RESUMO
Neurodegenerative disease (ND) is a clinical condition in which neurons degenerate with a consequent loss of functions in the affected brain region. Parkinson's disease (PD) is the second most progressive ND after Alzheimer's disease (AD), which affects the motor system and is characterized by the loss of dopaminergic neurons from the nigrostriatal pathway in the midbrain, leading to bradykinesia, rigidity, resting tremor, postural instability and non-motor symptoms such as cognitive declines, psychiatric disturbances, autonomic failures, sleep difficulties, and pain syndrome. Coconut oil (CO) is an edible oil obtained from the meat of Cocos nucifera fruit that belongs to the palm family and contains 92% saturated fatty acids. CO has been shown to mediate oxidative stress, neuroinflammation, mitochondrial dysfunction, apoptosis and excitotoxicity-induced effects in PD in various in vitro and in vivo models as a multi-target bioagent. CO intake through diet has also been linked to a decreased incidence of PD in people. During digestion, CO is broken down into smaller molecules, like ketone bodies (KBs). The KBs then penetrate the blood-brain barrier (BBB) and are used as a source of energy its ability to cross BBB made this an important class of natural remedies for the treatment of ND. The current review describes the probable neuroprotective potential pathways of CO in PD, either prophylactic or therapeutic. In addition, we briefly addressed the important pathogenic pathways that might be considered to investigate the possible use of CO in neurodegeneration such as AD and PD.
RESUMO
Player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years and identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is challenging due to changing camera angles, low video resolution, small object size in wide-range shots, and transient changes in the player's posture and movement. In this paper, we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. We generate novel synthetic datasets of different complexities to mitigate the data imbalance and scarcity in the samples. To show the effectiveness of our synthetic data generation, we use a multi-step strategy that enforces attention to a particular region of interest (player's torso), to identify jersey numbers. The solution first identifies and crops players in a frame using a person detection model, then utilizes a human pose estimation model to localize jersey numbers in the detected players, obviating the need for annotating bounding boxes for number detection. We experimented with two sets of Convolutional Neural Networks (CNNs) with different learning objectives: multi-class for two-digit number identification and multi-label for digit-wise detection to compare performance. Our experiments indicate that our novel synthetic data generation method improves the accuracy of various CNN models by 9% overall, and 18% on low frequency numbers.
