OVI Emission in the Halos of Edge-on Spiral Galaxies

We have used the Far Ultraviolet Spectroscopic Explorer to search for OVI 1031.926, 1037.617 A emission in the halos of the edge-on spiral galaxies NGC4631 and NGC891. In NGC4631, we detected OVI in emission toward a soft X-ray bubble above a region containing numerous Halpha arcs and filaments. The line-of-sight component of the motion of the OVI gas appears to match the underlying disk rotation. The observed OVI luminosities can account for 0.2-2% of the total energy input from supernovae (assuming a full OVI emitting halo) and yield mass flux cooling rates between 0.48 and 2.8 M_sun/yr depending on the model used in the derivations. Based on these findings, we believe it is likely that we are seeing cooling, galactic fountain gas. No emission was detected from the halo of NGC891, a galaxy in a direction with considerably high foreground Galactic extinction.Comment: accepted for publication in ApJ, 16 pages including 4 figure

Specific immune modulation of experimental colitis drives enteric alpha-synuclein accumulation and triggers age-related Parkinson-like brain pathology

Background: In some people with Parkinson’s disease (PD), a-synuclein (αSyn) accumulation may begin in the enteric nervous system (ENS) decades before development of brain pathology and disease diagnosis. Objective: To determine how different types and severity of intestinal inflammation could trigger αSyn accumulation in the ENS and the subsequent development of αSyn brain pathology. Methods: We assessed the effects of modulating short- and long-term experimental colitis on αSyn accumulation in the gut of αSyn transgenic and wild type mice by immunostaining and gene expression analysis. To determine the long-term effect on the brain, we induced dextran sulfate sodium (DSS) colitis in young αSyn transgenic mice and aged them under normal conditions up to 9 or 21 months before tissue analyses. Results: A single strong or sustained mild DSS colitis triggered αSyn accumulation in the submucosal plexus of wild type and αSyn transgenic mice, while short-term mild DSS colitis or inflammation induced by lipopolysaccharide did not have such an effect. Genetic and pharmacological modulation of macrophage-associated pathways modulated the severity of enteric αSyn. Remarkably, experimental colitis at three months of age exacerbated the accumulation of aggregated phospho-Serine 129 αSyn in the midbrain (including the substantia nigra), in 21- but not 9-month-old αSyn transgenic mice. This increase in midbrain αSyn accumulation is accompanied by the loss of tyrosine hydroxylase-immunoreactive nigral neurons. Conclusions: Our data suggest that specific types and severity of intestinal inflammation, mediated by monocyte/macrophage signaling, could play a critical role in the initiation and progression of PD

Epileptic Seizure Detection Using a Convolutional Neural Network

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy

Automatic transcription of Turkish microtonal music

Automatic music transcription, a central topic in music signal analysis, is typically limited to equal-tempered music and evaluated on a quartertone tolerance level. A system is proposed to automatically transcribe microtonal and heterophonic music as applied to the makam music of Turkey. Specific traits of this music that deviate from properties targeted by current transcription tools are discussed, and a collection of instrumental and vocal recordings is compiled, along with aligned microtonal reference pitch annotations. An existing multi-pitch detection algorithm is adapted for transcribing music with 20 cent resolution, and a method for converting a multi-pitch heterophonic output into a single melodic line is proposed. Evaluation metrics for transcribing microtonal music are applied, which use various levels of tolerance for inaccuracies with respect to frequency and time. Results show that the system is able to transcribe microtonal instrumental music at 20 cent resolution with an F-measure of 56.7%, outperforming state-of-the-art methods for the same task. Case studies on transcribed recordings are provided, to demonstrate the shortcomings and the strengths of the proposed method.QC 20161031</p

A cognitive framework for object recognition with application to autonomous vehicles

Autonomous vehicles or self-driving cars are capable of sensing the surrounding environment so they can navigate roads without human input. Decisions are constantly made on sensing, mapping and driving policy using machine learning techniques. Deep Learning – massive neural networks that utilize the power of parallel processing – has become a popular choice for addressing the complexities of real time decision making. This method of machine learning has been shown to outperform alternative solutions in multiple domains, and has an architecture that can be adapted to new problems with relative ease. To harness the power of Deep Learning, it is necessary to have large amounts of training data that are representative of all possible situations the system will face. To successfully implement situational awareness in driverless vehicles, it is not possible to exhaust all possible training examples. An alternative method is to apply cognitive approaches to perception, for situations the autonomous vehicles will face. Cognitive approaches to perception work by mimicking the process of human intelligence – thereby permitting a machine to react to situations it has not previously experienced. This paper proposes a novel cognitive approach for object recognition. The proposed cognitive object recognition algorithm, referred to as Recognition by Components, is inspired by the psychological studies pertaining to early childhood development. The algorithm works by breaking down images into a series of primitive forms such as square, triangle, circle or rectangle and memory based aggregation to identify objects. Experimental results suggest that Recognition by Component algorithm performs significantly better than algorithms that require large amounts of training data

Seismic resistance of traditional timber-frame hımış

Hımış structures have hardly ever found as extensive a role as other traditional timber housing, such as those originating from Japan or Central Europe, within the wide discourse on the seismic performance of timber-frame architecture that has gained significant momentum in the last few decades owing to advancing testing technologies. While the hımış construction technique was perhaps not born as a result of a conscious search for a seismically resistant building form, it was soon widely appreciated for its structural features advantageous under seismic loading - especially from the sixteenth century when it has become a well-established construction technique in part of the Balkans and in today’s Turkey. Despite widely available anecdotal information based on post-disaster studies regarding its performance under earthquakes, robust quantitative data on the seismic behaviour of these structures were practically non-existent until quite recently, and are still somewhat limited. However, we are now able to confirm that hımış constructions do have intrinsic qualities that are very beneficial under seismic action. This paper aims to make a brief review of the current state of our knowledge on structural performance of hımış buildings under earthquake loading, with specific emphasis on infill/cladding techniques, connection details and energy dissipation characteristics

The Making of Racial and Ethnic Categories: Official Statistics Reconsidered

One of the most striking features of the end of the twentieth century was the resurgence of the ethnic question in public debates, both in developing and in developed countries. Between conflicts and wars interpreted from an ethnic perspective (the Balkans and central Africa), nationalist struggles (the Basque country, Quebec and Belgium), and demands for recognition and political representation by new ethnic minorities resulting from immigration, every country is currently affected by what is commonly known as cultural pluralism (Hobsbawm 1993; Dieckhoff 2000; Faist 2009; Simon and Piché 2013). This ‘ethnic renewal’, to coin the expression used to qualify the growing interest for ethnic diversity in the 1960s in the US, is not only driven by a sort of obsession for cultural differences as an explanation for all kinds of social and political phenomenon. It derives from different legacies: from the increasing diversity of the population of countries that have undergone large immigration flows to the long lasting cohabitation of national minorities within modern Nation states, from the history of slavery to the post-colonial era. This resurgence or extension of the salience of ethnicity in most of the societies around the world can be found not only in public discourses, policy-making, scientific literature and popular representations, but also in the pivotal realm of statistics. Indeed, at the turn of century, an increasing number of countries are processing routinely data on ethnicity or race of their population. This is precisely what this book is about: ethnic and racial classifications in official statistics, as a reflection of the representations of population and an interpretation of social dynamics through different lenses