Classifying Flies Based on Reconstructed Audio Signals

Advancements in sensor technology and processing power have made it possible to create recording equipment that can reconstruct the audio signal of insects passing through a directed infrared beam. The widespread deployment of such devices would allow for a range of applications previously not practical. A sensor net of detectors could be used to help model population dynamics, assess the efficiency of interventions and serve as an early warning system. At the core of any such system is a classification problem: given a segment of audio collected as something passes through a sensor, can we classify it? We examine the case of detecting the presence of fly species, with a particular focus on mosquitoes. This gives rise to a range of problems such as: can we discriminate between species of fly? Can we detect different species of mosquito? Can we detect the sex of the insect? Automated classification would significantly improve the effectiveness and efficiency of vector monitoring using these sensor nets. We assess a range of time series classification (TSC) algorithms on data from two projects working in this area. We assess our prior belief that spectral features are most effective, and we remark on all approaches with respect to whether they can be considered ``real-time''

Conformational analysis of AT1 antagonist valsartan using 2DNMR spectroscopy and computational analysis: determination of thermodynamic parameters through dynamic NMR spectroscopy and semi-empirical calculations

AbstractArticles published in this journal are Indexed or Abstracted in Chemical Abstracts, Elsevier's Bibliographic Databases: Scopus, EMBASE, EMBiology, Elsevier BIOBASE, Compendex, GEOBASE, FLUIDEX, TEXTILE

Nanoscale processes during the interaction of aluminosilicate and carbonate mineral surfaces with acid mine drainage (AMD)

Depto. de Mineralogía y PetrologíaFac. de Ciencias GeológicasTRUEpu

BNN27, a 17-Spiroepoxy Steroid Derivative, Interacts With and Activates p75 Neurotrophin Receptor, Rescuing Cerebellar Granule Neurons from Apoptosis

Neurotrophin receptors mediate a plethora of signals affecting neuronal survival. The p75 pan-neurotrophin receptor controls neuronal cell fate after its selective activation by immature and mature isoforms of all neurotrophins. It also exerts pleiotropic effects interacting with a variety of ligands in different neuronal or non-neuronal cells. In the present study, we explored the biophysical and functional interactions of a bloodbrain-barrier (BBB) permeable, C17-spiroepoxy steroid derivative, BNN27, with p75NTR receptor. BNN27 was recently shown to bind to NGF high-affinity receptor, TrkA. We now tested the p75NTR-mediated effects of BNN27 in mouse Cerebellar Granule Neurons (CGNs), expressing p75NTR, but not TrkA receptors. Our findings show that BNN27 physically interacts with p75NTR receptors in specific amino-residues of its extracellular domain, inducing the recruitment of p75NTR receptor to its effector protein RIP2 and the simultaneous release of RhoGDI in primary neuronal cells. Activation of the p75NTR receptor by BNN27 reverses serum deprivation-induced apoptosis of CGNs resulting in the decrease of the phosphorylation of pro-apoptotic JNK kinase and of the cleavage of Caspase-3, effects completely abolished in CGNs, isolated from p75NTR null mice. In conclusion, BNN27 represents a lead molecule for the development of novel p75NTR ligands, controlling specific p75NTR-mediated signaling of neuronal cell fate, with potential applications in therapeutics of neurodegenerative diseases and brain traum

Pharmacoeconomic analysis of paliperidone palmitate for treating schizophrenia in Greece

BACKGROUND: Patients having chronic schizophrenia with frequent relapses and hospitalizations represent a great challenge, both clinically and financially. Risperidone long-acting injection (RIS-LAI) has been the main LAI atypical antipsychotic treatment in Greece. Paliperidone palmitate (PP-LAI) has recently been approved. It is dosed monthly, as opposed to biweekly for RIS-LAI, but such advantages have not yet been analysed in terms of economic evaluation. PURPOSE: To compare costs and outcomes of PP-LAI versus RIS-LAI in Greece. METHODS: A cost-utility analysis was performed using a previously validated decision tree to model clinical pathways and costs over 1 year for stable patients started on either medication. Rates were taken from the literature. A local expert panel provided feedback on treatment patterns. All direct costs incurred by the national healthcare system were obtained from the literature and standard price lists; all were inflated to 2011 costs. Patient outcomes analyzed included average days with stable disease, numbers of hospitalizations, emergency room visits, and quality-adjusted life-years (QALYs). RESULTS: The total annual healthcare cost with PP-LAI was €3529; patients experienced 325 days in remission and 0.840 QALY; 28% were hospitalized and 15% received emergency room treatment. With RIS-LAI, the cost was €3695, patients experienced 318.6 days in remission and 0.815 QALY; 33% were hospitalized and 17% received emergency room treatment. Thus, PP-LAI dominated RIS-LAI. Results were generally robust in sensitivity analyses with PP-LAI dominating in 74.6% of simulations. Results were sensitive to the price of PP-LAI. CONCLUSIONS: PP-LAI appears to be a cost-effective option for treating chronic schizophrenia in Greece compared with RIS-LAI since it results in savings to the health care system along with better patient outcomes

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.Fundação para a Ciência e a Tecnologia (FCT