In search of Vygotsky's blocks : Exploring CEV, BIK, MUR, and LAG in South Africa

This research exercise aimed to replicate use of the instrument (“Vygotsky’s Blocks”) of Vygotsky and Sakharov (1928-1934) in combination with the 22 wooden blocks and the later adaptations and scoring framework of Hanfmann and Kasanin (1937; 1942). This procedure – the functional method of double stimulation – examined new concept formation from early childhood to adulthood (N=60 subjects, aged three- to 76-years-old) to establish whether contemporary adults and children produced the same or similar patterns as those described by Vygotsky (1986). The study found a developmental trend consistent with Vygotsky’s (1986) writings on the ontogenesis of concept formation. The path from the syncretic, to the concrete and factual, to the intermediate phase before true conceptual thought becomes possible was reflected in a positive correlation between the age of the subjects and their modes of thinking. This verified Vygotsky’s assertion that true conceptual thinking only becomes possible in adolescence. This study aimed to encourage further research with this procedure to confirm the trends found by this study and to validate the adapted scoring method of Hanfmann and Kasanin (1942) for the purposes of cross-sectional use

LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

LifeCLEF 2015: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridging the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g. eBird, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipments have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching the real world’s requirements. The LifeCLEF lab proposes to evaluate these challenges around three tasks related to multimedia information retrieval and fine-grained classification problems in three living worlds. Each task is based on large and real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders in order to reflect realistic usage scenarios. This paper presents more particularly the 2014 edition of LifeCLEF, i.e. the pilot one. For each of the three tasks, we report the methodology and the datasets as well as the official results and the main outcomes

Composing first species counterpoint with a variable neighbourhood search algorithm

In this article, a variable neighbourhood search (VNS) algorithm is developed that can generate musical fragments consisting of a melody for the cantus firmus and the first species counterpoint. The objective function of the algorithm is based on a quantification of existing rules for counterpoint. The VNS algorithm developed in this article is a local search algorithm that starts from a randomly generated melody and improves it by changing one or two notes at a time. A thorough parametric analysis of the VNS reveals the significance of the algorithm's parameters on the quality of the composed fragment, as well as their optimal settings. A comparison of the VNS algorithm with a developed genetic algorithm shows that the VNS is more efficient. The VNS algorithm has been implemented in a user-friendly software environment for composition, called Optimuse. Optimuse allows a user to specify a number of characteristics such as length, key and mode. Based on this information, Optimuse 'composes' both cantus firmus and first species counterpoint. Alternatively, the user may specify a cantus firmus, and let Optimuse compose the accompanying first species counterpoint. © 2012 Taylor & Francis

An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

<p>Abstract</p> <p>Background</p> <p>Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from <it>Escherichia coli</it>. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between <it>Escherichia coli </it>and <it>Chlamydia trachomatis </it>are large enough to recommend an organism-specific modeling effort.</p> <p>Results</p> <p>Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model <it>Chlamydia trachomatis </it>σ⁶⁶promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for <it>Chlamydia trachomatis </it>RNA polymerase σ⁶⁶/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability.</p> <p>Conclusion</p> <p>This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new <it>Chlamydia trachomatis </it>σ⁶⁶promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.</p

Listening to ecosystems: data-rich acoustic monitoring through landscape-scale sensor networks

Ecologists have many ways to measure and monitor ecosystems, each of which can reveal details about the processes unfolding therein. Acoustic recording combined with machine learning methods for species detection can provide remote, automated monitoring of species richness and relative abundance. Such recordings also open a window into how species behave and compete for niche space in the sensory environment. These opportunities are associated with new challenges: the volume and velocity of such data require new approaches to species identification and visualization. Here we introduce a newly-initiated acoustic monitoring network across the subtropical island of Okinawa, Japan, as part of the broader OKEON (Okinawa Environmental Observation Network) project. Our aim is to monitor the acoustic environment of Okinawa’s ecosystems and use these space–time data to better understand ecosystem dynamics. We present a pilot study based on recordings from five field sites conducted over a one-month period in the summer. Our results provide a proof of concept for automated species identification on Okinawa, and reveal patterns of biogenic vs. anthropogenic noise across the landscape. In particular, we found correlations between forest land cover and detection rates of two culturally important species in the island soundscape: the Okinawa Rail and Ruddy Kingfisher. Among the soundscape indices we examined, NDSI, Acoustic Diversity and the Bioacoustic Index showed both diurnal patterns and differences among sites. Our results highlight the potential utility of remote acoustic monitoring practices that, in combination with other methods can provide a holistic picture of biodiversity. We intend this project as an open resource, and wish to extend an invitation to researchers interested in scientific collaboration

BLOMAP: An Encoding Of Amino Acids Which Improves Signal Peptide Cleavage Site Prediction

Research on cleavage site prediction for signal peptides has focused mainly on the application of different classification algorithms to achieve improved prediction accuracies. This paper addresses the fundamental issue of amino acid encoding to present amino acid sequences in the most beneficial way for machine learning algorithms. A comparison of several standard encoding methods shows, that for cleavage site prediction the frequently used orthonormal encoding is inferior compared to other methods. The best results are achieved with a new encoding method named BLOMAP – based on the BLOSUM62 substitution matrix – using a Naïve Bayes classifier