Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire

Vertebrate nervous systems can generate a remarkable diversity of behaviors. However, our understanding of how behaviors may have evolved in the chordate lineage is limited by the lack of neuroethological studies leveraging our closest invertebrate relatives. Here, we combine high-throughput video acquisition with pharmacological perturbations of bioamine signaling to systematically reveal the global structure of the motor behavioral repertoire in the Ciona intestinalis larvae. Most of Ciona’s postural variance can be captured by 6 basic shapes, which we term “eigencionas.” Motif analysis of postural time series revealed numerous stereotyped behavioral maneuvers including “startle-like” and “beat-and-glide.” Employing computational modeling of swimming dynamics and spatiotemporal embedding of postural features revealed that behavioral differences are generated at the levels of motor modules and the transitions between, which may in part be modulated by bioamines. Finally, we show that flexible motor module usage gives rise to diverse behaviors in response to different light stimuli.publishedVersio

Social Media Analytics in Social CRM – Towards a Research Agenda

Social Media have emerged as an additional source of information for companies. Regarding an analysis of the huge data volumes within the Social Web, other approaches than manually analyzing social content are needed. Thus, Social Media Analytics (SMA) applications have emerged in recent years and have become inevitable for automatically generating valuable in-sights. However, these tools still suffer different shortcomings, which inhibit a deeper analysis and understanding of data. This research investigates and categorizes currently available ana-lytics methods by outlining literature and analyzing practical applications. Furthermore, it draws a line between descriptive, predictive, and prescriptive analytics in the field of Social Media Analytics. As a result, this research complements existing research with strategic ques-tions, possible outcomes of SMA applications, and enabling methods to compute these out-comes, and finally defines a research agenda

Frequent Pattern Finding in Integrated Biological Networks

Biomedical research is undergoing a revolution with the advance of high-throughput technologies. A major challenge in the post-genomic era is to understand how genes, proteins and small molecules are organized into signaling pathways and regulatory networks. To simplify the analysis of large complex molecular networks, strategies are sought to break them down into small yet relatively independent network modules, e.g. pathways and protein complexes. In fulfillment of the motivation to find evolutionary origins of network modules, a novel strategy has been developed to uncover duplicated pathways and protein complexes. This search was first formulated into a computational problem which finds frequent patterns in integrated graphs. The whole framework was then successfully implemented as the software package BLUNT, which includes a parallelized version. To evaluate the biological significance of the work, several large datasets were chosen, with each dataset targeting a different biological question. An application of BLUNT was performed on the yeast protein-protein interaction network, which is described. A large number of frequent patterns were discovered and predicted to be duplicated pathways. To explore how these pathways may have diverged since duplication, the differential regulation of duplicated pathways was studied at the transcriptional level, both in terms of time and location. As demonstrated, this algorithm can be used as new data mining tool for large scale biological data in general. It also provides a novel strategy to study the evolution of pathways and protein complexes in a systematic way. Understanding how pathways and protein complexes evolve will greatly benefit the fundamentals of biomedical research

Improving multivariate data streams clustering.

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivariate data streams based on their evolving behavior over time, exploring the correlations among their attributes by computing the fractal dimension. Experimental results with climate data streams show that the clusters' quality and compactness can be improved compared to the competing method, leading to the thoughtfulness that attributes correlations cannot be put aside. In fact, the clusters' compactness are 7 to 25 times better using our method. Our framework also proves to be an useful tool to assist meteorologists in understanding the climate behavior along a period of time.Edição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016

Stops and Stares: Street Stops, Surveillance, and Race in the New Policing

The use of proactive tactics to disrupt criminal activities, such as Terry street stops and concentrated misdemeanor arrests, are essential to the “new policing.” This model applies complex metrics, strong management, and aggressive enforcement and surveillance to focus policing on high crime risk persons and places. The tactics endemic to the “new policing” gave rise in the 1990s to popular, legal, political and social science concerns about disparate treatment of minority groups in their everyday encounters with law enforcement. Empirical evidence showed that minorities were indeed stopped and arrested more frequently than similarly situated whites, even when controlling for local social and crime conditions. In this article, we examine racial disparities under a unique configuration of the street stop prong of the “new policing” – the inclusion of non-contact observations (or surveillances) in the field interrogation (or investigative stop) activity of Boston Police Department officers. We show that Boston Police officers focus significant portions of their field investigation activity in two areas: suspected and actual gang members, and the city’s high crime areas. Minority neighborhoods experience higher levels of field interrogation and surveillance activity net of crime and other social factors. Relative to white suspects, Black suspects are more likely to be observed, interrogated, and frisked or searched controlling for gang membership and prior arrest history. Moreover, relative to their black counterparts, white police officers conduct high numbers of field investigations and are more likely to frisk/search subjects of all races. We distinguish between preference-based and statistical discrimination by comparing stops by officer-suspect racial pairs. If officer activity is independent of officer race, we would infer that disproportionate stops of minorities reflect statistical discrimination. We show instead that officers seem more likely to investigate and frisk or search a minority suspect if officer and suspect race differ. We locate these results in the broader tensions of racial profiling that pose recurring social and constitutional concerns in the “new policing.”