Co-location Patterns Research Articles

The chemical coding of 5-hydroxytryptamine containing enteroendocrine cells in the mouse gastrointestinal tract.

The majority of 5-HT (serotonin) in the body is contained in enteroendocrine cells of the gastrointestinal mucosa. From the time of their discovery over 80years ago, the 5-HT-containing cells have been regarded as a class of cell that is distinct from enteroendocrine cells that contain peptide hormones. However, recent studies have cast doubt on the concept of there being distinct classes of enteroendocrine cells, each containing a single hormone or occasionally more than one hormone. Instead, data are rapidly accumulating that there are complex patterns of colocalisation of hormones that identify multiple subclasses of enteroendocrine cells. In the present work, multiple labelling immunohistochemistry is used to investigate patterns of colocalisation of 5-HT with enteric peptide hormones. Over 95% of 5-HT cells in the duodenum also contained cholecystokinin and about 40% of them also contained secretin. In the jejunum, about 75% of 5-HT cells contained cholecystokinin but not secretin and 25% contained 5-HT plus both cholecystokinin and secretin. Small proportions of 5-HT cells contained gastrin or somatostatin in the stomach, PYY or GLP-1 in the small intestine and GLP-1 or somatostatin in the large intestine. Rare or very rare 5-HT cells contained ghrelin (stomach), neurotensin (small and large intestines), somatostatin (small intestine) and PYY (in the large intestine). It is concluded that 5-HT-containing enteroendocrine cells are heterogeneous in their chemical coding and by implication in their functions.

Computation Analysis for Finding Co-Location Patterns using Map-Reduce Framework

Objectives: The main objectives of the paper are 1. Generating the Neib_tree based on the number of features and instances. 2. Finding all the co-location patterns using Parallel Approach. 3. Improving the computation time by exploring Map-Reduce Framework. Methods: To generate Neib-tree is by Grid based approach. The method used find co-location patterns is by parallel approach which drastically increases the time complexity. The exploratory results are directed by utilizing manufactured information sets by taking the different data sets one with 25k, 50k and 75k features and an average of 20k instances each, which produces the computational analysis with a distance of 20km. Findings: This paper presents fast calculation of co-location patterns where these is helpful in finding the people suffering from a particular problem in a place and what are the patterns affecting the problem. The proposed work diminishes the calculation time by 1/n terms where n is the quantity of components as it uses a Map-Reduce system. This paper presents exact and fulfillment of the new approach. At long last, exploratory assessments utilizing manufactured information sets demonstrate the calculation is computationally more productive. Applications: The concept presented in this paper is helpful in different areas like medical Field, NASA, and etc., Improvements: The paper improves the time complexity and space complexity by using parallel join-less approach.

Computation Analysis for Finding Co–Location Patterns using Map–Reduce Framework

Objectives: The main objectives of the paper are 1. Generating the Neib_tree based on the number of features and instances. 2. Finding all the co-location patterns using Parallel Approach. 3. Improving the computation time by exploring Map-Reduce Framework. Methods: To generate Neib-tree is by Grid based approach. The method used find co-location patterns is by parallel approach which drastically increases the time complexity. The exploratory results are directed by utilizing manufactured information sets by taking the different data sets one with 25k, 50k and 75k features and an average of 20k instances each, which produces the computational analysis with a distance of 20km. Findings: This paper presents fast calculation of co-location patterns where these is helpful in finding the people suffering from a particular problem in a place and what are the patterns affecting the problem. The proposed work diminishes the calculation time by 1/n terms where n is the quantity of components as it uses a Map-Reduce system. This paper presents exact and fulfillment of the new approach. At long last, exploratory assessments utilizing manufactured information sets demonstrate the calculation is computationally more productive. Applications: The concept presented in this paper is helpful in different areas like medical Field, NASA, and etc., Improvements: The paper improves the time complexity and space complexity by using parallel join-less approach.

Evaluating cognitive models of visual word recognition using fMRI: Effects of lexical and sublexical variables

In this study predictions of the dual-route cascaded (DRC) model of word reading were tested using fMRI. Specifically, patterns of co-localization were investigated: (a) between pseudoword length effects and a pseudowords vs. fixation contrast, to reveal the sublexical grapho-phonemic conversion (GPC) system; and (b) between word frequency effects and a words vs. pseudowords contrast, to reveal the orthographic and phonological lexicon. Forty four native speakers of Greek were scanned at 3T in an event-related lexical decision task with three event types: (a) 150 words in which frequency, length, bigram and syllable frequency, neighborhood, and orthographic consistency were decorrelated; (b) 150 matched pseudowords; and (c) fixation. Whole-brain analysis failed to reveal the predicted co-localizations. Further analysis with participant-specific regions of interest defined within masks from the group contrasts revealed length effects in left inferior parietal cortex and frequency effects in the left middle temporal gyrus. These findings could be interpreted as partially consistent with the existence of the GPC system and phonological lexicon of the model, respectively. However, there was no evidence in support of an orthographic lexicon, weakening overall support for the model. The results are discussed with respect to the prospect of using neuroimaging in cognitive model evaluation.

Spatial Co-location Patterns of Aerospace Industry Firms in Mexico

The Aerospace Industry (AI) is considered strategic in Mexico due to the opportunities it offers Mexican business communities to insert themselves into a global value chain of high competitive standards. Due to its production specificities, it needs to develop a chain of suppliers that may lead to externalities or intentional knowledge transfer and the creation of networks with local economies and business co-locations. This paper aims to investigate patterns of co-location of firms and establishments around the AI across Mexico. The analysis applies spatial statistical techniques to detect spatial agglomerations of different industrial sectors related to the AI. The findings include a detailed description of the spatial distribution of AI co-location patterns in terms of industrial branch and firm size. Results indicate that the AI industry is mainly spatially co-located by itself and by industries in the electronics, machinery and equipment sectors. Our findings could potentially provide input to policy makers in terms of clustering and public policies according to regionally productive vocations.

Multidimensional Circlet Lattice based Approach to Mine Spatially Co-Located Patterns

With immense extent of applications, one of the important tasks in data mining is the spatial pattern identification. Discovering spatial co-location patterns presents challenges as spatial objects are embedded in a continuous space. For co-location pattern mining, previous studies often emphasize on the association analysis of every spatial feature. As a result, interesting patterns involving events with different frequency cannot be captured. In this paper, we study the problem of efficiently mining co-location patterns with lattice structure called, Multi-dimensional Circlet Lattice-based Spatial Co-location Pattern Mining (MCL-SCPM), frequently located together in spatial proximity. MCL-SCPM initially identifies the candidate co-location using feature inclusive ratio which incorporates the spatial co-location patterns at minimum time interval with maximum coverage ratio. Next, MCL-SCPM discovers co-location patterns in a multi-dimensional spatial structure for different movements of an object by measuring the cohesion of a pattern. To extract maximal co-located patterns, MCL-SCPM method finally, uses Circlet Lattice-based structure to extract maximal co-located patterns. We conduct an extensive performance study to test and evaluate the effectiveness of MCL-SCPM method. Our experimental results show that MCL-SCPM method minimizes the execution time for mining co-location patterns without producing large computational overheads.

Parallel Approach for Finding Co-location Pattern – A Map Reduce Framework

Spatial co-location pattern mining is a sub field of data mining which is used to discover interesting patterns which are expressed as co-location rules. The objects that are frequently located in certain region are expressed as spatial co-locations. It presents a challenge for finding co-location patterns as the traditional data is considered discrete whereas the spatial objects are embedded in a continuous space. For this a join-less approach is proposed, but as the data size increases, a large amount of computation time is devoted to find co-location rules as the approach is purely sequential. We propose a parallelized join-less approach which finds the spatial neighbor relationship in order to identify co-location instances and co-location rules. The proposed work decreases the computation time drastically as it uses a Map-Reduce framework. This paper presents precise and completeness of the new approach. Finally, an experimental evaluations using synthetic data sets show the algorithm is computationally more efficient.

Measuring the effect of transportation infrastructure on retail firm co-location patterns

This paper studies the effects of transportation infrastructure on the location behavior of competing retail firms. First, in a game theoretic framework that describes the location choices made by retailers with respect to each other, the connection between relative firm location and transportation demand is outlined. Second, a multivariate point pattern statistic based on this theoretical framework is applied to location patterns of fast food outlets to measure the effects that major road infrastructure has on firm location behavior in this sector.The results suggest that transportation infrastructure affects the location behavior of firms with respect to their location in relation to their competitors. Access to important transportation infrastructure induces competing outlets to locate next to each other—a tendency not necessarily observed among outlets without such access. Theoretically, this does not only affect economic outcomes, but also travel demand and consumer accessibility. The findings from this study stress the importance of accounting for how transportation infrastructure changes the location behavior of firms in travel demand frameworks.

Cocaine- and amphetamine-regulated transcript and calcium binding proteins immunoreactivity in the subicular complex of the guinea pig

In this study we present the distribution and colocalization pattern of cocaine- and amphetamine-regulated transcript (CART) and three calcium-binding proteins: calbindin (CB), calretinin (CR) and parvalbumin (PV) in the subicular complex (SC) of the guinea pig. The subiculum (S) and presubiculum (PrS) showed higher CART-immunoreactivity (-IR) than the parasubiculum (PaS) as far as the perikarya and neuropil were concerned. CART- IR cells were mainly observed in the pyramidal layer and occasionally in the molecular layer of the S. In the PrS and PaS, single CART-IR perikarya were dispersed, however with a tendency to be found only in superficial layers. CART-IR fibers were observed throughout the entire guinea pig subicular neuropil. Double-labeling immunofluorescence showed that CART-IR perikarya, as well as fibers, did not stain positively for any of the three CaBPs. CART-IR fibers were only located near the CB-, CR-, PV-IR perikarya, whereas CART-IR fibers occasionally intersected fibers containing one of the three CaBPs. The distribution pattern of CART was more similar to that of CB and CR than to that of PV. In the PrS, the CART, CB and CR immunoreactivity showed a laminar distribution pattern. In the case of the PV, this distribution pattern in the PrS was much less prominent than that of CART, CB and CR. We conclude that a heterogeneous distribution of the CART and CaBPs in the guinea pig SC is in keeping with findings from other mammals, however species specific differences have been observed.

Spatial co-location pattern mining for location-based services in road networks

With the evolution of geographic information capture and the emergency of volunteered geographic information, it is getting more important to extract spatial knowledge automatically from large spatial datasets. Spatial co-location patterns represent the subsets of spatial features whose objects are often located in close geographic proximity. Such pattern is one of the most important concepts for geographic context awareness of location-based services (LBS). In the literature, most existing methods of co-location mining are used for events taking place in a homogeneous and isotropic space with distance expressed as Euclidean, while the physical movement in LBS is usually constrained by a road network. As a result, the interestingness value of co-location patterns involving network-constrained events cannot be accurately computed. In this paper, we propose a different method for co-location mining with network configurations of the geographical space considered. First, we define the network model with linear referencing and refine the neighborhood of traditional methods using network distances rather than Euclidean ones. Then, considering that the co-location mining in networks suffers from expensive spatial-join operation, we propose an efficient way to find all neighboring object pairs for generating clique instances. By comparison with the previous approaches based on Euclidean distance, this approach can be applied to accurately calculate the probability of occurrence of a spatial co-location on a network. Our experimental results from real and synthetic data sets show that the proposed approach is efficient and effective in identifying co-location patterns which actually rely on a network.

EFFECT OF DIPHTHERIA TOXIN T-DOMAIN ON ENDOSOMAL pH.

A key step in the mode of cytotoxic action of diphtheria toxin (DT) is the transfer of its catalytic domain (Cd) from endosomes into the cytosol. The main activity in this process is performed by the transport domain (Td), but the molecular mechanism of its action remains unknown. We have previously shown that Td can have some influence on the endosomal transport of DT The aim of this work was to study the effect of diphtheria toxin on the toxin compartmentalization in the intracellular transporting pathway and endosomal pH. We used recombinant fragments of DT which differed only by the presence of Td in their structure, fused with fluorescent proteins. It was shown that the toxin fragment with Td moved slower by the pathway early-late endosomes-lysosomes, and had a slightly different pattern of colocalization with endosomal markers than DT fragment without Td. In addition, endosomes containing DT fragments with Td had a constant pH of about 6.5 from the 10th to 50th minute of observation, for the same time endosomes containing DT fragments without Td demonstrated a decrease in pH from 6.3 to 5.5. These results indicate that Td inhibits acidification of endosomal medium. One of possible explanations for this may be the effect of the ion channel formed by the T-domain on the process of the endosomal acidification. This property of Td may not only inhibit maturation of endosomes but also inhibit activation of endosomal pH-dependent proteases, and this promotes successful transport of Cd into the cell cytosol.

Spatial Relationship and Colocation of Crimes in Jefferson County, Kentucky

Crime is a major social issue in U.S. cities. Understanding the spatial pattern of crimes is imperative for developing informed policy and effective policing strategies that will reduce crime in local communities. This article examines the spatial distribution of major crimes in Jefferson County, Kentucky. Using crime data spanning the period between 2009 and 2011, this study looked into the spatial relationships and colocation patterns among different types of crime across the county. Specifically we tried to uncover which crime types tend to colocate with each other and where the colocations exist in the county. More than thirty types of crime (excluding sex crimes) were first reclassified into ten categories, and aggregated into each of the 575 census block groups. Principal component analysis was used at the census block group level to extract components from reclassified crime data to identify the categories of crime that are heavily loaded together and therefore share similar location patterns. Four components were extracted and named contraband, violent, property, and theft. Component scores for each component were mapped and analyzed for census block groups, which helps reveal spatial colocation patterns of crimes in Jefferson County. Temporal changing in crime colocation patterns from 2009 to 2011 was also examined in this study.

The RAGE receptor and its ligands are highly expressed in astrocytes in a grade-dependant manner in the striatum and subependymal layer in Huntington's disease.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease caused by an expansion of the CAG repeat in the huntingtin gene. One of the brain changes that occurs in HD is the expression of the receptor for advanced glycation end products (RAGE), a receptor protein capable of activating multiple signalling pathways by interacting with a range of ligands leading to either beneficial or harmful effects to the cell. Here, we demonstrate in human HD brains a high degree of co-localization of RAGE with its putative ligands S100B and N-carboxymethyllysine (CML) in the caudate nucleus (CN) and the subependymal layer (SEL). The level of co-staining for both RAGE-S100B and RAGE-CML was the highest in the astrocytes but was low in neurons and microglia. The immunostaining for RAGE, S100B and CML extended in a medio-lateral (SEL-CN) direction with increasing grade, such that any change in the expression and co-localization pattern between grades was less prominent in the lateral CN. Additionally, signalling molecules that are downstream of RAGE activation showed changes in their activation status in HD brains. A larger number of RAGE-positive astrocytic cells had NF-kB translocated to the nucleus and the level of phospho-ERK1/2 was also increased in HD brains. Interestingly, the level of mDia-1, that interacts directly with the cytoplasmic domain of RAGE, decreased in HD. Overall, the results suggest a correlation between the functions of RAGE and the HD pathology, but the influence of RAGE on astrocytes and the impact of this on HD progression requires further study. RAGE (receptor for advanced glycation end products) binds multiple types of ligand to produce either neurotrophic or neurotoxic effects. Immunohistochemical staining of HD human brains showed that both RAGE and its ligands were expressed primarily in astrocytes. The pattern of staining corresponded to the grade and region-wise pattern of neurodegeneration suggesting a possible role for RAGE in HD pathology.

Parallel GPU-based Plane-Sweep Algorithm for Construction of iCPI-Trees

This article tackles the problem of efficient construction of iCPI trees, frequently used in co-location pattern discovery in spatial databases. It discusses the methods for parallelization of iCPI-tree construction and plane-sweep algorithms used in state-of-the-art algorithms for co-location pattern mining. The main contribution of this paper is threefold: (1) a general algorithm for parallel iCPI-tree construction is presented, (2) two variants of parallel plane-sweep algorithm (which can be used in conjunction with the aforementioned iCPI-tree construction algorithm) are introduced and (3) all three algorithms are implemented on CUDA GPU platform and their performance is tested against an efficient multithreaded parallel implementation of iCPI-tree construction on CPU. Experiments prove that our solutions allow for large speedups over CPU version of the algorithm. This paper is an extension of the conference paper (Andrzejewski & Boinski, 2014).

Defining clusters of related industries

Clusters are geographic concentrations of industries related by knowledge, skills, inputs, demand and/or other linkages. There is an increasing need for cluster-based data to support research, facilitate comparisons of clusters across regions and support policymakers in defining regional strategies. This article develops a novel clustering algorithm that systematically generates and assesses sets of cluster definitions (i.e., groups of closely related industries). We implement the algorithm using 2009 data for U.S. industries (six-digit NAICS), and propose a new set of benchmark cluster definitions that incorporates measures of inter-industry linkages based on co-location patterns, input–output links, and similarities in labor occupations. We also illustrate the algorithm’s ability to compare alternative sets of cluster definitions by evaluating our new set against existing sets in the literature. We find that our proposed set outperforms other methods in capturing a wide range of inter-industry linkages, including the grouping of industries within the same three-digit NAICS.

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST’s functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST’s general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

Abstract 611: AP-1 and ETS Family Transcription Factors Co-localize at Enhancers in Human Aortic Endothelial Cells

Introduction: Sequence-specific transcription factors bind DNA regulatory elements and play a key role in establishing cellular identity. Studies comparing macrophages to B cells have revealed that small numbers of such collaborative or lineage-determining transcription factors (LDTF) establish distinct enhancers in each cell type. These factors also allow for the binding of signal dependent transcription factors. Here we present data which suggest members of the AP-1, ETS, and STAT transcription factor families serve as collaborative transcriptional regulators in human aortic endothelial cells (HAEC). Hypothesis: We hypothesize that a set of AP-1 and ETS transcription factors collaborate to establish key endothelial cell enhancers. Methods: Working in HAEC, we measured poised and active enhancers using ChIP-seq for the epigenetic histone modifications H3K4me2 and H3K27Ac, performed motif analysis, and measured transcription factor binding for candidate factors. Knockdowns of JUN, ERG, and STAT3 followed by RNA-seq were used to evaluate altered enhancer function and gene targets of candidate factors. Results: Our de novo motif analysis revealed that motifs for ETS and AP-1 transcription factors are highly enriched at HAEC enhancers. ChIP-seq experiments for JUN, JUNB, ERG, and STAT3 showed between 8,000 and 55,000 intergenic peaks for each factor. Together these peaks bind 50% of poised enhancers, with a subset co-localizing at these sites. Gene ontology analysis showed that gene targets of these enhancers are involved in endothelial-specific functions. Further, knockdown of JUN, ERG, and STAT3 resulted in a twofold or greater change in expression of hundreds of HAEC transcripts. Conclusion: The genome-wide pattern of JUN, JUNB, ERG, and STAT3 co-localization at enhancers in HAEC suggests these factors serve as key regulators that collaboratively modulate endothelial-specific gene expression. Further investigation of candidate lineage-determining transcription factors using pro-atherogenic signals could reveal regulatory mechanisms of disease-relevant endothelial transcriptional programs.

Galanin is Co-Expressed with Substance P, Calbindin and Corticotropin-Releasing Factor (CRF) in The Enteric Nervous System of the Wild Boar (Sus scrofa) Small Intestine.

Galanin is a neuropeptide widely present in the enteric nervous system of numerous animal species and exhibiting neurotransmittery/neuromodulatory roles. Colocalization patterns of galanin with substance P (SP), corticotropin-releasing factor (CRF) and calbindin were studied in the small intestine of the wild boar using immunofluorescence technique. We demonstrated the presence of SP in substantial populations of galanin-immunoreactive (IR) submucous neurons. Additionally, different amounts of nerve fibres exhibiting simultaneous presence of galanin and SP were noted in the small intestinal smooth musculature, submucous ganglia, lamina muscularis mucosae and mucosa. In the wild boar duodenum, jejunum and ileum, the co-expression of galanin and calbindin was limited to minor populations of submucous neurons only. Single galanin-/CRF-IR nerve fibres were exclusively present in the duodenal and jejunal (but not ileal) mucosa. These results strongly suggest that galanin participates in neuronal control of the wild boar small intestine also by functional co-operation with other biologically active neuropeptides.

The TRPM8 Protein Is a Testosterone Receptor: I. BIOCHEMICAL EVIDENCE FOR DIRECT TRPM8-TESTOSTERONE INTERACTIONS

The transient receptor potential ion channel of the melastatin subfamily, TRPM8, is a major cold receptor in the peripheral nervous system. Along with the sensory neurons, the TRPM8 protein is highly expressed in the prostate epithelial cells, and this expression is regulated by androgens. Here we investigated the expression and intracellular localization of the TRPM8 channel in relationship to androgens. We performed experiments using human prostate tissues obtained from healthy individuals and patients with prostate cancer at various stages of the disease as well as in cultured cells. Using an immunohistochemistry approach, we detected an intensive colocalization pattern of the TRPM8 protein with endogenous androgens in all tissues tested, suggesting possible interactions. Co-immunoprecipitation experiments performed using cultured prostate epithelial cells, prostate cancer cells, and HEK-293 cells stably expressing TRPM8 further confirmed direct binding of the steroid hormone, testosterone, to the TRPM8 protein. Applications of picomolar concentrations of testosterone to the primary human prostate cells, endogenously expressing TRPM8, elicited Ca(2+) responses and channel currents, and those were inhibited in the presence of TRPM8 antagonist, N-(2-aminoethyl)-N-(4-(benzyloxy)-3-methoxybenzyl)thiophene-2-carboxamide hydrochloride. These results indicate that the TRPM8 channel is physically associated with testosterone and suggest that, in addition to a genomic role, testosterone plays a role in direct regulation of the TRPM8 channel function.

Impact of repeated asenapine treatment on FosB/ΔFosB expression in neurons of the rat central nucleus of the amygdala: colocalization with corticoliberine (CRH) and effect of an unpredictable mild stress preconditioning.

FosB/ΔFosB expression in the central amygdalar nucleus (CeA) in response to repeated asenapine (ASE) treatment (an atypical antipsychotic used for the treatment of schizophrenia) was studied in normal rats and rats preconditioned with chronic unpredictable variable mild stress (CMS). The goal of this study was to reveal whether repeated ASE treatment for 14 days may: 1) induce FosB/ΔFosB expression in the amygdala, 2) activate CRH-synthesizing neurons in the CeA, and 3) interfere with 21 days lasting concomitant CMS preconditioning. Four groups of animals were studied: controls and ASE-, CMS-, and CMS+ASE-treated ones. CMS consisted of the restrain, social isolation, crowding, swimming, and cold and lasted 21 days. The ASE and CMS+ASE groups were from the 7th day of the experiment treated with ASE (0.3 mg/kg, subcutaneously - s.c.) twice a day, i.e. together for 14 days. Controls and CMS groups were treated with saline (300 µl/rat, s.c.) twice a day for 14 days. All the animals were sacrificed on the 22nd day, i.e. 16-18 hours after the last treatments. Single FosB/ΔFosB, FosB/ΔFosB colocalizations with CRH, and CRH immunolabeled perikarya were investigated in the CeA using a combined light and fluorescent immunohistochemistry. The distribution aspect of the black FosB/ΔFosB profiles was homogeneous over the whole CeA and no significant differences in the number of FosB/ΔFosB profiles between the individual groups of the rats really occurred. The level of colocalization pattern of FosB/ΔFosB in CRH perikarya was also very similar between the individual groups and in each case it reached approximately 10% of double-labeling. No differences were also seen in the number of CRH immunolabeled perikarya. The density of CRH nerve projections within the CeA was very alike in the individual groups of animals investigated. The study provides a new anatomical/functional finding about the lack of the stimulatory effect of the repeated ASE treatment on the expression of FosB/ΔFosB, FosB/ΔFosB/CRH colocalizations, and CRH immunolabeled perikarya number in the CeA. In addition, CMS preconditioning itself neither stimulated nor inhibited FosB/ΔFosB expression, nor altered the impact of ASE on the activity of CRH neurons in the CeA.