Acute pancreatitis (AP) and recurring AP are serious health care problems causing excruciating pain and potentially lethal outcomes due to sepsis. The validated caerulein- (CAE) induced mouse model of acute/recurring AP produces secondary persistent hypersensitivity and anxiety-like behavioral changes for study.

To determine efficacy of acetyl-L-carnitine (ALC) to reduce pain-related behaviors and brain microglial activation along the pain circuitry in CAE-pancreatitis.

Pancreatitis was induced with 6 hly intraperitoneal (i.p.) injections of CAE (50 µg/kg), 3 d a week for 6 wk in male C57BL/6J mice. Starting in week 4, mice received either vehicle or ALC until experiment’s end. Mechanical hyper-sensitivity was assessed with von Frey filaments. Heat hypersensitivity was determined with the hotplate test. Anxiety-like behavior was tested in week 6 using elevated plus maze and open field tests. Microglial activation in brain was quantified histologically by immunostaining for ionized calcium-binding adaptor molecule 1 (Iba1).

Mice with CAE-induced pancreatitis had significantly reduced mechanical withdrawal thresholds and heat response latencies, indicating ongoing pain. Treatment with ALC attenuated inflammation-induced hypersensitivity, but hypersensitivity due to abdominal wall injury caused by repeated intraperitoneal injections persisted. Animals with pancreatitis displayed spontaneous anxiety-like behavior in the elevated plus maze compared to controls. Treatment with ALC resulted in increased numbers of rearing activity events, but time spent in “safety” was not changed. After all the abdominal injections, pancreata were translucent if excised at experiment’s end and opaque if excised on the subsequent day, indicative of spontaneous healing. Post mortem histopathological analysis performed on pancreas sections stained with Sirius Red and Fast Green identified wide-spread fibrosis and acinar cell atrophy in sections from mice with CAE-induced pancreatitis that was not rescued by treatment with ALC. Microglial Iba1 immunostaining was significantly increased in hippocampus, thalamus (intralaminar nuclei), hypothalamus, and amygdala of mice with CAE-induced pancreatitis compared to naïve controls but unchanged in the primary somatosensory cortex compared to naïves.

CAE-induced pancreatitis caused increased pain-related behaviors, pancreatic fibrosis, and brain microglial changes. ALC alleviated CAE-induced mechanical and heat hypersensitivity but not abdominal wall injury-induced hypersensitivity caused by the repeated injections.

• Acute recurrent pancreatitis
• Neuropathic pain
• Mechanical hypersensitivity
• Heat hypersensitivity
• Anxiety-like behavior
• Ionized calcium-binding adaptor molecule 1

• Acetylcarnitine
• Acute Disease
• Animals
• Brain
• Ceruletide/toxicity
• Disease Models, Animal
• Male
• Mice
• Mice, Inbred C57BL
• Microglia
• Pain
• Pancreas
• Pancreatitis/chemically induced
• Pancreatitis/drug therapy

Tau assemblies have prion‐like properties: they propagate from one neuron to another and amplify by seeding the aggregation of endogenous Tau. Although key in prion‐like propagation, the binding of exogenous Tau assemblies to the plasma membrane of naïve neurons is not understood. We report that fibrillar Tau forms clusters at the plasma membrane following lateral diffusion. We found that the fibrils interact with the Na⁺/K⁺‐ATPase (NKA) and AMPA receptors. The consequence of the clustering is a reduction in the amount of α3‐NKA and an increase in the amount of GluA2‐AMPA receptor at synapses. Furthermore, fibrillar Tau destabilizes functional NKA complexes. Tau and α‐synuclein aggregates often co‐exist in patients’ brains. We now show evidences for cross‐talk between these pathogenic aggregates with α‐synuclein fibrils dramatically enhancing fibrillar Tau clustering and synaptic localization. Our results suggest that fibrillar α‐synuclein and Tau cross‐talk at the plasma membrane imbalance neuronal homeostasis.

• cross‐talk of pathogenic proteins
• misfolding disease
• protein aggregation and clustering
• single‐particle tracking
• tauopathies

Intraocular pressure (IOP) is the primary risk factor for developing glaucoma, yet little is known about the contribution of genomic background to IOP regulation. The present study leverages an array of systems genetics tools to study genomic factors modulating normal IOP in the mouse. The BXD recombinant inbred (RI) strain set was used to identify genomic loci modulating IOP. We measured the IOP in a total of 506 eyes from 38 different strains. Strain averages were subjected to conventional quantitative trait analysis by means of composite interval mapping. Candidate genes were defined, and immunohistochemistry and quantitative PCR (qPCR) were used for validation. Of the 38 BXD strains examined the mean IOP ranged from a low of 13.2mmHg to a high of 17.1mmHg. The means for each strain were used to calculate a genome wide interval map. One significant quantitative trait locus (QTL) was found on Chr.8 (96 to 103 Mb). Within this 7 Mb region only 4 annotated genes were found: Gm15679, Cdh8, Cdh11 and Gm8730. Only two genes (Cdh8 and Cdh11) were candidates for modulating IOP based on the presence of non-synonymous SNPs. Further examination using SIFT (Sorting Intolerant From Tolerant) analysis revealed that the SNPs in Cdh8 (Cadherin 8) were predicted to not change protein function; while the SNPs in Cdh11 (Cadherin 11) would not be tolerated, affecting protein function. Furthermore, immunohistochemistry demonstrated that CDH11 is expressed in the trabecular meshwork of the mouse. We have examined the genomic regulation of IOP in the BXD RI strain set and found one significant QTL on Chr. 8. Within this QTL, there is one good candidate gene, Cdh11.

• BXD
• IOP
• cadherin
• eye
• glaucoma
• intraocular pressure
• mouse
• quantitative trait mapping

• C57BL/6
• cerebellum
• foliation
• heterotopia
• malformation

The corpus callosum is the main commissure connecting left and right cerebral hemispheres, and varies widely in size. Differences in the midsagittal area of the corpus callosum (MSACC) have been associated with a number of cognitive and behavioral phenotypes, including obsessive-compulsive disorders, psychopathy, suicidal tendencies, bipolar disorder, schizophrenia, autism, and attention deficit hyperactivity disorder. Although there is evidence to suggest that MSACC is heritable in normal human populations, there is surprisingly little evidence concerning the genetic modulation of this variation. Mice provide a potentially ideal tool to dissect the genetic modulation of MSACC. Here, we use a large genetic reference panel – the BXD recombinant inbred line – to dissect the natural variation of the MSACC. We estimated the MSACC in over 300 individuals from nearly 80 strains. We found a 4-fold difference in MSACC between individual mice, and a 2.5-fold difference among strains. MSACC is a highly heritable trait (h² = 0.60), and we mapped a suggestive QTL to the distal portion of Chr 14. Using sequence data and neocortical expression databases, we were able to identify eight positional and plausible biological candidate genes within this interval. Finally, we found that MSACC correlated with behavioral traits associated with anxiety and attention.

• BXD
• QTL
• corpus callosum
• midsagittal area
• mouse
• neocortex

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, ‘digital mRNA counting’ is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.

• Animals
• Corpus Striatum/metabolism
• Gene Expression/genetics
• Gene Expression Profiling/methods
• High-Throughput Nucleotide Sequencing
• Mice
• Mice, Inbred C57BL
• Mice, Inbred DBA
• Oligonucleotide Array Sequence Analysis/methods
• Polymorphism, Single Nucleotide/genetics
• Sequence Analysis, RNA/methods

• AA011114 NIAAA NIH HHS
• R01 AA011114 NIAAA NIH HHS
• R01 DA005228 NIDA NIH HHS
• P50 AA010760 NIAAA NIH HHS
• AA013484 NIAAA NIH HHS
• T32 AA007468 NIAAA NIH HHS
• T32AA07468 NIAAA NIH HHS
• 5T15LM007088 NLM NIH HHS
• R29 AA011114 NIAAA NIH HHS
• 5P30CA069533-13 NCI NIH HHS
• U01 AA013484 NIAAA NIH HHS
• T32DA07262 NIDA NIH HHS
• UL1 RR024140 NCRR NIH HHS
• AA010760 NIAAA NIH HHS
• MH051372 NIMH NIH HHS
• T32 DA007262 NIDA NIH HHS
• R01 AA011034 NIAAA NIH HHS
• AA011034 NIAAA NIH HHS
• R01 MH051372 NIMH NIH HHS
• T15 LM007088 NLM NIH HHS
• DA005228 NIDA NIH HHS
• P60 AA010760 NIAAA NIH HHS
• P30 CA069533 NCI NIH HHS
• 5UL1RR024140 NCRR NIH HHS

Serial section electron microscopy (ssEM) is rapidly expanding as a primary tool to investigate synaptic circuitry and plasticity. The ultrastructural images collected through ssEM are content rich and their comprehensive analysis is beyond the capacity of an individual laboratory. Hence, sharing ultrastructural data is becoming crucial to visualize, analyze, and discover the structural basis of synaptic circuitry and function in the brain. We devised a web-based management system called SynapticDB (http://synapses.clm.utexas.edu/synapticdb/) that catalogues, extracts, analyzes, and shares experimental data from ssEM. The management strategy involves a library with check-in, checkout and experimental tracking mechanisms. We developed a series of spreadsheet templates (MS Excel, Open Office spreadsheet, etc) that guide users in methods of data collection, structural identification, and quantitative analysis through ssEM. SynapticDB provides flexible access to complete templates, or to individual columns with instructional headers that can be selected to create user-defined templates. New templates can also be generated and uploaded. Research progress is tracked via experimental note management and dynamic PDF forms that allow new investigators to follow standard protocols and experienced researchers to expand the range of data collected and shared. The combined use of templates and tracking notes ensures that the supporting experimental information is populated into the database and associated with the appropriate ssEM images and analyses. We anticipate that SynapticDB will serve future meta-analyses towards new discoveries about the composition and circuitry of neurons and glia, and new understanding about structural plasticity during development, behavior, learning, memory, and neuropathology.

Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.

The MouseBIRN Atlasing Toolkit (MBAT) project was developed as a cross-platform, free open-source application that unifies and accelerates the digital atlas workflow. A tiered, plug-in architecture was designed for the neuroinformatics and genomics goals of the project to provide a modular and extensible design. MBAT provides the ability to use a single query to search and retrieve data from multiple data sources, align image data using the user's preferred registration method, composite data from multiple sources in a common space, and link relevant informatics information to the current view of the data or atlas. The workspaces leverage tool plug-ins to extend and allow future extensions of the basic workspace functionality. A wide variety of tool plug-ins were developed that integrate pre-existing as well as newly created technology into each workspace. Novel atlasing features were also developed, such as supporting multiple label sets, dynamic selection and grouping of labels, and synchronized, context-driven display of ontological data.

MBAT empowers researchers to discover correlations among disparate data by providing a unified environment for bringing together distributed reference resources, a user's image data, and biological atlases into the same spatial or semantic context. Through its extensible tiered plug-in architecture, MBAT allows researchers to customize all platform components to quickly achieve personalized workflows.

• gene expression
• heritability
• sex differences
• systems genetics

• Alcohol Drinking/genetics
• Animals
• Behavior, Animal
• Body Weight
• Cocaine-Related Disorders/genetics
• Habituation, Psychophysiologic/genetics
• Handling, Psychological
• Housing, Animal
• Mice
• Mice, Inbred C57BL
• Mice, Inbred DBA
• Morphine Dependence/genetics
• Organ Size
• Phenotype
• RNA, Messenger/genetics
• Recombination, Genetic
• Substance-Related Disorders/genetics

• U01 AA016662 NIAAA NIH HHS
• U01 AA013509 NIAAA NIH HHS
• DA020677 NIDA NIH HHS
• R01 DA020677 NIDA NIH HHS
• AA016662 NIAAA NIH HHS
• R01 DA021198 NIDA NIH HHS
• U01 AA016669-03 NIAAA NIH HHS
• U01 AA013499 NIAAA NIH HHS
• U01 AA013509-06 NIAAA NIH HHS
• AA13509 NIAAA NIH HHS
• U01 AA016669 NIAAA NIH HHS
• R01 DA020677-04 NIDA NIH HHS
• DA021198 NIDA NIH HHS
• AA13499 NIAAA NIH HHS
• U01 AA016662-03 NIAAA NIH HHS
• R01 DA021198-03 NIDA NIH HHS

The size of the cerebral cortex varies widely within human populations, and a large portion of this variance is modulated by genetic factors. The discovery and characterization of these genes and their variants can contribute to an understanding of individual differences in brain development, behavior, and disease susceptibility. Here we use unbiased stereological techniques to map quantitative trait loci (QTLs) that modulate the volume of neocortex.

We estimated volumes bilaterally in an expanded set of BXD recombinant inbred strains (n = 56 strains and 223 animals) taken from the Mouse Brain Library . We generated matched microarray data for the cerebral cortex in the same large panel of strains and in parental neonates to efficiently nominate and evaluate candidate genes. Volume of the neocortex varies widely, and is a heritable trait. Genome-wide mapping of this trait revealed two QTLs – one on chromosome (Chr) 6 at 88 ± 5 Mb and another at Chr 11 (41 ± 8 Mb). We generated both neonatal and adult neocortical gene expression databases using microarray technology. Using these databases in combination with other bioinformatic tools we have identified positional candidates on these QTL intervals.

This study is the first to use the expanded set of BXD strains to map neocortical volume, and we found that normal variation of this trait is, at least in part, genetically modulated. These results provide a baseline from which to assess the genetic contribution to regional variation in neocortical volume, as well as other neuroanatomic phenotypes that may contribute to variation in regional volume, such as proliferation, death, and number and packing density of neurons

• 3D reconstruction
• atlas
• automated segmentation
• brain model
• neuroanatomy
• registration
• spatial normalization
• standard coordinate space

• P20 MH062009 NIMH NIH HHS

Digital brain atlases are useful as references, analytical tools, and as a data integration framework. As a result, they and their supporting tools are being recognized as potentially useful resources in the movement toward data sharing. Several projects are connecting infrastructure to these tools which facilitate sharing, managing, and retrieving data of different types, scale, and even location. With these in place, we have the ability to combine, analyze, and interpret these data in a manner not previously possible, opening the door to examine issues in new and exciting ways, and potentially leading to speedier discovery of answers as well as new questions about the brain. Here we discuss recent efforts in the use of digital mouse atlases for data sharing.

• anatomical framework
• atlas
• brain
• data management
• gene expression
• mouse
• neuroinformatics

Neuroinformatics seeks to create and maintain web-accessible databases of experimental and computational data, together with innovative software tools, essential for understanding the nervous system in its normal function and in neurological disorders. Neuroinformatics includes traditional bioinformatics of gene and protein sequences in the brain; atlases of brain anatomy and localization of genes and proteins; imaging of brain cells; brain imaging by positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and other methods; many electrophysiological recording methods; and clinical neurological data, among others. Building neuroinformatics databases and tools presents difficult challenges because they span a wide range of spatial scales and types of data stored and analyzed. Traditional bioinformatics, by comparison, focuses primarily on genomic and proteomic data (which of course also presents difficult challenges). Much of bioinformatics analysis focus on sequences (DNA, RNA, and protein molecules), as the type of data that are stored, compared, and sometimes modeled. Bioinformatics is undergoing explosive growth with the addition, for example, of databases that catalog interactions between proteins, of databases that track the evolution of genes, and of systems biology databases which contain models of all aspects of organisms. This commentary briefly reviews neuroinformatics with clarification of its relationship to traditional and modern bioinformatics.

• between
• comparison
• difference
• overlap
• similarity
• understand

• Animals
• Behavior, Animal/physiology
• Brain/metabolism
• Brain/pathology
• Brain/physiopathology
• Female
• Gene Expression
• Gliosis/metabolism
• Gliosis/pathology
• Gliosis/physiopathology
• Homozygote
• Male
• Mice
• Mice, Inbred C57BL
• Mice, Inbred Strains
• Mice, Transgenic
• Motor Activity/genetics
• Motor Activity/physiology
• Mutation
• Prion Diseases/genetics
• Prion Diseases/pathology
• Prion Diseases/physiopathology
• Prions/genetics
• Prions/metabolism
• Prions/physiology
• Time Factors

• N01AI25491 NIAID NIH HHS
• N01 AI25491 NIAID NIH HHS
• R01 NS/AI40334 NIAID NIH HHS
• R01 NS040334-04 NINDS NIH HHS
• R01 NS040334 NINDS NIH HHS

• surface reconstruction
• contours
• implicit models
• multi-level partition of unity
• normal estimation

• P20 MH062009 NIMH NIH HHS
• U24 RR021760 NCRR NIH HHS
• U24 RR021760-02 NCRR NIH HHS

• Animals
• Brain/anatomy & histology
• Brain/physiology
• Brain Mapping
• Database Management Systems
• Diagnostic Imaging/methods
• Image Processing, Computer-Assisted
• Information Storage and Retrieval
• Rodentia
• Software
• Software Design

Three dimensional biomedical image sets are becoming ubiquitous, along with the canonical atlases providing the necessary spatial context for analysis. To make full use of these 3D image sets, one must be able to present views for 2D display, either surface renderings or 2D cross-sections through the data. Typical display software is limited to presentations along one of the three orthogonal anatomical axes (coronal, horizontal, or sagittal). However, data sets precisely oriented along the major axes are rare. To make fullest use of these datasets, one must reasonably match the atlas' orientation; this involves resampling the atlas in planes matched to the data set. Traditionally, this requires the atlas and browser reside on the user's desktop; unfortunately, in addition to being monolithic programs, these tools often require substantial local resources. In this article, we describe a network-capable, client-server framework to slice and visualize 3D atlases at off-axis angles, along with an open client architecture and development kit to support integration into complex data analysis environments.

Here we describe the basic architecture of a client-server 3D visualization system, consisting of a thin Java client built on a development kit, and a computationally robust, high-performance server written in ANSI C++. The Java client components (NetOStat) support arbitrary-angle viewing and run on readily available desktop computers running Mac OS X, Windows XP, or Linux as a downloadable Java Application. Using the NeuroTerrain Software Development Kit (NT-SDK), sophisticated atlas browsing can be added to any Java-compatible application requiring as little as 50 lines of Java glue code, thus making it eminently re-useable and much more accessible to programmers building more complex, biomedical data analysis tools. The NT-SDK separates the interactive GUI components from the server control and monitoring, so as to support development of non-interactive applications. The server implementation takes full advantage of data center's high-performance hardware, where it can be co-localized with centrally-located, 3D dataset repositories, extending access to the researcher community throughout the Internet.

The combination of an optimized server and modular, platform-independent client provides an ideal environment for viewing complex 3D biomedical datasets, taking full advantage of high-performance servers to prepare images and subsets of associated meta-data for viewing, as well as the graphical capabilities in Java to actually display the data.

• anatomy
• atlas
• neonatal
• probabilistic

• Age Factors
• Anatomy, Artistic
• Animals
• Animals, Newborn
• Atlases as Topic
• Brain/anatomy & histology
• Brain/pathology
• Computational Biology
• Databases, Factual
• Disease Models, Animal
• Epilepsy/pathology
• Image Processing, Computer-Assisted
• Imaging, Three-Dimensional/methods
• Magnetic Resonance Imaging
• Male
• Medical Illustration
• Mice/anatomy & histology
• Mice, Inbred C57BL
• Mice, Mutant Strains
• Models, Anatomic
• Terminology as Topic

• P41 RR013642 NCRR NIH HHS
• 5R01 MH61223 NIMH NIH HHS
• P41 RR13642 NCRR NIH HHS

• Amygdala/anatomy & histology
• Amygdala/physiology
• Animals
• Chromosome Mapping
• Genetic Variation
• Genotype
• Mice
• Mice, Inbred Strains
• Phenotype
• Polymorphism, Single Nucleotide
• Quantitative Trait Loci

• P20-DA 21131 NIDA NIH HHS
• U01AA13499 NIAAA NIH HHS
• U01CA105417 NCI NIH HHS
• U01NR 105417 NINR NIH HHS
• U24AA13513 NIAAA NIH HHS

• Animals
• Automation
• Brain/anatomy & histology
• Brain/cytology
• Brain/pathology
• Brain Mapping/methods
• Cerebellum/physiology
• Female
• Humans
• Image Processing, Computer-Assisted
• Mice
• Mice, Inbred C57BL
• Mice, Transgenic
• Microscopy
• Prions/genetics
• Promoter Regions, Genetic
• Substantia Nigra/physiology
• User-Computer Interface
• beta-Galactosidase/genetics

• Animals
• Body Size/genetics
• Brain/anatomy & histology
• Brain/growth & development
• Cerebral Cortex/anatomy & histology
• Cerebral Cortex/growth & development
• Chromosome Mapping
• Crosses, Genetic
• Female
• Genetic Variation/genetics
• Linkage Disequilibrium
• Male
• Mice
• Mice, Inbred C57BL/genetics
• Mice, Inbred DBA/genetics
• Multifactorial Inheritance/genetics
• Organ Size/genetics
• Phenotype
• Polymorphism, Single Nucleotide
• Quantitative Trait Loci

• K07 MH001953 NIMH NIH HHS
• 1K07MH01953 NIMH NIH HHS
• DC/8424559 NIDCD NIH HHS

• Age Factors
• Animals
• Animals, Newborn
• Brain Mapping
• Cell Size
• Dendrites/physiology
• Image Processing, Computer-Assisted/methods
• Lysine/analogs & derivatives
• Lysine/metabolism
• Mice
• Models, Neurological
• Motor Neurons/cytology
• Spinal Cord/cytology
• Spinal Cord/growth & development

• R01 NS39600 NINDS NIH HHS

• Animals
• Brain/physiology
• Computational Biology/methods
• Computational Biology/trends
• Database Management Systems
• Databases, Factual
• Electroencephalography/methods
• Electroencephalography/trends
• Humans
• Information Dissemination/methods
• Information Storage and Retrieval/methods
• Information Storage and Retrieval/standards
• Internet
• Medical Records Systems, Computerized/standards
• Neurosciences/methods
• Neurosciences/trends
• Research/standards
• Research/trends
• Research Design
• Systems Integration
• United States

• Animals
• Brain Mapping/methods
• Cerebral Cortex/anatomy & histology
• Cerebral Cortex/physiology
• Mice
• Mice, Inbred C57BL
• Mice, Inbred DBA
• Predictive Value of Tests
• Species Specificity

• Animals
• Atlases as Topic
• Computational Biology
• Computer Graphics
• Databases, Factual
• Embryo, Mammalian
• Gene Expression
• Image Processing, Computer-Assisted
• Information Storage and Retrieval
• Mice
• Models, Anatomic
• Online Systems
• Programming Languages

• MC_U127527203 Medical Research Council
• HD39928-02 NICHD NIH HHS

• Animals
• Chromosome Mapping
• Computational Biology
• Computers, Molecular
• Databases, Factual
• Gene Expression
• Genetic Linkage
• Genotype
• Humans
• Information Storage and Retrieval/methods
• Mice
• Mice, Inbred Strains/genetics
• Neurosciences
• Oligonucleotide Array Sequence Analysis
• Online Systems
• Phenotype
• Quantitative Trait Loci
• Recombination, Genetic

• P20-MH 62009 NIMH NIH HHS

• Brain
• Brain Mapping
• Cellular Structures/metabolism
• Computational Biology
• Databases, Factual
• Image Processing, Computer-Assisted/methods
• Information Storage and Retrieval
• Internet
• Microscopy/methods
• Microscopy, Electron
• National Library of Medicine (U.S.)
• Online Systems/organization & administration
• Proteins/metabolism
• United States
• Workforce

• DC03192 NIDCD NIH HHS
• RR04050 NCRR NIH HHS
• RR043050-S2 NCRR NIH HHS
• RR08605 NCRR NIH HHS
• RR08605-08S1 NCRR NIH HHS