Temporal patterns of rat behaviour in the central platform of the elevated plus maze. Comparative analysis between male subjects of strains with different basal levels of emotionality

We have analyzed the temporal patterns of behaviour of male rats of the Wistar and DA/Han strains on the central platform of the elevated plus maze. The ethogram encompassed 10 behavioural elements. Durations, frequencies and latencies showed quantitative differences as to walking and sniffing activities. Wistar rats displayed significantly lower latency and significantly higher durations and frequencies of walking activities. DA/Han rats showed a significant increase of sniffing duration. In addition, DA/Han rats showed a significantly higher amount of time spent in the central platform. Multivariate T-pattern analysis revealed differences in the temporal organization of behaviour of the two rat strains. DA/Han rats showed (a) higher behavioural complexity and variability and (b) a significantly higher mean number of T-patterns than Wistar rats. Taken together, T-pattern analysis of behaviour in the centre of the elevated plus maze can noticeably improve the detection of subtle features of anxiety related behaviour. We suggest that T-pattern analysis could be used as sensitive tool to test the action of anxiolytic and anxiogenic manipulations.peer-reviewe

Quantification of abnormal repetitive behaviour in captive European starlings (Sturnus vulgaris).

Stereotypies are repetitive, unvarying and goalless behaviour patterns that are often considered indicative of poor welfare in captive animals. Quantifying stereotypies can be difficult, particularly during the early stages of their development when behaviour is still flexible. We compared two methods for objectively quantifying the development of route-tracing stereotypies in caged starlings. We used Markov chains and T-pattern analysis (implemented by the software package, Theme) to identify patterns in the sequence of locations a bird occupied within its cage. Pattern metrics produced by both methods correlated with the frequency of established measures of stereotypic behaviour and abnormal behaviour patterns counted from video recordings, suggesting that both methods could be useful for identifying stereotypic individuals and quantifying stereotypic behaviour. We discuss the relative benefits and disadvantages of the two approaches

Compositional Model based Fisher Vector Coding for Image Classification

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to depict the generation process of local features. However, the representative power of the GMM could be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes and the number of prototypes is usually small in FVC. To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions. Instead, we adopt a compositional mechanism which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as the linear combination of multiple key components and the combination weight is a latent random variable. In this way, we can greatly enhance the representative power of the generative model of FVC. To implement our idea, we designed two particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and Machine Intelligence (TPAMI

Exploring Context with Deep Structured models for Semantic Segmentation

State-of-the-art semantic image segmentation methods are mostly based on training deep convolutional neural networks (CNNs). In this work, we proffer to improve semantic segmentation with the use of contextual information. In particular, we explore `patch-patch' context and `patch-background' context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets including $NYUDv2$, $PASCAL$-$VOC2012$, $Cityscapes$, $PASCAL$-$Context$, $SUN$-$RGBD$, $SIFT$-$flow$, and $KITTI$ datasets. Particularly, we report an intersection-over-union score of $77.8$ on the $PASCAL$-$VOC2012$ dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine Intelligence, 2017. Extended version of arXiv:1504.0101

How Game Location Affects Soccer Performance: T-Pattern Analysis of Attack Actions in Home and Away Matches

The influence of game location on performance has been widely examined in sport contexts. Concerning soccer, game-location affects positively the secondary and tertiary level of performance; however, there are fewer evidences about its effect on game structure (primary level of performance). This study aimed to detect the effect of game location on a primary level of performance in soccer. In particular, the objective was to reveal the hidden structures underlying the attack actions, in both home and away matches played by a top club (Serie A 2012/2013 - First League). The methodological approach was based on observational design, supported by digital recordings and T-pattern analysis. Data were analyzed with Theme 6.0 software. A quantitative analysis, with nonparametric and descriptive statistics, was carried out to test the hypotheses. A qualitative analysis on complex patterns was performed to get in-depth information on the game structure. This study showed that game tactics were significantly different, with home matches characterized by a more structured and varied game than away matches. Theme software, and the corresponding T-pattern detection algorithm, enhance research opportunities by going further than frequency-based analyses, making this method an effective tool in supporting sport performance analysis and training

Acute nicotine induces anxiety and disrupts temporal pattern organization of rat exploratory behavior in hole-board : a potential role for the lateral habenula

Nicotine is one of the most addictive drugs of abuse. Tobacco smoking is a major cause of many health problems, and is the first preventable cause of death worldwide. Several findings show that nicotine exerts significant aversive as well as the well-known rewarding motivational effects. Less certain is the anatomical substrate that mediates or enables nicotine aversion. Here, we show that acute nicotine induces anxiogenic-like effects in rats at the doses investigated (0.1, 0.5, and 1.0 mg/kg, i.p.), as measured by the hole-board apparatus and manifested in behaviors such as decreased rearing and head-dipping and increased grooming. No changes in locomotor behavior were observed at any of the nicotine doses given. T-pattern analysis of the behavioral outcomes revealed a drastic reduction and disruption of complex behavioral patterns induced by all three nicotine doses, with the maximum effect for 1 mg/kg. Lesion of the lateral habenula (LHb) induced hyperlocomotion and, strikingly, reversed the nicotine-induced anxiety obtained at 1 mg/kg to an anxiolytic-like effect, as shown by T-pattern analysis. We suggest that the LHb is critically involved in emotional behavior states and in nicotine-induced anxiety, most likely through modulation of monoaminergic nuclei.peer-reviewe

Lateral habenula regulates temporal pattern organization of rat exploratory behavior and acute nicotine-induced anxiety in hole board

Nicotine is one of the most addictive drugs of abuse. Tobacco smoking is a major cause of many health problems worldwide, and is the first preventable cause of death. Several findings show that nicotine exerts significant aversive as well as the well-known rewarding motivational effects. Less certain is the anatomical substrate that mediates or enables nicotine aversion. Here we have focused on nicotine-induced anxiety-like behavior in unlesioned and lesioned lateral habenula (LHb) rats. Firstly, we showed that acute nicotine induces anxiogenic effects in rats at the doses investigated (0.1, 0.5, and 1.0 mg/kg, i.p.) as measured by the hole-board apparatus, and manifested in behaviors such as decreased rearing and head-dipping and increased grooming. No changes in locomotor behavior were observed at any of the nicotine doses given. T-pattern analysis of the behavioral outcomes revealed a drastic reduction and disruption of complex behavioral patterns induced by all three nicotine doses, with the maximum effect for 1 mg/kg. Lesion of the LHb induced a significant anxiogenic effect, reduced the mean occurrences of T-patterns detected, and strikingly reverted the nicotine-induced anxiety to an anxiolytic effect. We suggest that LHb is critically involved in emotional behavior states and in nicotine-induced anxiety, most likely through modulating serotonergic/dopaminergic nuclei.peer-reviewe

T-pattern analysis in soccer games: Relationship between time and attack actions

Sports performance consists of a multiple series of strategies that tend to follow one another. Performance analysis in team sports is usually focused on primary (fundamental skill execution), secondary (scoring) and tertiary (match result) outcomes. While there is general agreement over measuring secondary and tertiary outcomes, literature does not show a unanimous agreement over a unique measure of the primary level of performance. The aim of this study was to investigate primary performance outcomes through an analysis of temporal patterns. In particular, we were interested in verifying if changes in tertiary performance outcomes may be related to changes in primary ones. We selected three soccer matches played by a top club during the Serie A league over the 2012-2013 seasonin which there was a change in match result between *rst and second half (tertiary level of performance). The methodological approach was based on observational design, supported by digital recordings and computer analysis. Data were analyzed with theme 6 beta software, which detects the temporal and sequential structure of datasets, revealing repeated patterns that may regularly or irregularly occur within a period of observation (Tpatterns). Striking di+erences were found comparing first and second half temporal patterns, especially when the *nal match outcome showed an improvement of the first half ’s one. Our results suggest that 'eme software and T-pattern enhance research opportunities by identifying a useful tool to study the link between primary and tertiary level of performance, making this an e+ective research and support instrument for sports analysis

Behavioral Patterns Associated with Chemotherapy-Induced Emesis: A Potential Signature for Nausea in Musk Shrews

Nausea and vomiting are common symptoms in patients with many diseases, including cancer and its treatments. Although the neurological basis of vomiting is reasonably well known, an understanding of the physiology of nausea is lacking. The primary barrier to mechanistic research on the nausea system is the lack of an animal model. Indeed investigating the effects of anti-nausea drugs in pre-clinical models is difficult because the primary readout is often emesis. It is known that animals show a behavioral profile of sickness, associated with reduced feeding and movement, and possibly these general measures are signs of nausea. Studies attempting to relate the occurrence of additional behaviors to emesis have produced mixed results. Here we applied a statistical method, temporal pattern (t-pattern) analysis, to determine patterns of behavior associated with emesis. Musk shrews were injected with the chemotherapy agent cisplatin (a gold standard in emesis research) to induce acute (<24 h) and delayed (>24 h) emesis. Emesis and other behaviors were coded and tracked from video files. T-pattern analysis revealed hundreds of non-random patterns of behavior associated with emesis, including sniffing, changes in body contraction, and locomotion. There was little evidence that locomotion was inhibited by the occurrence of emesis. Eating, drinking, and other larger body movements including rearing, grooming, and body rotation, were significantly less common in emesis-related behavioral patterns in real versus randomized data. These results lend preliminary evidence for the expression of emesis-related behavioral patterns, including reduced ingestive behavior, grooming, and exploratory behaviors. In summary, this statistical approach to behavioral analysis in a pre-clinical emesis research model could be used to assess the more global effects and limitations of drugs used to control nausea and its potential correlates, including reduced feeding and activity levels

Structural analyses in the study of behavior: From rodents to non-human primates

The term "structure" indicates a set of components that, in relation to each other, shape an organic complex. Such a complex takes on essential connotations of functionally unitary entity resulting from the mutual relationships of its constituent elements. In a broader sense, we can use the word "structure" to define the set of relationships among the elements of an emergent system that is not determined by the mere algebraic sum of these elements, but by the interdependence relationships of these components from which the function of the entire structure itself derives. The behavior of an integrated living being can be described in structural terms via an ethogram, defined as an itemized list of behavioral units. Akin to an architectural structure, a behavioral structure arises from the reciprocal relationships that the individual units of behavior establish. Like an architectural structure, the function of the resulting behaving complex emerges from the relationships of the parts. Hence, studying behavior in its wholeness necessitates not only the identification of its constitutive units in their autarchic individuality, but also, and importantly, some understanding of their relationships. This paper aimed to critically review different methods to study behavior in structural terms. First, we emphasized the utilization of T-pattern analysis, i.e., one of the most effective and reliable tools to provide structural information on behavior. Second, we discussed the application of other methodological approaches that are based on the analysis of transition matrices, such as hierarchical clustering, stochastic analyses, and adjusted residuals. Unlike T-pattern analysis, these methods allow researchers to explore behavioral structure beyond its temporal characteristics and through other relational constraints. After an overview of how these methods are used in the study of animal behavior, from rodents to non-human primates, we discussed the specificities, advantages and challenges of each approach. This paper could represent a useful background for all scientists who intend to study behavior both quantitatively and structurally, that is in terms of the reciprocal relationships that the various units of a given behavioral repertoire normally weave together