scholarly journals Analysis of behaviour in the Active Allothetic Place Avoidance task based on cluster analysis of the rat movement motifs

2017 ◽  
Author(s):  
Tiago V. Gehring ◽  
Malgorzata J. Wesierska ◽  
Daniel K. Wójcik ◽  
Eleni Vasilaki
Keyword(s):  
Cluster Analysis ◽  
Performance Measures ◽  
Clustering Algorithm ◽  
Memory Performance ◽  
Reference Memory ◽  
Individual Performance ◽  
Avoidance Task ◽  
Standard Analysis ◽  
Place Avoidance ◽  
Freely Moving

AbstractThe Active Allothetic Place Avoidance test (AAPA) is a useful tool to study spatial memory in a dynamic world. In this task a rat, freely moving on a rotating circular arena, has to avoid a sector where shocks are presented. The standard analysis of memory performance in the AAPA task relies on evaluating individual performance measures. Here we present a new method of analysis for the AAPA test that focuses on the movement paths of the animals and utilizes a clustering algorithm to automatically extract the stereotypical types of behaviour as reflected in the recorded paths. We apply the method to experiments that study the effect of silver nanoparticles (AgNPs) on the reference memory and identify six major classes of movement motifs not previously described in AAPA tests. The method allows us to analyse the data with no prior expectations about the motion to be seen in the experiments.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

scholarly journals Improved well logs clustering algorithm for shale gas identification and formation evaluation

2021 ◽  
Author(s):  
N. P. Szabó ◽  
B. A. Braun ◽  
M. M. G. Abdelrahman ◽  
M. Dobróka
Keyword(s):  
Cluster Analysis ◽  
Shale Gas ◽  
Clustering Algorithm ◽  
Traditional Approach ◽  
Harmful Effect ◽  
Weighted Average ◽  
Well Logs ◽  
Organic Carbon Content ◽  
Wireline Logs ◽  
Most Frequent Value

AbstractThe identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

scholarly journals Risperidone and ritanserin but not haloperidol block effect of dizocilpine on the active allothetic place avoidance task

2008 ◽  
Vol 105 (3) ◽  
pp. 1061-1066 ◽  
Author(s):  
V. Bubenikova-Valesova ◽  
A. Stuchlik ◽  
J. Svoboda ◽  
J. Bures ◽  
K. Vales
Keyword(s):  
Avoidance Task ◽  
Place Avoidance ◽  
Block Effect

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals An Integrated Approach to Product Delivery Planning and Scheduling

2011 ◽  
Vol 45 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Galina Merkuryeva ◽  
Vitaly Bolshakov ◽  
Maksims Kornevs
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Expert Knowledge ◽  
Real Life ◽  
Integrated Approach ◽  
Classification Model ◽  
Performance Criteria ◽  
Planning And Scheduling ◽  
Delivery Planning ◽  
Product Delivery

An Integrated Approach to Product Delivery Planning and SchedulingProduct delivery planning and scheduling is a task of high priority in transport logistics. In distribution centres this task is related to deliveries of various types of goods in predefined time windows. In real-life applications the problem has different stochastic performance criteria and conditions. Optimisation of schedules itself is time consuming and requires an expert knowledge. In this paper an integrated approach to product delivery planning and scheduling is proposed. It is based on a cluster analysis of demand data of stores to identify typical dynamic demand patterns and product delivery tactical plans, and simulation optimisation to find optimal parameters of transportation or vehicle schedules. Here, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to find an optimal grouping of stores into regions based on their geographical locations and the total demand uniformly distributed over regions, a multiobjective optimisation problem is formulated and solved with the NSGA II algorithm.

scholarly journals Silver Nanoparticles Impair Cognitive Functions and Modify the Hippocampal Level of Neurotransmitters in a Coating-Dependent Manner

2021 ◽  
Vol 22 (23) ◽  
pp. 12706
Author(s):  
Katarzyna Dziendzikowska ◽  
Małgorzata Węsierska ◽  
Joanna Gromadzka-Ostrowska ◽  
Jacek Wilczak ◽  
Michał Oczkowski ◽  
...  
Keyword(s):  
Silver Nanoparticles ◽  
Spatial Memory ◽  
Antibacterial Properties ◽  
Coating Material ◽  
Long Term Memory ◽  
Avoidance Task ◽  
Dependent Manner ◽  
Memory Functioning ◽  
Place Avoidance ◽  
The Brain

Due to their potent antibacterial properties, silver nanoparticles (AgNPs) are widely used in industry and medicine. However, they can cross the brain–blood barrier, posing a risk to the brain and its functions. In our previous study, we demonstrated that oral administration of bovine serum albumin (BSA)-coated AgNPs caused an impairment in spatial memory in a dose-independent manner. In this study, we evaluated the effects of AgNPs coating material on cognition, spatial memory functioning, and neurotransmitter levels in rat hippocampus. AgNPs coated with BSA (AgNPs(BSA)), polyethylene glycol (AgNPs(PEG)), or citrate (AgNPs(Cit)) or silver ions (Ag+) were orally administered at a dose of 0.5 mg/kg b.w. to male Wistar rats for a period of 28 days, while the control (Ctrl) rats received 0.2 mL of water. The acquisition and maintenance of spatial memory related to place avoidance were assessed using the active allothetic place avoidance task, in which rats from AgNPs(BSA), AgNPs(PEG), and Ag+ groups performed worse than the Ctrl rats. In the retrieval test assessing long-term memory, only rats from AgNPs(Cit) and Ctrl groups showed memory maintenance. The analysis of neurotransmitter levels indicated that the ratio between serotonin and dopamine concentration was disturbed in the AgNPs(BSA) rats. Furthermore, treatment with AgNPs or Ag+ resulted in the induction of peripheral inflammation, which was reflected by the alterations in the levels of serum inflammatory mediators. In conclusion, depending on the coating material used for their stabilization, AgNPs induced changes in memory functioning and concentration of neurotransmitters.

scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

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