Movements of articulators (e.g., tongue, lips and jaw) in different speaking rates are related in a complex manner. In this work, we examine the underlying function to transform articulatory movements involved in producing speech at a neutral speaking rate into those at fast and slow speaking rates (N2F and N2S). For this we use articulatory movement data collected from five subjects using an Electromagnetic articulograph at neutral, fast and slow speaking rates. As candidate transformation functions (TF), we use affine transformations with a diagonal matrix and a full matrix and a nonlinear function modeled by a deep neural network (DNN). Since the duration of an utterance in different speaking rates would typically be unequal, it is required to time align the articulatory movement trajectories, which, in turn, affects the TF learnt. Therefore, we propose an iterative algorithm to alternately optimize for the TF and the time alignments. Subject specific experiments reveal that while N2F transformation can be well described by an affine transformation with a full matrix, N2S transformation is better represented by a more complex nonlinear function modeled by a DNN. This could be because subjects exhibit gross articulatory movements during fast speech and hyper-articulate while producing slow speech.
Purpose: Proteoglycans (PGs) are negatively charged macromolecules containing a core protein and single or several glycosaminoglycan chains attached by covalent bond.They are distributed in all tissues, including extracellular matrix (ECM), cell surface, and basement membrane. They are involved in major pathways and cell signalling cascades which modulate several vital physiological functions of the body. They have also emerged as a target molecule for cancer treatment and as possible biomarkers for early cancer detection. Among cancers, breast cancer is a highly invasive and heterogenous type and has become the major cause of mortality especially among women. So, this review revisits the studies on PGs characterization in breast cancer using LC-MS/MSbased proteomics approach, which will be further helpful for identification of potential
PGs-based biomarkers or therapeutic targets.Experimental design: There is a lack of comprehensive knowledge on the use of LC-MS/MS-based proteomics approaches to identify and characterize PGs in breast cancer.
Results: LC-MS/MS assisted PGs characterization in breast cancer revealed the vitalPGs in breast cancer invasion and progression. In addition, comprehensive profiling and characterization of PGs in breast cancer are efficiently carried out by this approach.
Conclusions: Proteomics techniques including LC-MS/MS-based identification of proteoglycans is effectively carried out in breast cancer research. Identification of expression at different stages of breast cancer is a major challenge, and LC-MS/MSbased profiling of PGs can boost novel strategies to treat breast cancer, which involve targeting PGs, and also aid early diagnosis using PGs as biomarkers.
For the benefit of spoken language training, concatenation based articulatory video synthesis has been proposed in the past to overcome the limitation in the articulatory data recording. For this, real time magnetic resonance imaging (rt-MRI) video image-frames (IFs) containing articulatory movements have been used. These IFs require a visual augmentation for better understanding. We, in this work, propose an augmentation method using pixel intensities in the regions enclosed by the articulatory boundaries obtained from air-tissue boundaries (ATBs). Since, the pixel intensities reflect the muscle movements in the articulators, the augmented IFs could provide realistic articulatory movements, when we color them accordingly. However, the ATB manual annotation is time consuming; hence, we propose to synthesize ATBs using the ATBs from a few selected frames that have been used in synthesizing the articulatory videos. We augment a set of synthesized articulatory videos for 50 words obtained from the MRI-TIMIT database. Subjective evaluation on the quality of the augmented videos using twenty-one subjects suggests that the videos are visually more appealing than the respective synthesized rt-MRI videos with a rating of 3.75 out of 5, where a score of 5 (1) indicates that the augmented video quality is excellent (poor).
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