Automated discrimination of psychotropic drugs in mice via computer vision-based analysis

Cataloged from PDF version of article.We developed an inexpensive computer vision-based method utilizing an algorithm which differentiates drug-induced behavioral alterations. The mice were observed in an open-field arena and their activity was recorded for 100 min. For each animal the first 50 min of observation were regarded as the drug-free period. Each animal was exposed to only one drug and they were injected (i.p.) with either amphetamine or cocaine as the stimulant drugs or morphine or diazepam as the inhibitory agents. The software divided the arena into virtual grids and calculated the number of visits (sojourn counts) to the grids and instantaneous speeds within these grids by analyzing video data. These spatial distributions of sojourn counts and instantaneous speeds were used to construct feature vectors which were fed to the classifier algorithms for the final step of matching the animals and the drugs. The software decided which of the animals were drug-treated at a rate of 96%. The algorithm achieved 92% accuracy in sorting the data according to the increased or decreased activity and then determined which drug was delivered. The method differentiated the type of psychostimulant or inhibitory drugs with a success ratio of 70% and 80%, respectively. This method provides a new way to automatically evaluate and classify drug-induced behaviors in mice. Crown Copyright © 2009 Published by Elsevier B.V. All rights reserved

Social group behaviour of triads. Dependence on purpose and gender

We analysed a set of uninstructed pedestrian trajectories automatically tracked in a public area, and we asked a human coder to assess their group relationships. For those pedestrians who belong to the groups, we asked the coder to identify their apparent purpose of visit to the tracking area and apparent gender. We studied the quantitative dependence of the group dynamics on such properties in the case of triads (three people groups) and compared them to the two pedestrian group case (dyads), studied in a previous work. We found that the group velocity strongly depends on relation and gender for both triads and dyads, while the influence of these properties on spatial structure of groups is less clear in the triadic case. We discussed the relevance of these results to the modelling of pedestrian and crowd dynamics, and examined the possibility of the future works on this subject

Intrinsic group behaviour II: On the dependence of triad spatial dynamics on social and personal features; and on the effect of social interaction on small group dynamics

In a follow-up to our work on the dependence of walking dyad dynamics on intrinsic properties of the group, we now analyse how these properties affect groups of three people (triads), taking also in consideration the effect of social interaction on the dynamical properties of the group. We show that there is a strong parallel between triads and dyads. Work-oriented groups are faster and walk at a larger distance between them than leisure-oriented ones, while the latter move in a less ordered way. Such differences are present also when colleagues are contrasted with friends and families; nevertheless the similarity between friend and colleague behaviour is greater than the one between family and colleague behaviour. Male triads walk faster than triads including females, males keep a larger distance than females, and same gender groups are more ordered than mixed ones. Groups including tall people walk faster, while those with elderly or children walk at a slower pace. Groups including children move in a less ordered fashion. Results concerning relation and gender are particularly strong, and we investigated whether they hold also when other properties are kept fixed. While this is clearly true for relation, patterns relating gender often resulted to be diminished. For instance, the velocity difference due to gender is reduced if we compare only triads in the colleague relation. The effects on group dynamics due to intrinsic properties are present regardless of social interaction, but socially interacting groups are found to walk in a more ordered way. This has an opposite effect on the space occupied by non-interacting dyads and triads, since loss of structure makes dyads larger, but causes triads to lose their characteristic V formation and walk in a line (i.e., occupying more space in the direction of movement but less space in the orthogonal one)

Artificial Neural Network Based Audio Reinforcement for Computer Assisted Rote Learning

The dual-channel assumption of the cognitive theory of multimedia learning suggests that importing a large amount of information through a single (visual or audio) channel overloads that channel, causing partial loss of information, while importing it simultaneously through multiple channels relieves the burden on them and leads to the registration of a larger amount of information. In light of such knowledge, this study investigates the possibility of reinforcing visual stimuli with audio for supporting e-learners in memorization tasks. Specifically, we consider three kinds of learning material and two kinds of audio stimuli and partially reinforce each kind of material with each kind of stimuli in an arbitrary way. In a series of experiments, we determine the particular type of audio, which offers the highest improvement for each kind of material. Our work stands out as being the first study investigating the differences in memory performance in relation to different combinations of learning content and stimulus. Our key findings from the experiments are: (i) E-learning is more effective in refreshing memory rather than studying from scratch, (ii) Non-informative audio is more suited to verbal content, whereas informative audio is better for numerical content, (iii) Constant audio triggering degrades learning performance and thus audio triggering should be handled with care. Based on these findings, we develop an ANN-based estimator to determine the proper moment for triggering audio (i.e. when memory performance is estimated to be declining) and carry out follow-up experiments for testing the integrated framework. Our contributions involve (i) determination of the most effective audio for each content type, (ii) estimation of memory deterioration based on learners' interaction logs, and (iii) the proposal of improvement of memory registration through auditory reinforcement. We believe that such findings constitute encouraging evidence the memory registration of e-learners can be enhanced with content-aware audio incorporation

Neg/pos-Normalized Accuracy Measures for Software Defect Prediction

In evaluating the performance of software defect prediction models, accuracy measures such as precision and recall are commonly used. However, most of these measures are affected by neg/pos ratio of the data set being predicted, where neg is the number of negative cases (defect-free modules) and pos is the number of positive cases (defective modules). Thus, it is not fair to compare such values across different data sets with different neg/pos ratios and it may even lead to misleading or contradicting conclusions. The objective of this study is to address the class imbalance issue in assessing performance of defect prediction models. The proposed method relies on computation of expected values of accuracy measures based solely on the value of the neg and pos values of the data set. Based on the expected values, we derive the neg/pos-normalized accuracy measures, which are defined as their divergence from the expected value divided by the standard deviation of all possible prediction outcomes. The proposed measures enable us to provide a ranking of predictions across different data sets, which can distinguish between successful predictions and unsuccessful predictions. Our results derived from a case study of defect prediction based on 19 defect data sets indicate that ranking of predictions is significantly different than the ranking of conventional accuracy measures such as precision and recall as well as composite measures F1-value, AUC of ROC, MCC, G-mean and Balance. In addition, we conclude that MCC attains a better defect prediction accuracy than F1-value, AUC of ROC, G-mean and Balance

Estimating social relation from trajectories

This study focuses on social pedestrian groups in public spaces and makes an effort to identify the social relation between the group members. We particularly consider dyads having coalitional or mating relation. We derive several observables from individual and group trajectories, which are suggested to be distinctive for these two sorts of relations and propose a recognition algorithm taking these observables as features and yielding an estimation of social relation in a probabilistic manner at every sampling step. On the average, we detect coalitional relation with 87% and mating relation with 81% accuracy. To the best of our knowledge, this is the first study to infer social relation from joint (loco)motion patterns and we consider the detection rates to be a satisfactory considering the inherent challenge of the problem

Macroscopic and microscopic dynamics of a pedestrian cross-flow: Part I, experimental analysis

In this work we investigate the behaviour of a human crowd in a cross-flow by analysing the results of a set of controlled experiments in which subjects were divided into two groups, organised in such a way to explore different density settings, and asked to walk through the crossing area. We study the results of the experiment by defining and investigating a few macroscopic and microscopic observables. Along with analysing traditional indicators such as density and velocity, whose dynamics was, to the extent of our knowledge, poorly understood for this setting, we pay particular attention to walking and body orientation, studying how these microscopic observables are influenced by density. Furthermore, we report a preliminary but quantitative analysis on the emergence of self-organising patterns (stripes) in the crossing area, a phenomenon that had been previously qualitatively reported for human crowds, and reproduced in models, but whose quantitative analysis with respect to density conditions is, again according to our knowledge, a novel contribution

Identification of social relation within pedestrian dyads

This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories

A pure number to assess “congestion” in pedestrian crowds

The development of technologies for reliable tracking of pedestrian trajectories in public spaces has recently enabled collecting large data sets and real-time information about the usage of urban space and indoor facilities by human crowds. Such an information, nevertheless, may be properly used only with the aid of theoretical and computational tools to assess the state of the crowd. As shown in this work, traditional assessment metrics such as density and flow may provide only a partial information, since it is also important to understand how “regular” these flows are, as spatially uniform flows are arguably less problematic than strongly fluctuating ones. Recently, the Congestion Level (CL), based on the computation of spatial variation of the rotor of the crowd velocity field, has been proposed as an assessment metric to evaluate the state of the crowd. Nevertheless, the definition was lacking sound theoretical foundations and, more importantly, was of very difficult interpretation (it was difficult to understand “what” was measuring). As we believe that such theoretical shortcomings were limiting also its relevance to applied studies, in this work we clarify some aspects concerning the definition, and we show that such an assessment metric may be improved by defining a dimensionless Congestion Number (CN). As a first application of the newly defined indicator we first focus on the cross-flow scenario and, by using discrete and continuous toy models, idealised “limit scenarios”, more realistic simulations and finally data from experiments with human participants, we show that corresponds to a crowd with a regular and safe motion (even in high density and high flow settings), while indicates the emergence of a congested and possibly dangerous condition. We finally use the indicator to analyse and discuss different settings such as bottlenecks, uni-, bi- and multi-directional flows, and real-world data concerning the movement of pedestrians in the world’s busiest railway station

Tumor growth suppression induced by biomimetic silk fibroin hydrogels

Protein-based hydrogels with distinct conformations which enable encapsulation or differentiation of cells are of great interest in 3D cancer research models. Conformational changes may cause macroscopic shifts in the hydrogels, allowing for its use as biosensors and drug carriers. In depth knowledge on how 3D conformational changes in proteins may affect cell fate and tumor formation is required. Thus, this study reports an enzymatically crosslinked silk fibroin (SF) hydrogel system that can undergo intrinsic conformation changes from random coil to β-sheet conformation. In random coil status, the SF hydrogels are transparent, elastic, and present ionic strength and pH stimuli-responses. The random coil hydrogels become β-sheet conformation after 10 days in vitro incubation and 14 days in vivo subcutaneous implantation in rat. When encapsulated with ATDC-5 cells, the random coil SF hydrogel promotes cell survival up to 7 days, whereas the subsequent β-sheet transition induces cell apoptosis in vitro. HeLa cells are further incorporated in SF hydrogels and the constructs are investigated in vitro and in an in vivo chick chorioallantoic membrane model for tumor formation. In vivo, Angiogenesis and tumor formation are suppressed in SF hydrogels. Therefore, these hydrogels provide new insights for cancer research and uses of biomaterials.The authors would like to thank the Portuguese Foundation for Science and Technology (FCT) project grants OsteoCart (PTDC/CTM-BPC/115977/2009) and Tissue2Tissue (PTDC/CTM/105703/2008) which supported this study. Research leading to these results has also received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no REGPOT-CT2012-316331-POLARIS. Le-Ping Yan was awarded a PhD scholarship from FCT (SFRH/BD/64717/2009). We also would like to thank FCT for the distinction attributed to J.M. Oliveira under the Investigador FCT program (IF/00423/2012). The authors also like to acknowledge Dr. Mariana B. Oliveira for technical assistance on the dynamic mechanical analysis of the cell-laden hydrogels