Stroke is one of the leading causes of death and disability worldwide. However, there is a lack of effective medications to speed up the recovery process. Ischemic stroke, as the result of cerebral infarction or cerebral artery narrowing, is accompanied by hemiplegia or impaired consciousness. There are many transcription factors involved in the development of this condition, whose alterations can influence or signal the prognostic outcomes of ischemic stroke. Among them, the augmented expression of specificity protein 1 (SP1) can participate in the progression of the disease by binding DNA to regulate the transcriptions of many genes. Different studies have provided different answers as to whether SP1 plays a positive or a negative role in ischemic stroke. On the one hand, SP1 can play a cytoprotective role as both an antioxidant and anti-apoptotic agent for neurons and glial cells. On the other hand, it can also damage neuronal cells by promoting inflammation and exacerbating brain edema. In this review, we highlight the roles of SP1 in ischemic stroke and shed light on the underlying mechanism.

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We implement rate-adaptable prefix-free code distribution matching in an FPGA, demonstrating its real-time feasibility with substantially less hardware resources than low-density parity-check coding.

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.