York University

About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.

Coordination of metabolic plasticity in skeletal muscle.

Abstract: Skeletal muscle is a highly malleable tissue, capable of pronounced metabolic and morphological adaptations in response to contractile activity (i.e. exercise). Each bout of contractile activity results in a coordinated alteration in the expression of a variety of nuclear DNA and mitochondrial DNA (mtDNA) gene products, leading to phenotypic adaptations. This results in an increase in muscle mitochondrial volume and changes in organelle composition, referred to as mitochondrial biogenesis. The functional consequence of this biogenesis is an improved resistance to fatigue. Signals initiated by the exercise bout involve changes in intracellular Ca2+ as well as alterations in energy status (i.e. ATP/ADP ratio) and the consequent activation of downstream kinases such as AMP kinase and Ca2+-calmodulin-activated kinases. These kinases activate transcription factors that bind DNA to affect the transcription of genes, the most evident manifestation of which occurs during the post-exercise recovery period when energy metabolism is directed toward anabolism, rather than contractile activity. An important protein that is affected by exercise is the transcriptional coactivator PGC-1alpha, which cooperates with multiple transcription factors to induce the expression of nuclear genes encoding mitochondrial proteins. Once translated in the cytosol, these mitochondrially destined proteins are imported into the mitochondrial outer membrane, inner membrane or matrix space via specific import machinery transport components. Contractile activity affects the expression of the import machinery, as well as the kinetics of import, thus facilitating the entry of newly synthesized proteins into the expanding organelle. An important set of proteins that are imported are the mtDNA transcription factors, which influence the expression and replication of mtDNA. While mtDNA contributes only 13 proteins to the synthesis of the organelle, these proteins are vital for the proper assembly of multi-subunit complexes of the respiratory chain, when combined with nuclear-encoded protein subunits. The expansion of skeletal muscle mitochondria during organelle biogenesis involves the assembly of an interconnected network system (i.e. a mitochondrial reticulum). This expansion of membrane size is influenced by the balance between mitochondrial fusion and fission. Thus, mitochondrial biogenesis is an adaptive process that requires the coordination of multiple cellular events, including the transcription of two genomes, the synthesis of lipids and proteins and the stoichiometric assembly of multisubunit protein complexes into a functional respiratory chain. Impairments at any step can lead to defective electron transport, a subsequent failure of ATP production and an inability to maintain energy homeostasis.

The ISPRS benchmark on urban object classification and 3D building reconstruction

Abstract: . For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

Neurophenomenology Integrating Subjective Experience and Brain Dynamics in the Neuroscience of Consciousness

Abstract: The paper presents a research programme for the neuroscience of con- sciousness called 'neurophenomenology' (Varela 1996) and illustrates it with a recent pilot study (Lutz et al., 2002). At a theoretical level, neurophenomenology pursues a n e mbodied a nd l arge-scale d ynamical a pproach t o t he neurophysiology of consciousness (Varela 1995; Thompson and Varela 2001; Varela and Thompson 2003). At a methodological level, the neurophenomeno- logical strategy is to make rigorous and extensive use of first-person data about subjective experience as a heuristic to describe and quantify the large-scale neurodynamics of consciousness (Lutz 2002). The paper foocuses on neurophenomenology in relation to three challenging methodological issues about incorporating first-person data into cognitive neuroscience: (i) first-person reports can be biased or inaccurate; (ii) the process of generating first-person reports about an experience can modify that experience; and (iii) there is an 'ex- planatory gap' in our understanding of how to relate first-person, phenomeno- logical data to third-person, biobehavioural data.

Gender Diversity and Securities Fraud

Abstract: We formulate theory on the effect of board of director gender diversity on the broad spectrum of securities fraud, and generate three key insights. First, based on ethicality, risk aversion, and diversity, we hypothesize that gender diversity on boards can operate as a significant moderator for the frequency of fraud. Second, we advance that the stock market response to fraud from a more gender-diverse board is significantly less pronounced. Third, we posit that women are more effective in male-dominated industries in reducing both the frequency and severity of fraud. Results of our novel empirical tests, based on data from a large sample of Chinese firms that committed securities fraud, are largely consistent with each of these hypotheses.

Exploring convolutional neural network structures and optimization techniques for speech recognition.

Abstract: Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully connected deep neural networks within the hybrid deep neural network / hidden Markov model (DNN/HMM) framework on the phone recognition task. In this paper, we extend the earlier basic form of the CNN and explore it in multiple ways. We first investigate several CNN architectures, including full and limited weight sharing, convolution along frequency and time axes, and stacking of several convolution layers. We then develop a novel weighted softmax pooling layer so that the size in the pooling layer can be automatically learned. Further, we evaluate the effect of CNN pretraining, which is achieved by using a convolutional version of the RBM. We show that all CNN architectures we have investigated outperform the earlier basic form of the DNN on both the phone recognition and large vocabulary speech recognition tasks. The architecture with limited weight sharing provides additional gains over the full weight sharing architecture. The softmax pooling layer performs as well as the best CNN with the manually tuned fixed-pooling size, and has a potential for further improvement. Finally, we show that CNN pretraining produces significantly better results on a large vocabulary speech recognition task.