Scientific Evidence and Rationale for the Development of Curcumin and Resveratrol as Nutraceutricals for Joint Health

Interleukin 1β (IL-1β) and tumor necrosis factor α (TNF-α) are key cytokines that drive the production of inflammatory mediators and matrix-degrading enzymes in osteoarthritis (OA). These proinflammatory cytokines bind to their respective cell surface receptors and activate inflammatory signaling pathways culminating with the activation of nuclear factor κB (NF-κB), a transcription factor that can be triggered by a host of stress-related stimuli including, excessive mechanical stress and ECM degradation products. Once activated, NF-κB regulates the expression of many cytokines, chemokines, adhesion molecules, inflammatory mediators, and several matrix-degrading enzymes. Therefore, proinflammatory cytokines, their cell surface receptors, NF-κB and downstream signaling pathways are therapeutic targets in OA. This paper critically reviews the recent literature and outlines the potential prophylactic properties of plant-derived phytochemicals such as curcumin and resveratrol for targeting NF-κB signaling and inflammation in OA to determine whether these phytochemicals can be used as functional foods

Scalar quantization of features in discrete hidden Markov models

Traditionally, discrete hidden Markov models (DHMM) use vector quantized speech feature vectors. In this paper, we propose scalar quantization of each element of the speech feature vector in the D-HMM formulation. The alteration required in the D-HMM algorithms for this modification is discussed here. Later, a comparison is made between the performance of D-HMM based speech recognizers using scalar and vector quantization of speech features respectively. A speaker independent TIMIT vowel classification experiment is chosen for this task. It is observed that the scalar quantization of features enhances the vowel classification accuracy by 8 to 9 %, compared to VQ based D-HMM. Also, the number of HMM parameters to estimate from a given amount of training data has drastically reduced in the new ide

Incorporating phonetic properties in hidden Markov models for speech recognition

In this paper the incorporation of important phonetic properties into hidden Markov models (HMM) is studied. Phones have characteristic properties such as unique temporal structure, context sensitive behavior and specific duration, etc. New HMMs which incorporate the above properties with additional degrees of freedom to the standard HMM states are proposed. The use of each of the phonetic property for speech recognition is demonstrated using the new HMMs. All the algorithms required for using these new models in various applications of speech recognition have been presented. Experimental comparison with the standard discrete HMM for a speaker-independent continuous speech phone recognition task show that consistent improvement is achieved by the new models

Offline Handwritten Word Recognition in Hindi

This paper discusses the Hindi offline handwritten word recognizer (HWR) that we are developing. For the purpose of training and testing the offline HWR, we have created a Hindi handwritten word and character database from 100 writers. In our HWR we use two-pass Dynamic Programming algorithm to match the test word against each word in the lexicon by initially segmenting the test word image into probablecharacters. Weextractdirectionalelementfeatures (DEF) on each character image segment and statistically model them. Currently we are achieving word recognition accuracies of 91.23 % to 79.94 % on 10 to 30 vocabulary words