While the evaluation of the WRF model confirms earlier findings related to the model's wet bias over European land, the applied satellite-derived precipitation datasets revealed differences between the land and ocean areas along with uncertainties in the observation datasets.

Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?

PubMed Central

Zuberi, Aamir; Lutz, Cathleen

2016-01-01

Abstract The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling systems, are synergistic and serve to make the mouse a better model for biomedical research, enhancing the potential for preclinical drug discovery and personalized medicine. PMID:28053071

Liver carcinogenesis by FOS-dependent inflammation and cholesterol dysregulation

PubMed Central

Bakiri, Latifa; Hamacher, Rainer; Graña, Osvaldo; Guío-Carrión, Ana; Martinez, Lola; Dienes, Hans P.; Thomsen, Martin K.; Hasenfuss, Sebastian C.

2017-01-01

Human hepatocellular carcinomas (HCCs), which arise on a background of chronic liver damage and inflammation, express c-Fos, a component of the AP-1 transcription factor. Using mouse models, we show that hepatocyte-specific deletion of c-Fos protects against diethylnitrosamine (DEN)-induced HCCs, whereas liver-specific c-Fos expression leads to reversible premalignant hepatocyte transformation and enhanced DEN-carcinogenesis. c-Fos–expressing livers display necrotic foci, immune cell infiltration, and altered hepatocyte morphology. Furthermore, increased proliferation, dedifferentiation, activation of the DNA damage response, and gene signatures of aggressive HCCs are observed. Mechanistically, c-Fos decreases expression and activity of the nuclear receptor LXRα, leading to increased hepatic cholesterol and accumulation of toxic oxysterols and bile acids. The phenotypic consequences of c-Fos expression are partially ameliorated by the anti-inflammatory drug sulindac and largely prevented by statin treatment. An inverse correlation between c-FOS and the LXRα pathway was also observed in human HCC cell lines and datasets. These findings provide a novel link between chronic inflammation and metabolic pathways important in liver cancer. PMID:28356389

Inactivation of the ATMIN/ATM pathway protects against glioblastoma formation

PubMed Central

Blake, Sophia M; Stricker, Stefan H; Halavach, Hanna; Poetsch, Anna R; Cresswell, George; Kelly, Gavin; Kanu, Nnennaya; Marino, Silvia; Luscombe, Nicholas M; Pollard, Steven M; Behrens, Axel

2016-01-01

Glioblastoma multiforme (GBM) is the most aggressive human primary brain cancer. Using a Trp53-deficient mouse model of GBM, we show that genetic inactivation of the Atm cofactor Atmin, which is dispensable for embryonic and adult neural development, strongly suppresses GBM formation. Mechanistically, expression of several GBM-associated genes, including Pdgfra, was normalized by Atmin deletion in the Trp53-null background. Pharmacological ATM inhibition also reduced Pdgfra expression, and reduced the proliferation of Trp53-deficient primary glioma cells from murine and human tumors, while normal neural stem cells were unaffected. Analysis of GBM datasets showed that PDGFRA expression is also significantly increased in human TP53-mutant compared with TP53-wild-type tumors. Moreover, combined treatment with ATM and PDGFRA inhibitors efficiently killed TP53-mutant primary human GBM cells, but not untransformed neural stem cells. These results reveal a new requirement for ATMIN-dependent ATM signaling in TP53-deficient GBM, indicating a pro-tumorigenic role for ATM in the context of these tumors. DOI: http://dx.doi.org/10.7554/eLife.08711.001 PMID:26984279

Mouse Model for the Preclinical Study of Metastatic Disease | NCI Technology Transfer Center | TTC

Cancer.gov

The Laboratory of Cancer Biology and Genetics, National Cancer Institute seeks partners for collaborative research to co-develop a mouse model that shows preclinical therapeutic response of residual metastatic disease.

Role of Growth Hormone in Prostate Cancer

DTIC Science & Technology

2007-02-01

syndrome produced by targeted disruption of the mouse growth hormone receptor/binding protein gene (the Laron mouse). Proc Natl Acad Sci USA 94:13215... Laron mouse, in which the gene coding for both GHR and GH binding protein has been disrupted or knocked out, with the C3(1)/Tag mouse, which develops...the Laron mouse). Nevertheless, the new model presented here demonstrates that the loss of GHR produced a significant reduction in the level of PIN in

Bat-mouse bone marrow chimera: a novel animal model for dissecting the uniqueness of the bat immune system.

PubMed

Yong, Kylie Su Mei; Ng, Justin Han Jia; Her, Zhisheng; Hey, Ying Ying; Tan, Sue Yee; Tan, Wilson Wei Sheng; Irac, Sergio Erdal; Liu, Min; Chan, Xue Ying; Gunawan, Merry; Foo, Randy Jee Hiang; Low, Dolyce Hong Wen; Mendenhall, Ian Hewitt; Chionh, Yok Teng; Dutertre, Charles-Antoine; Chen, Qingfeng; Wang, Lin-Fa

2018-03-16

Bats are an important animal model with long lifespans, low incidences of tumorigenesis and an ability to asymptomatically harbour pathogens. Currently, in vivo studies of bats are hampered due to their low reproduction rates. To overcome this, we transplanted bat cells from bone marrow (BM) and spleen into an immunodeficient mouse strain NOD-scid IL-2R -/- (NSG), and have successfully established stable, long-term reconstitution of bat immune cells in mice (bat-mice). Immune functionality of our bat-mouse model was demonstrated through generation of antigen-specific antibody response by bat cells following immunization. Post-engraftment of total bat BM cells and splenocytes, bat immune cells survived, expanded and repopulated the mouse without any observable clinical abnormalities. Utilizing bat's remarkable immunological functions, this novel model has a potential to be transformed into a powerful platform for basic and translational research.

Galantamine improves olfactory learning in the Ts65Dn mouse model of Down syndrome

PubMed Central

Simoes de Souza, Fabio M.; Busquet, Nicolas; Blatner, Megan; Maclean, Kenneth N.; Restrepo, Diego

2011-01-01

Down syndrome (DS) is the most common form of congenital intellectual disability. Although DS involves multiple disturbances in various tissues, there is little doubt that in terms of quality of life cognitive impairment is the most serious facet and there is no effective treatment for this aspect of the syndrome. The Ts65Dn mouse model of DS recapitulates multiple aspects of DS including cognitive impairment. Here the Ts65Dn mouse model of DS was evaluated in an associative learning paradigm based on olfactory cues. In contrast to disomic controls, trisomic mice exhibited significant deficits in olfactory learning. Treatment of trisomic mice with the acetylcholinesterase inhibitor galantamine resulted in a significant improvement in olfactory learning. Collectively, our study indicates that olfactory learning can be a sensitive tool for evaluating deficits in associative learning in mouse models of DS and that galantamine has therapeutic potential for improving cognitive abilities. PMID:22355654

Galantamine improves olfactory learning in the Ts65Dn mouse model of Down syndrome.

PubMed

de Souza, Fabio M Simoes; Busquet, Nicolas; Blatner, Megan; Maclean, Kenneth N; Restrepo, Diego

2011-01-01

Down syndrome (DS) is the most common form of congenital intellectual disability. Although DS involves multiple disturbances in various tissues, there is little doubt that in terms of quality of life cognitive impairment is the most serious facet and there is no effective treatment for this aspect of the syndrome. The Ts65Dn mouse model of DS recapitulates multiple aspects of DS including cognitive impairment. Here the Ts65Dn mouse model of DS was evaluated in an associative learning paradigm based on olfactory cues. In contrast to disomic controls, trisomic mice exhibited significant deficits in olfactory learning. Treatment of trisomic mice with the acetylcholinesterase inhibitor galantamine resulted in a significant improvement in olfactory learning. Collectively, our study indicates that olfactory learning can be a sensitive tool for evaluating deficits in associative learning in mouse models of DS and that galantamine has therapeutic potential for improving cognitive abilities.

A candidate model for Angelman syndrome in the mouse.

PubMed

Cattanach, B M; Barr, J A; Beechey, C V; Martin, J; Noebels, J; Jones, J

1997-07-01

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are well-recognized examples of imprinting in humans. They occur most commonly with paternal and maternal 15q11-13 deletions, but also with maternal and paternal disomy. Both syndromes have also occurred more rarely in association with smaller deletions seemingly causing abnormal imprinting. A putative mouse model of PWS, occurring with maternal duplication (partial maternal disomy) for the homologous region, has been described in a previous paper but, although a second imprinting effect that could have provided a mouse model of AS was found, it appeared to be associated with a slightly different region of the chromosome. Here, we provide evidence that the same region is in fact involved and further demonstrate that animals with paternal duplication for the region exhibit characteristics of AS patients. A mouse model of AS is, therefore, strongly indicated.

Development and testing of a mouse simulated space flight model

NASA Technical Reports Server (NTRS)

Sonnenfeld, Gerald

1987-01-01

The development and testing of a mouse model for simulating some aspects of weightlessness that occurs during space flight, and the carrying out of immunological experiments on animals undergoing space flight is examined. The mouse model developed was an antiorthostatic, hypokinetic, hypodynamic suspension model similar to one used with rats. The study was divided into two parts. The first involved determination of which immunological parameters should be observed on animals flown during space flight or studied in the suspension model. The second involved suspending mice and determining which of those immunological parameters were altered by the suspension. Rats that were actually flown in Space Shuttle SL-3 were used to test the hypotheses.

Multiple Drug Treatments That Increase cAMP Signaling Restore Long-Term Memory and Aberrant Signaling in Fragile X Syndrome Models.

PubMed

Choi, Catherine H; Schoenfeld, Brian P; Bell, Aaron J; Hinchey, Joseph; Rosenfelt, Cory; Gertner, Michael J; Campbell, Sean R; Emerson, Danielle; Hinchey, Paul; Kollaros, Maria; Ferrick, Neal J; Chambers, Daniel B; Langer, Steven; Sust, Steven; Malik, Aatika; Terlizzi, Allison M; Liebelt, David A; Ferreiro, David; Sharma, Ali; Koenigsberg, Eric; Choi, Richard J; Louneva, Natalia; Arnold, Steven E; Featherstone, Robert E; Siegel, Steven J; Zukin, R Suzanne; McDonald, Thomas V; Bolduc, Francois V; Jongens, Thomas A; McBride, Sean M J

2016-01-01

Fragile X is the most common monogenic disorder associated with intellectual disability (ID) and autism spectrum disorders (ASD). Additionally, many patients are afflicted with executive dysfunction, ADHD, seizure disorder and sleep disturbances. Fragile X is caused by loss of FMRP expression, which is encoded by the FMR1 gene. Both the fly and mouse models of fragile X are also based on having no functional protein expression of their respective FMR1 homologs. The fly model displays well defined cognitive impairments and structural brain defects and the mouse model, although having subtle behavioral defects, has robust electrophysiological phenotypes and provides a tool to do extensive biochemical analysis of select brain regions. Decreased cAMP signaling has been observed in samples from the fly and mouse models of fragile X as well as in samples derived from human patients. Indeed, we have previously demonstrated that strategies that increase cAMP signaling can rescue short term memory in the fly model and restore DHPG induced mGluR mediated long term depression (LTD) in the hippocampus to proper levels in the mouse model (McBride et al., 2005; Choi et al., 2011, 2015). Here, we demonstrate that the same three strategies used previously with the potential to be used clinically, lithium treatment, PDE-4 inhibitor treatment or mGluR antagonist treatment can rescue long term memory in the fly model and alter the cAMP signaling pathway in the hippocampus of the mouse model.

A bivariate contaminated binormal model for robust fitting of proper ROC curves to a pair of correlated, possibly degenerate, ROC datasets.

PubMed

Zhai, Xuetong; Chakraborty, Dev P

2017-06-01

The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics. A limited simulation validation of the method was performed. CORCBM and CORROC2 were applied to two datasets containing nine readers each contributing paired interpretations. CORCBM successfully fitted the data for all readers, whereas CORROC2 failed to fit a degenerate dataset. All fits were visually reasonable. All CORCBM fits were proper, whereas all CORROC2 fits were improper. CORCBM and CORROC2 were in agreement (a) in declaring only one of the nine readers as having significantly different performances in the two modalities; (b) in estimating higher correlations for diseased cases than for nondiseased ones; and (c) in finding that the intermodality correlation estimates for nondiseased cases were consistent between the two methods. All CORCBM fits yielded higher area under curve (AUC) than the CORROC2 fits, consistent with the fact that a proper ROC model like CORCBM is based on a likelihood-ratio-equivalent decision variable, and consequently yields higher performance than the binormal model-based CORROC2. The method gave satisfactory fits to four simulated datasets. CORCBM is a robust method for fitting paired ROC datasets, always yielding proper ROC curves, and able to fit degenerate datasets. © 2017 American Association of Physicists in Medicine.

The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics.

PubMed

Melozzi, Francesca; Woodman, Marmaduke M; Jirsa, Viktor K; Bernard, Christophe

2017-01-01

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain's structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

Monitoring blood-flow in the mouse cochlea using an endoscopic laser speckle contrast imaging system

PubMed Central

Yu, Sunkon; Jung, Byungjo; Choi, Jin Sil

2018-01-01

Laser speckle contrast imaging (LSCI) enables continuous high-resolution assessment of microcirculation in real-time. We applied an endoscope to LSCI to measure cochlear blood-flow in an ischemia–reperfusion mouse model. We also explored whether using xenon light in combination with LSCI facilitates visualization of anatomical position. Based on a previous preliminary study, the appropriate wavelength for penetrating the thin bony cochlea was 830 nm. A 2.7-mm-diameter endoscope was used, as appropriate for the size of the mouse cochlea. Our endoscopic LSCI system was used to illuminate the right cochlea after dissection of the mouse. We observed changes in the speckle signals when we applied the endoscopic LSCI system to the ischemia-reperfusion mouse model. The anatomical structure of the mouse cochlea and surrounding structures were clearly visible using the xenon light. The speckle signal of the cochlea was scattered, with an intensity that varied between that of the stapes (with the lowest signal), the negative control, and the stapedial artery (with the highest signal), the positive control. In the cochlear ischemia–reperfusion mouse model, the speckle signal of the cochlea decreased during the ischemic phase, and increased during the reperfusion phase, clearly reflecting cochlear blood-flow. The endoscopic LSCI system generates high-resolution images in real-time, allowing visualization of blood-flow and its changes in the mouse cochlea. Anatomical structures were clearly matched using LSCI along with visible light. PMID:29489849

Monitoring blood-flow in the mouse cochlea using an endoscopic laser speckle contrast imaging system.

PubMed

Kong, Tae Hoon; Yu, Sunkon; Jung, Byungjo; Choi, Jin Sil; Seo, Young Joon

2018-01-01

Laser speckle contrast imaging (LSCI) enables continuous high-resolution assessment of microcirculation in real-time. We applied an endoscope to LSCI to measure cochlear blood-flow in an ischemia-reperfusion mouse model. We also explored whether using xenon light in combination with LSCI facilitates visualization of anatomical position. Based on a previous preliminary study, the appropriate wavelength for penetrating the thin bony cochlea was 830 nm. A 2.7-mm-diameter endoscope was used, as appropriate for the size of the mouse cochlea. Our endoscopic LSCI system was used to illuminate the right cochlea after dissection of the mouse. We observed changes in the speckle signals when we applied the endoscopic LSCI system to the ischemia-reperfusion mouse model. The anatomical structure of the mouse cochlea and surrounding structures were clearly visible using the xenon light. The speckle signal of the cochlea was scattered, with an intensity that varied between that of the stapes (with the lowest signal), the negative control, and the stapedial artery (with the highest signal), the positive control. In the cochlear ischemia-reperfusion mouse model, the speckle signal of the cochlea decreased during the ischemic phase, and increased during the reperfusion phase, clearly reflecting cochlear blood-flow. The endoscopic LSCI system generates high-resolution images in real-time, allowing visualization of blood-flow and its changes in the mouse cochlea. Anatomical structures were clearly matched using LSCI along with visible light.

BMDExpress Data Viewer: A Visualization Tool to Analyze BMDExpress Datasets

EPA Science Inventory

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure in human risk assessments. BMDExpress applies BMD modeling to transcriptomics datasets and groups genes to biological processes and pathways for rapid assessment of doses at whic...

ANALYSIS OF TMEFF2 ALLOGRAFTS AND TRANSGENIC MOUSE MODELS REVEALS ROLES IN PROSTATE REGENERATION AND CANCER

PubMed Central

Corbin, JM.; Overcash, RF.; Wren, JD.; Coburn, A.; Tipton, GJ.; Ezzell, JA.; McNaughton, KK.; Fung, KM; Kosanke, SD.; Ruiz-Echevarria, MJ

2015-01-01

BACKGROUND Previous results from our lab indicate a tumor suppressor role for the transmembrane protein with epidermal growth factor and two follistatin motifs 2 (TMEFF2) in prostate cancer (PCa). Here, we further characterize this role and uncover new functions for TMEFF2 in cancer and adult prostate regeneration. METHODS The role of TMEFF2 was examined in PCa cells using Matrigel™ cultures and allograft models of PCa cells. In addition, we developed a transgenic mouse model that expresses TMEFF2 from a prostate specific promoter. Anatomical, histological and metabolic characterizations of the transgenic mouse prostate were conducted. The effect of TMEFF2 in prostate regeneration was studied by analyzing branching morphogenesis in the TMEFF2-expressing mouse lobes and alterations in branching morphogenesis were correlated with the metabolomic profiles of the mouse lobes. The role of TMEFF2 in prostate tumorigenesis in whole animals was investigated by crossing the TMEFF2 transgenic mice with the TRAMP mouse model of PCa and analyzing the histopathological changes in the progeny. RESULTS Ectopic expression of TMEFF2 impairs growth of PCa cells in Matrigel or allograft models. Surprisingly, while TMEFF2 expression in the TRAMP mouse did not have a significant effect on the glandular prostate epithelial lesions, the double TRAMP/TMEFF2 transgenic mice displayed an increased incidence of neuroendocrine type tumors. In addition, TMEFF2 promoted increased branching specifically in the dorsal lobe of the prostate suggesting a potential role in developmental processes. These results correlated with data indicating an alteration in the metabolic profile of the dorsal lobe of the transgenic TMEFF2 mice. CONCLUSIONS Collectively, our results confirm the tumor suppressor role of TMEFF2 and suggest that ectopic expression of TMEFF2 in mouse prostate leads to additional lobe-specific effects in prostate regeneration and tumorigenesis. This points to a complex and multifunctional role for TMEFF2 during PCa progression. PMID:26417683

Analysis of TMEFF2 allografts and transgenic mouse models reveals roles in prostate regeneration and cancer.

PubMed

Corbin, Joshua M; Overcash, Ryan F; Wren, Jonathan D; Coburn, Anita; Tipton, Greg J; Ezzell, Jennifer A; McNaughton, Kirk K; Fung, Kar-Ming; Kosanke, Stanley D; Ruiz-Echevarria, Maria J

2016-01-01

Previous results from our lab indicate a tumor suppressor role for the transmembrane protein with epidermal growth factor and two follistatin motifs 2 (TMEFF2) in prostate cancer (PCa). Here, we further characterize this role and uncover new functions for TMEFF2 in cancer and adult prostate regeneration. The role of TMEFF2 was examined in PCa cells using Matrigel(TM) cultures and allograft models of PCa cells. In addition, we developed a transgenic mouse model that expresses TMEFF2 from a prostate specific promoter. Anatomical, histological, and metabolic characterizations of the transgenic mouse prostate were conducted. The effect of TMEFF2 in prostate regeneration was studied by analyzing branching morphogenesis in the TMEFF2-expressing mouse lobes and alterations in branching morphogenesis were correlated with the metabolomic profiles of the mouse lobes. The role of TMEFF2 in prostate tumorigenesis in whole animals was investigated by crossing the TMEFF2 transgenic mice with the TRAMP mouse model of PCa and analyzing the histopathological changes in the progeny. Ectopic expression of TMEFF2 impairs growth of PCa cells in Matrigel or allograft models. Surprisingly, while TMEFF2 expression in the TRAMP mouse did not have a significant effect on the glandular prostate epithelial lesions, the double TRAMP/TMEFF2 transgenic mice displayed an increased incidence of neuroendocrine type tumors. In addition, TMEFF2 promoted increased branching specifically in the dorsal lobe of the prostate suggesting a potential role in developmental processes. These results correlated with data indicating an alteration in the metabolic profile of the dorsal lobe of the transgenic TMEFF2 mice. Collectively, our results confirm the tumor suppressor role of TMEFF2 and suggest that ectopic expression of TMEFF2 in mouse prostate leads to additional lobe-specific effects in prostate regeneration and tumorigenesis. This points to a complex and multifunctional role for TMEFF2 during PCa progression. © 2015 Wiley Periodicals, Inc.

What do mouse models of muscular dystrophy tell us about the DAPC and its components?

PubMed

Whitmore, Charlotte; Morgan, Jennifer

2014-12-01

There are over 30 mouse models with mutations or inactivations in the dystrophin-associated protein complex. This complex is thought to play a crucial role in the functioning of muscle, as both a shock absorber and signalling centre, although its role in the pathogenesis of muscular dystrophy is not fully understood. The first mouse model of muscular dystrophy to be identified with a mutation in a component of the dystrophin-associated complex (dystrophin) was the mdx mouse in 1984. Here, we evaluate the key characteristics of the mdx in comparison with other mouse mutants with inactivations in DAPC components, along with key modifiers of the disease phenotype. By discussing the differences between the individual phenotypes, we show that the functioning of the DAPC and consequently its role in the pathogenesis is more complicated than perhaps currently appreciated. © 2014 The Authors. International Journal of Experimental Pathology © 2014 International Journal of Experimental Pathology.

Characterisation of a C1qtnf5 Ser163Arg Knock-In Mouse Model of Late-Onset Retinal Macular Degeneration

PubMed Central

Shu, Xinhua; Luhmann, Ulrich F. O.; Aleman, Tomas S.; Barker, Susan E.; Lennon, Alan; Tulloch, Brian; Chen, Mei; Xu, Heping; Jacobson, Samuel G.; Ali, Robin; Wright, Alan F.

2011-01-01

A single founder mutation resulting in a Ser163Arg substitution in the C1QTNF5 gene product causes autosomal dominant late-onset retinal macular degeneration (L-ORMD) in humans, which has clinical and pathological features resembling age-related macular degeneration. We generated and characterised a mouse “knock-in” model carrying the Ser163Arg mutation in the orthologous murine C1qtnf5 gene by site-directed mutagenesis and homologous recombination into mouse embryonic stem cells. Biochemical, immunological, electron microscopic, fundus autofluorescence, electroretinography and laser photocoagulation analyses were used to characterise the mouse model. Heterozygous and homozygous knock-in mice showed no significant abnormality in any of the above measures at time points up to 2 years. This result contrasts with another C1qtnf5 Ser163Arg knock-in mouse which showed most of the features of L-ORMD but differed in genetic background and targeting construct. PMID:22110650

Live dynamic imaging and analysis of developmental cardiac defects in mouse models with optical coherence tomography

NASA Astrophysics Data System (ADS)

Lopez, Andrew L.; Wang, Shang; Garcia, Monica; Valladolid, Christian; Larin, Kirill V.; Larina, Irina V.

2015-03-01

Understanding mouse embryonic development is an invaluable resource for our interpretation of normal human embryology and congenital defects. Our research focuses on developing methods for live imaging and dynamic characterization of early embryonic development in mouse models of human diseases. Using multidisciplinary methods: optical coherence tomography (OCT), live mouse embryo manipulations and static embryo culture, molecular biology, advanced image processing and computational modeling we aim to understand developmental processes. We have developed an OCT based approach to image live early mouse embryos (E8.5 - E9.5) cultured on an imaging stage and visualize developmental events with a spatial resolution of a few micrometers (less than the size of an individual cell) and a frame rate of up to hundreds of frames per second and reconstruct cardiodynamics in 4D (3D+time). We are now using these methods to study how specific embryonic lethal mutations affect cardiac morphology and function during early development.

Scattered Dose Calculations and Measurements in a Life-Like Mouse Phantom

PubMed Central

Welch, David; Turner, Leah; Speiser, Michael; Randers-Pehrson, Gerhard; Brenner, David J.

2017-01-01

Anatomically accurate phantoms are useful tools for radiation dosimetry studies. In this work, we demonstrate the construction of a new generation of life-like mouse phantoms in which the methods have been generalized to be applicable to the fabrication of any small animal. The mouse phantoms, with built-in density inhomogeneity, exhibit different scattering behavior dependent on where the radiation is delivered. Computer models of the mouse phantoms and a small animal irradiation platform were devised in Monte Carlo N-Particle code (MCNP). A baseline test replicating the irradiation system in a computational model shows minimal differences from experimental results from 50 Gy down to 0.1 Gy. We observe excellent agreement between scattered dose measurements and simulation results from X-ray irradiations focused at either the lung or the abdomen within our phantoms. This study demonstrates the utility of our mouse phantoms as measurement tools with the goal of using our phantoms to verify complex computational models. PMID:28140787

Overview of genetically engineered mouse models of colorectal carcinoma to enable translational biology and drug development.

PubMed

Roper, Jatin; Martin, Eric S; Hung, Kenneth E

2014-06-16

Preclinical models for colorectal cancer (CRC) are critical for translational biology and drug development studies to characterize and treat this condition. Mouse models of human cancer are particularly popular because of their relatively low cost, short life span, and ease of use. Genetically engineered mouse models (GEMMs) of CRC are engineered from germline or somatic modification of critical tumor suppressor genes and/or oncogenes that drive mutations in human disease. Detailed in this overview are the salient features of several useful colorectal cancer GEMMs and their value as tools for translational biology and preclinical drug development. Copyright © 2014 John Wiley & Sons, Inc.

Application of Mouse Models to Research in Hearing and Balance.

PubMed

Ohlemiller, Kevin K; Jones, Sherri M; Johnson, Kenneth R

2016-12-01

Laboratory mice (Mus musculus) have become the major model species for inner ear research. The major uses of mice include gene discovery, characterization, and confirmation. Every application of mice is founded on assumptions about what mice represent and how the information gained may be generalized. A host of successes support the continued use of mice to understand hearing and balance. Depending on the research question, however, some mouse models and research designs will be more appropriate than others. Here, we recount some of the history and successes of the use of mice in hearing and vestibular studies and offer guidelines to those considering how to apply mouse models.

Continuous imaging of the blood vessels in tumor mouse dorsal skin window chamber model by using SD-OCT

NASA Astrophysics Data System (ADS)

Peng, Xiao; Yang, Shaozhuang; Yu, Bin; Wang, Qi; Lin, Danying; Gao, Jian; Zhang, Peiqi; Ma, Yiqun; Qu, Junle; Niu, Hanben

2016-03-01

Optical Coherence Tomography (OCT) has been widely applied into microstructure imaging of tissues or blood vessels with a series of advantages, including non-destructiveness, real-time imaging, high resolution and high sensitivity. In this study, a Spectral Domain OCT (SD-OCT) system with higher sensitivity and signal-to-noise ratio (SNR) was built up, which was used to observe the blood vessel distribution and blood flow in the dorsal skin window chamber of the nude mouse tumor model. In order to obtain comparable data, the distribution images of blood vessels were collected from the same mouse before and after tumor injection. In conclusion, in vivo blood vessel distribution images of the tumor mouse model have been continuously obtained during around two weeks.

Graded Maximal Exercise Testing to Assess Mouse Cardio-Metabolic Phenotypes

PubMed Central

Petrosino, Jennifer M.; Heiss, Valerie J.; Maurya, Santosh K.; Kalyanasundaram, Anuradha; Periasamy, Muthu; LaFountain, Richard A.; Wilson, Jacob M.; Simonetti, Orlando P.; Ziouzenkova, Ouliana

2016-01-01

Functional assessments of cardiovascular fitness (CVF) are needed to establish animal models of dysfunction, test the effects of novel therapeutics, and establish the cardio-metabolic phenotype of mice. In humans, the graded maximal exercise test (GXT) is a standardized diagnostic for assessing CVF and mortality risk. These tests, which consist of concurrent staged increases in running speed and inclination, provide diagnostic cardio-metabolic parameters, such as, VO2max, anaerobic threshold, and metabolic crossover. Unlike the human-GXT, published mouse treadmill tests have set, not staged, increases in inclination as speed progress until exhaustion (PXT). Additionally, they often lack multiple cardio-metabolic parameters. Here, we developed a mouse-GXT with the intent of improving mouse-exercise testing sensitivity and developing translatable parameters to assess CVF in healthy and dysfunctional mice. The mouse-GXT, like the human-GXT, incorporated staged increases in inclination, speed, and intensity; and, was designed by considering imitations of the PXT and differences between human and mouse physiology. The mouse-GXT and PXTs were both tested in healthy mice (C57BL/6J, FVBN/J) to determine their ability to identify cardio-metabolic parameters (anaerobic threshold, VO2max, metabolic crossover) observed in human-GXTs. Next, theses assays were tested on established diet-induced (obese-C57BL/6J) and genetic (cardiac isoform Casq2-/-) models of cardiovascular dysfunction. Results showed that both tests reported VO2max and provided reproducible data about performance. Only the mouse-GXT reproducibly identified anaerobic threshold, metabolic crossover, and detected impaired CVF in dysfunctional models. Our findings demonstrated that the mouse-GXT is a sensitive, non-invasive, and cost-effective method for assessing CVF in mice. This new test can be used as a functional assessment to determine the cardio-metabolic phenotype of various animal models or the effects of novel therapeutics. PMID:26859763

Graded Maximal Exercise Testing to Assess Mouse Cardio-Metabolic Phenotypes.

PubMed

Petrosino, Jennifer M; Heiss, Valerie J; Maurya, Santosh K; Kalyanasundaram, Anuradha; Periasamy, Muthu; LaFountain, Richard A; Wilson, Jacob M; Simonetti, Orlando P; Ziouzenkova, Ouliana

2016-01-01

Functional assessments of cardiovascular fitness (CVF) are needed to establish animal models of dysfunction, test the effects of novel therapeutics, and establish the cardio-metabolic phenotype of mice. In humans, the graded maximal exercise test (GXT) is a standardized diagnostic for assessing CVF and mortality risk. These tests, which consist of concurrent staged increases in running speed and inclination, provide diagnostic cardio-metabolic parameters, such as, VO2max, anaerobic threshold, and metabolic crossover. Unlike the human-GXT, published mouse treadmill tests have set, not staged, increases in inclination as speed progress until exhaustion (PXT). Additionally, they often lack multiple cardio-metabolic parameters. Here, we developed a mouse-GXT with the intent of improving mouse-exercise testing sensitivity and developing translatable parameters to assess CVF in healthy and dysfunctional mice. The mouse-GXT, like the human-GXT, incorporated staged increases in inclination, speed, and intensity; and, was designed by considering imitations of the PXT and differences between human and mouse physiology. The mouse-GXT and PXTs were both tested in healthy mice (C57BL/6J, FVBN/J) to determine their ability to identify cardio-metabolic parameters (anaerobic threshold, VO2max, metabolic crossover) observed in human-GXTs. Next, theses assays were tested on established diet-induced (obese-C57BL/6J) and genetic (cardiac isoform Casq2-/-) models of cardiovascular dysfunction. Results showed that both tests reported VO2max and provided reproducible data about performance. Only the mouse-GXT reproducibly identified anaerobic threshold, metabolic crossover, and detected impaired CVF in dysfunctional models. Our findings demonstrated that the mouse-GXT is a sensitive, non-invasive, and cost-effective method for assessing CVF in mice. This new test can be used as a functional assessment to determine the cardio-metabolic phenotype of various animal models or the effects of novel therapeutics.

Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants.

PubMed

Sauzet, Odile; Peacock, Janet L

2017-07-20

The analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present. Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared. The presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations. This study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.

Multilevel principal component analysis (mPCA) in shape analysis: A feasibility study in medical and dental imaging.

PubMed

Farnell, D J J; Popat, H; Richmond, S

2016-06-01

Methods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately. We wish to test the feasibility of multilevel principal components analysis (PCA) to build active shape models (ASMs) for cases relevant to medical and dental imaging. Multilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of "standard" (single-level) PCA. Proof of principle was tested by applying mPCA to model basic peri-oral expressions (happy, neutral, sad) approximated to the junction between the mouth/lips. Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points, number of images, and number of groups (i.e., "expressions" for this example). To further test the robustness of the method, mPCA was subsequently applied to a dental imaging dataset utilising landmark points (placed by different clinicians) along the boundary of mandibular cortical bone in panoramic radiographs of the face. Changes of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset. Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA. Similarly, variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset. Results for mPCA were found to be comparable to those of standard PCA for point-to-point errors via miss-one-out testing for this dataset. These errors reduce with increasing number of eigenvectors/values retained, as expected. We have shown that mPCA can be used in shape models for dental and medical image processing. mPCA was found to provide more control and flexibility when compared to standard "single-level" PCA. Specifically, mPCA is preferable to "standard" PCA when multiple levels occur naturally in the dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

Deformable Image Registration based on Similarity-Steered CNN Regression.

PubMed

Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-Jeong; Wang, Qian; Shen, Dinggang

2017-09-01

Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

Ultrastructural study of Rift Valley fever virus in the mouse model

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

Reed, Christopher; Steele, Keith E.; Honko, Anna

Detailed ultrastructural studies of Rift Valley fever virus (RVFV) in the mouse model are needed to develop and characterize a small animal model of RVF for the evaluation of potential vaccines and therapeutics. In this study, the ultrastructural features of RVFV infection in the mouse model were analyzed. The main changes in the liver included the presence of viral particles in hepatocytes and hepatic stem cells accompanied by hepatocyte apoptosis. However, viral particles were observed rarely in the liver; in contrast, particles were extremely abundant in the CNS. Despite extensive lymphocytolysis, direct evidence of viral replication was not observed inmore » the lymphoid tissue. These results correlate with the acute-onset hepatitis and delayed-onset encephalitis that are dominant features of severe human RVF, but suggest that host immune-mediated mechanisms contribute significantly to pathology. The results of this study expand our knowledge of RVFV-host interactions and further characterize the mouse model of RVF.« less

Diverse Application of Magnetic Resonance Imaging for Mouse Phenotyping

PubMed Central

Wu, Yijen L.; Lo, Cecilia W.

2017-01-01

Small animal models, particularly mouse models, of human diseases are becoming an indispensable tool for biomedical research. Studies in animal models have provided important insights into the etiology of diseases and accelerated the development of therapeutic strategies. Detailed phenotypic characterization is essential, both for the development of such animal models and mechanistic studies into disease pathogenesis and testing the efficacy of experimental therapeutics. Magnetic Resonance Imaging (MRI) is a versatile and non-invasive imaging modality with excellent penetration depth, tissue coverage, and soft tissue contrast. MRI, being a multi-modal imaging modality, together with proven imaging protocols and availability of good contrast agents, is ideally suited for phenotyping mutant mouse models. Here we describe the applications of MRI for phenotyping structural birth defects involving the brain, heart, and kidney in mice. The versatility of MRI and its ease of use are well suited to meet the rapidly increasing demands for mouse phenotyping in the coming age of functional genomics. PMID:28544650

A G542X cystic fibrosis mouse model for examining nonsense mutation directed therapies.

PubMed

McHugh, Daniel R; Steele, Miarasa S; Valerio, Dana M; Miron, Alexander; Mann, Rachel J; LePage, David F; Conlon, Ronald A; Cotton, Calvin U; Drumm, Mitchell L; Hodges, Craig A

2018-01-01

Nonsense mutations are present in 10% of patients with CF, produce a premature termination codon in CFTR mRNA causing early termination of translation, and lead to lack of CFTR function. There are no currently available animal models which contain a nonsense mutation in the endogenous Cftr locus that can be utilized to test nonsense mutation therapies. In this study, we create a CF mouse model carrying the G542X nonsense mutation in Cftr using CRISPR/Cas9 gene editing. The G542X mouse model has reduced Cftr mRNA levels, demonstrates absence of CFTR function, and displays characteristic manifestations of CF mice such as reduced growth and intestinal obstruction. Importantly, CFTR restoration is observed in G542X intestinal organoids treated with G418, an aminoglycoside with translational readthrough capabilities. The G542X mouse model provides an invaluable resource for the identification of potential therapies of CF nonsense mutations as well as the assessment of in vivo effectiveness of these potential therapies targeting nonsense mutations.

Humanized Mouse Models for the Study of Human Malaria Parasite Biology, Pathogenesis, and Immunity.

PubMed

Minkah, Nana K; Schafer, Carola; Kappe, Stefan H I

2018-01-01

Malaria parasite infection continues to inflict extensive morbidity and mortality in resource-poor countries. The insufficiently understood parasite biology, continuously evolving drug resistance and the lack of an effective vaccine necessitate intensive research on human malaria parasites that can inform the development of new intervention tools. Humanized mouse models have been greatly improved over the last decade and enable the direct study of human malaria parasites in vivo in the laboratory. Nevertheless, no small animal model developed so far is capable of maintaining the complete life cycle of Plasmodium parasites that infect humans. The ultimate goal is to develop humanized mouse systems in which a Plasmodium infection closely reproduces all stages of a parasite infection in humans, including pre-erythrocytic infection, blood stage infection and its associated pathology, transmission as well as the human immune response to infection. Here, we discuss current humanized mouse models and the future directions that should be taken to develop next-generation models for human malaria parasite research.

Convergence and Divergence in a Multi-Model Ensemble of Terrestrial Ecosystem Models in North America

NASA Astrophysics Data System (ADS)

Dungan, J. L.; Wang, W.; Hashimoto, H.; Michaelis, A.; Milesi, C.; Ichii, K.; Nemani, R. R.

2009-12-01

In support of NACP, we are conducting an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate uncertainties among ecosystem models, satellite datasets, and in-situ measurements. The models used in the experiment include public-domain versions of Biome-BGC, LPJ, TOPS-BGC, and CASA, driven by a consistent set of climate fields for North America at 8km resolution and daily/monthly time steps over the period of 1982-2006. The reference datasets include MODIS Gross Primary Production (GPP) and Net Primary Production (NPP) products, Fluxnet measurements, and other observational data. The simulation results and the reference datasets are consistently processed and systematically compared in the climate (temperature-precipitation) space; in particular, an alternative to the Taylor diagram is developed to facilitate model-data intercomparisons in multi-dimensional space. The key findings of this study indicate that: the simulated GPP/NPP fluxes are in general agreement with observations over forests, but are biased low (underestimated) over non-forest types; large uncertainties of biomass and soil carbon stocks are found among the models (and reference datasets), often induced by seemingly “small” differences in model parameters and implementation details; the simulated Net Ecosystem Production (NEP) mainly responds to non-respiratory disturbances (e.g. fire) in the models and therefore is difficult to compare with flux data; and the seasonality and interannual variability of NEP varies significantly among models and reference datasets. These findings highlight the problem inherent in relying on only one modeling approach to map surface carbon fluxes and emphasize the pressing necessity of expanded and enhanced monitoring systems to narrow critical structural and parametrical uncertainties among ecosystem models.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

PubMed

Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

2018-01-01

Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Datasets

PubMed Central

Wood, Jeffrey J.; Lynne, Sarah D.; Langer, David A.; Wood, Patricia A.; Clark, Shaunna L.; Eddy, J. Mark; Ialongo, Nicholas

2011-01-01

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using three longitudinal datasets (Ns= 20745, 2311, and 671). Participants in 1st through 12th grades were interviewed annually or bi-annually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged regression models were tested. In a nationally representative dataset (Add Health), middle school students with relatively greater absenteeism at study year 1 tended towards increased depression and conduct problems in study year 2, over and above the effects of autoregressive associations and demographic covariates. The opposite direction of effects was found for both middle and high school students. Analyses with two regionally representative datasets were also partially supportive. Longitudinal links were more evident in adolescence than in childhood. PMID:22188462

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

PubMed

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

In utero mouse embryonic imaging with OCT for ophthalmologic research

NASA Astrophysics Data System (ADS)

Syed, Saba H.; Larina, Irina V.; Dickinson, Mary E.; Larin, Kirill V.

2011-03-01

Live imaging of an eye during embryonic development in mammalian model is important for understanding dynamic aspects of normal and abnormal eye morphogenesis. In this study, we used Swept Source Optical Coherence Tomography (SS-OCT) for live structural imaging of mouse embryonic eye through the uterine wall. The eye structure was reconstructed in mouse embryos at 13.5 to 17.5 days post coitus (dpc). Despite the limited imaging depth of OCT in turbid tissues, we were able to visualize the whole eye globe at these stages. These results suggest that live in utero OCT imaging is a useful tool to study embryonic eye development in the mouse model.

A physiologically based pharmacokinetic model for atrazine and its main metabolites in the adult male C57BL/6 mouse

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

Lin Zhoumeng; Interdisciplinary Toxicology Program, University of Georgia, Athens, GA 30602; Fisher, Jeffrey W.

Atrazine (ATR) is a chlorotriazine herbicide that is widely used and relatively persistent in the environment. In laboratory rodents, excessive exposure to ATR is detrimental to the reproductive, immune, and nervous systems. To better understand the toxicokinetics of ATR and to fill the need for a mouse model, a physiologically based pharmacokinetic (PBPK) model for ATR and its main chlorotriazine metabolites (Cl-TRIs) desethyl atrazine (DE), desisopropyl atrazine (DIP), and didealkyl atrazine (DACT) was developed for the adult male C57BL/6 mouse. Taking advantage of all relevant and recently made available mouse-specific data, a flow-limited PBPK model was constructed. The ATR andmore » DACT sub-models included blood, brain, liver, kidney, richly and slowly perfused tissue compartments, as well as plasma protein binding and red blood cell binding, whereas the DE and DIP sub-models were constructed as simple five-compartment models. The model adequately simulated plasma levels of ATR and Cl-TRIs and urinary dosimetry of Cl-TRIs at four single oral dose levels (250, 125, 25, and 5 mg/kg). Additionally, the model adequately described the dose dependency of brain and liver ATR and DACT concentrations. Cumulative urinary DACT amounts were accurately predicted across a wide dose range, suggesting the model's potential use for extrapolation to human exposures by performing reverse dosimetry. The model was validated using previously reported data for plasma ATR and DACT in mice and rats. Overall, besides being the first mouse PBPK model for ATR and its Cl-TRIs, this model, by analogy, provides insights into tissue dosimetry for rats. The model could be used in tissue dosimetry prediction and as an aid in the exposure assessment to this widely used herbicide.« less

Comparative mRNA analysis of behavioral and genetic mouse models of aggression.

PubMed

Malki, Karim; Tosto, Maria G; Pain, Oliver; Sluyter, Frans; Mineur, Yann S; Crusio, Wim E; de Boer, Sietse; Sandnabba, Kenneth N; Kesserwani, Jad; Robinson, Edward; Schalkwyk, Leonard C; Asherson, Philip

2016-04-01

Mouse models of aggression have traditionally compared strains, most notably BALB/cJ and C57BL/6. However, these strains were not designed to study aggression despite differences in aggression-related traits and distinct reactivity to stress. This study evaluated expression of genes differentially regulated in a stress (behavioral) mouse model of aggression with those from a recent genetic mouse model aggression. The study used a discovery-replication design using two independent mRNA studies from mouse brain tissue. The discovery study identified strain (BALB/cJ and C57BL/6J) × stress (chronic mild stress or control) interactions. Probe sets differentially regulated in the discovery set were intersected with those uncovered in the replication study, which evaluated differences between high and low aggressive animals from three strains specifically bred to study aggression. Network analysis was conducted on overlapping genes uncovered across both studies. A significant overlap was found with the genetic mouse study sharing 1,916 probe sets with the stress model. Fifty-one probe sets were found to be strongly dysregulated across both studies mapping to 50 known genes. Network analysis revealed two plausible pathways including one centered on the UBC gene hub which encodes ubiquitin, a protein well-known for protein degradation, and another on P38 MAPK. Findings from this study support the stress model of aggression, which showed remarkable molecular overlap with a genetic model. The study uncovered a set of candidate genes including the Erg2 gene, which has previously been implicated in different psychopathologies. The gene networks uncovered points at a Redox pathway as potentially being implicated in aggressive related behaviors. © 2016 Wiley Periodicals, Inc.

The Dipeptidyl Peptidases 4, 8, and 9 in Mouse Monocytes and Macrophages: DPP8/9 Inhibition Attenuates M1 Macrophage Activation in Mice.

PubMed

Waumans, Yannick; Vliegen, Gwendolyn; Maes, Lynn; Rombouts, Miche; Declerck, Ken; Van Der Veken, Pieter; Vanden Berghe, Wim; De Meyer, Guido R Y; Schrijvers, Dorien; De Meester, Ingrid

2016-02-01

Atherosclerosis remains the leading cause of death in Western countries. Dipeptidyl peptidase (DPP) 4 has emerged as a novel target for the prevention and treatment of atherosclerosis. Family members DPP8 and 9 are abundantly present in macrophage-rich regions of atherosclerotic plaques, and DPP9 inhibition attenuates activation of human M1 macrophages in vitro. Studying this family in a mouse model for atherosclerosis would greatly advance our knowledge regarding their potential as therapeutic targets. We found that DPP4 is downregulated during mouse monocyte-to-macrophage differentiation. DPP8 and 9 expression seems relatively low in mouse monocytes and macrophages. Viability of primary mouse macrophages is unaffected by DPP4 or DPP8/9 inhibition. Importantly, DPP8/9 inhibition attenuates macrophage activation as IL-6 secretion is significantly decreased. Mouse macrophages respond similarly to DPP inhibition, compared to human macrophages. This shows that the mouse could become a valid model species for the study of DPPs as therapeutic targets in atherosclerosis.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers.

PubMed

Teodoro, Douglas; Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms.

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers

PubMed Central

Sundvall, Erik; João Junior, Mario; Ruch, Patrick; Miranda Freire, Sergio

2018-01-01

The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms. PMID:29293556

Biome specificity of distinct genetic lineages within the four-striped mouse Rhabdomys pumilio (Rodentia: Muridae) from southern Africa with implications for taxonomy.

PubMed

du Toit, Nina; van Vuuren, Bettine Jansen; Matthee, Sonja; Matthee, Conrad A

2012-10-01

Within southern Africa, a link between past climatic changes and faunal diversification has been hypothesized for a diversity of taxa. To test the hypothesis that evolutionary divergences may be correlated to vegetation changes (induced by changes in climate), we selected the widely distributed four-striped mouse, Rhabdomys, as a model. Two species are currently recognized, the mesic-adapted R. dilectus and arid-adapted R. pumilio. However, the morphology-based taxonomy and the distribution boundaries of previously described subspecies remain poorly defined. The current study, which spans seven biomes, focuses on the spatial genetic structure of the arid-adapted R. pumilio (521 specimens from 31 localities), but also includes limited sampling of the mesic-adapted R. dilectus (33 specimens from 10 localities) to act as a reference for interspecific variation within the genus. The mitochondrial COI gene and four nuclear introns (Eef1a1, MGF, SPTBN1, Bfib7) were used for the construction of gene trees. Mitochondrial DNA analyses indicate that Rhabdomys consists of four reciprocally monophyletic, geographically structured clades, with three distinct lineages present within the arid-adapted R. pumilio. These monophyletic lineages differ by at least 7.9% (±0.3) and these results are partly confirmed by a multilocus network of the combined nuclear intron dataset. Ecological niche modeling in MaxEnt supports a strong correlation between regional biomes and the distribution of distinct evolutionary lineages of Rhabdomys. A Bayesian relaxed molecular clock suggests that the geographic clades diverged between 3.09 and 4.30Ma, supporting the hypothesis that the radiation within the genus coincides with paleoclimatic changes (and the establishment of the biomes) characterizing the Miocene-Pliocene boundary. Marked genetic divergence at the mitochondrial DNA level, coupled with strong nuclear and mtDNA signals of non-monophyly of R. pumilio, support the notion that a taxonomic revision of the genus is needed. Copyright © 2012 Elsevier Inc. All rights reserved.

Early onset of disc degeneration in SM/J mice is associated with changes in ion transport systems and fibrotic events.

PubMed

Zhang, Ying; Xiong, Chi; Kudelko, Mateusz; Li, Yan; Wang, Cheng; Wong, Yuk Lun; Tam, Vivian; Rai, Muhammad Farooq; Cheverud, James; Lawson, Heather A; Sandell, Linda; Chan, Wilson C W; Cheah, Kathryn S E; Sham, Pak C; Chan, Danny

2018-04-09

Intervertebral disc degeneration (IDD) causes back pain and sciatica, affecting quality of life and resulting in high economic/social burden. The etiology of IDD is not well understood. Along with aging and environmental factors, genetic factors also influence the onset, progression and severity of IDD. Genetic studies of risk factors for IDD using human cohorts are limited by small sample size and low statistical power. Animal models amenable to genetic and functional studies of IDD provide desirable alternatives. Despite differences in size and cellular content as compared to human intervertebral discs (IVDs), the mouse is a powerful model for genetics and assessment of cellular changes relevant to human biology. Here, we provide evidence for early onset disc degeneration in SM/J relative to LG/J mice with poor and good tissue healing capacity respectively. In the first few months of life, LG/J mice maintain a relatively constant pool of notochordal-like cells in the nucleus pulposus (NP) of the IVD. In contrast, chondrogenic events are observed in SM/J mice beginning as early as one-week-old, with progressive fibrotic changes. Further, the extracellular matrix changes in the NP are consistent with IVD degeneration. Leveraging on the genomic data of two parental and two recombinant inbred lines, we assessed the genetic contribution to the NP changes and identified processes linked to the regulation of ion transport systems. Significantly, "transport" system is also in the top three gene ontology (GO) terms from a comparative proteomic analysis of the mouse NP. These findings support the potential of the SM/J, LG/J and their recombinant inbred lines for future genetic and biological analysis in mice and validation of candidate genes and biological relevance in human cohort studies. The proteomic data has been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD008784. Copyright © 2017. Published by Elsevier B.V.

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

PubMed

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

2018-01-01

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

A Model-Based Approach for Microvasculature Structure Distortion Correction in Two-Photon Fluorescence Microscopy Images

PubMed Central

Dao, Lam; Glancy, Brian; Lucotte, Bertrand; Chang, Lin-Ching; Balaban, Robert S; Hsu, Li-Yueh

2015-01-01

SUMMARY This paper investigates a post-processing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modeling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to sub volumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. PMID:26224257

CERRADO SMALL MAMMALS: a dataset of abundance and distribution of marsupials, lagomorphs, and rodents in a neotropical savanna.

PubMed

Mendonça, André F; Percequillo, Alexandre R; de Camargo, Nicholas F; Ribeiro, Juliana F; Palma, Alexandre R T; Oliveira, Leonardo C; Câmara, Edeltrudes M V C; Vieira, Emerson M

2018-04-27

Patterns in distribution and local abundance of species within a biome are central concerns in ecology and allow the understanding of the effects of habitat loss on rates of species extinction; provide support for the creation and management of reserves; and contribute to the identification and quantification of the processes that allow niche partitioning by species. However, despite the importance in the conservation and management of the ecosystems, most systematized information on the abundance and distribution of small mammals is restricted to the northern hemisphere or forest ecosystems. For tropical biomes, an important part of this information remains dispersed and difficult to access in the form of theses, technical reports or unpublished datasets. Here we present a comprehensive dataset of abundance and richness of small mammals in the Cerrado, the largest Neotropical savanna. This dataset includes 2,599 records of 446 sites from 96 studies. Despite of more than 50% of references in this dataset are peer-reviewed journal articles, 45.78% of communities were compiled from theses. The dataset comprises 24,283 individuals of 55 genera and at least 118 species of small mammals including 29 marsupials, two lagomorphs (one exotic) and 87 rodents (three exotic). Local species richness ranged from one to 26 species (5.82 ±3.55, average species richness ±SD). We observed hyper-dominance of a few species; the 10 most abundant species in this dataset represented 60.19% of all recorded individuals. The hairy-tailed bolo mouse (Necromys lasiurus) represented over than 20% of all individuals and occurred at more than 50% of sites. Furthermore, we identified 18 environments, 16 native vegetation types, and two anthropic environments. Typical savanna and gallery forest were the most frequently sampled vegetation types (comprising 46.94% of all sampled sites) and the most speciose ones (57 species for typical savanna and 53 species for gallery forest). The information contained on this dataset can be used to analyze ecological questions as relationship between local abundance and regional distribution, relevance of local and regional factors on community structuring, and the role of phylogenetic mechanisms on community assembling. It can also be useful in conservation efforts in this biodiversity hotspot. No copyright, proprietary, or cost restrictions apply. Please cite this paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Flood Inundation Modelling in the Kuantan River Basin using 1D-2D Flood Modeller coupled with ASTER-GDEM

NASA Astrophysics Data System (ADS)

Ng, Z. F.; Gisen, J. I.; Akbari, A.

2018-03-01

Topography dataset is an important input in performing flood inundation modelling. However, it is always difficult to obtain high resolution topography that provide accurate elevation information. Fortunately, there are some open source topography datasets available with reasonable resolution such as SRTM and ASTER-GDEM. In Malaysia particularly in Kuantan, the modelling research on the floodplain area is still lacking. This research aims to: a) to investigate the suitability of ASTER-GDEM to be applied in the 1D-2D flood inundation modelling for the Kuantan River Basin; b) to generate flood inundation map for Kuantan river basin. The topography dataset used in this study is ASTER-GDEM to generate physical characteristics of watershed in the basin. It is used to perform rainfall runoff modelling for hydrological studies and to delineate flood inundation area in the Flood Modeller. The results obtained have shown that a 30m resolution ASTER-GDEM is applicable as an input for the 1D-2D flood modelling. The simulated water level in 2013 has NSE of 0.644 and RSME of 1.259. As a conclusion, ASTER-GDEM can be used as one alternative topography datasets for flood inundation modelling. However, the flood level obtained from the hydraulic modelling shows low accuracy at flat urban areas.

Dyadic social interaction of C57BL/6 mice versus interaction with a toy mouse: conditioned place preference/aversion, substrain differences, and no development of a hierarchy.

PubMed

Pinheiro, Barbara S; Seidl, Simon S; Habazettl, Eva; Gruber, Bernadette E; Bregolin, Tanja; Zernig, Gerald

2016-04-01

Impaired social interaction is a hallmark symptom of many psychiatric diseases, including dependence syndromes (substance use disorders). Helping the addict reorient her/his behavior away from the drug of abuse toward social interaction would be of considerable therapeutic benefit. To study the neural basis of such a reorientation, we have developed several animal models in which the attractiveness of a dyadic (i.e. one-to-one) social interaction (DSI) can be compared directly with that of cocaine as a prototypical drug of abuse. Our models are based on the conditioned place preference (CPP) paradigm. In an ongoing effort to validate our experimental paradigms in C57BL/6 mice to make use of the plethora of transgenic models available in this genus, we found the following: (a) DSI with a live mouse produced CPP, whereas an interaction with an inanimate mouse-like object (i.e. a 'toy mouse'; toy mouse interaction) led to conditioned place aversion - but only in the Jackson substrain (C57BL/6J). (b) In the NIH substrain (C57BL/6N), both DSI and toy mouse interaction produced individual aversion in more than 50% of the tested mice. (c) Four 15 min DSI episodes did not result in the development of an observable hierarchy, that is, dominance/subordination behavior in the overwhelming majority (i.e. 30 of 32) of the tested Jackson mouse pairs. Therefore, dominance/subordination does not seem to be a confounding variable in our paradigm, at least not in C57BL/6J mice. Respective data for NIH mice were too limited to allow any conclusion. The present findings indicate that (a) DSI with a live mouse produces CPP to a greater degree than an interaction with an inanimate object resembling a mouse and that (b) certain substrain differences with respect to CPP/aversion to DSI do exist between the Jax and NIH substrain of C57BL/6 mice. These differences have to be considered when choosing a proper mouse substrain model for investigating the neural basis of DSI reward versus drug reward.

The connection Between Plasma Protein Binding and Acute Toxicity as Determined by the LD50 Value.

PubMed

Svennebring, Andreas

2016-02-01

Preclinical Research A dataset of three drug classes (acids, bases, and neutrals) with LD50 values in mice was analysed to investigate a possible connection between high plasma protein binding and acute toxicity. Initially, it was found that high plasma protein binding was associated with toxicity for acids and neutrals, but after compensating for differences in lipophilicity, plasma protein binding was found not to be associated with toxicity. The therapeutic index established by the quotient between mouse LD50 and the defined daily dose was unaffected by both lipophilicity and plasma protein binding. © 2015 Wiley Periodicals, Inc.

Quantitative volumetric imaging of normal, neoplastic and hyperplastic mouse prostate using ultrasound.

PubMed

Singh, Shalini; Pan, Chunliu; Wood, Ronald; Yeh, Chiuan-Ren; Yeh, Shuyuan; Sha, Kai; Krolewski, John J; Nastiuk, Kent L

2015-09-21

Genetically engineered mouse models are essential to the investigation of the molecular mechanisms underlying human prostate pathology and the effects of therapy on the diseased prostate. Serial in vivo volumetric imaging expands the scope and accuracy of experimental investigations of models of normal prostate physiology, benign prostatic hyperplasia and prostate cancer, which are otherwise limited by the anatomy of the mouse prostate. Moreover, accurate imaging of hyperplastic and tumorigenic prostates is now recognized as essential to rigorous pre-clinical trials of new therapies. Bioluminescent imaging has been widely used to determine prostate tumor size, but is semi-quantitative at best. Magnetic resonance imaging can determine prostate volume very accurately, but is expensive and has low throughput. We therefore sought to develop and implement a high throughput, low cost, and accurate serial imaging protocol for the mouse prostate. We developed a high frequency ultrasound imaging technique employing 3D reconstruction that allows rapid and precise assessment of mouse prostate volume. Wild-type mouse prostates were examined (n = 4) for reproducible baseline imaging, and treatment effects on volume were compared, and blinded data analyzed for intra- and inter-operator assessments of reproducibility by correlation and for Bland-Altman analysis. Examples of benign prostatic hyperplasia mouse model prostate (n = 2) and mouse prostate implantation of orthotopic human prostate cancer tumor and its growth (n =  ) are also demonstrated. Serial measurement volume of the mouse prostate revealed that high frequency ultrasound was very precise. Following endocrine manipulation, regression and regrowth of the prostate could be monitored with very low intra- and interobserver variability. This technique was also valuable to monitor the development of prostate growth in a model of benign prostatic hyperplasia. Additionally, we demonstrate accurate ultrasound image-guided implantation of orthotopic tumor xenografts and monitoring of subsequent tumor growth from ~10 to ~750 mm(3) volume. High frequency ultrasound imaging allows precise determination of normal, neoplastic and hyperplastic mouse prostate. Low cost and small image size allows incorporation of this imaging modality inside clean animal facilities, and thereby imaging of immunocompromised models. 3D reconstruction for volume determination is easily mastered, and both small and large relative changes in volume are accurately visualized. Ultrasound imaging does not rely on penetration of exogenous imaging agents, and so may therefore better measure poorly vascularized or necrotic diseased tissue, relative to bioluminescent imaging (IVIS). Our method is precise and reproducible with very low inter- and intra-observer variability. Because it is non-invasive, mouse models of prostatic disease states can be imaged serially, reducing inter-animal variability, and enhancing the power to detect small volume changes following therapeutic intervention.

Capturing microRNA targets using an RNA-induced silencing complex (RISC)-trap approach

PubMed Central

Cambronne, Xiaolu A.; Shen, Rongkun; Auer, Paul L.; Goodman, Richard H.

2012-01-01

Identifying targets is critical for understanding the biological effects of microRNA (miRNA) expression. The challenge lies in characterizing the cohort of targets for a specific miRNA, especially when targets are being actively down-regulated in miRNA– RNA-induced silencing complex (RISC)–messengerRNA (mRNA) complexes. We have developed a robust and versatile strategy called RISCtrap to stabilize and purify targets from this transient interaction. Its utility was demonstrated by determining specific high-confidence target datasets for miR-124, miR-132, and miR-181 that contained known and previously unknown transcripts. Two previously unknown miR-132 targets identified with RISCtrap, adaptor protein CT10 regulator of kinase 1 (CRK1) and tight junction-associated protein 1 (TJAP1), were shown to be endogenously regulated by miR-132 in adult mouse forebrain. The datasets, moreover, differed in the number of targets and in the types and frequency of microRNA recognition element (MRE) motifs, thus revealing a previously underappreciated level of specificity in the target sets regulated by individual miRNAs. PMID:23184980