Catalpol alleviates hypoxia ischemia-induced brain damage by inhibiting ferroptosis through the PI3K/NRF2/system Xc-/GPX4 axis in neonatal rats.

Hypoxic-ischemic encephalopathy (HIE) is a brain damage caused by perinatal hypoxia and blood flow reduction. Severe HIE leads to death. Available treatments remain limited. Oxidative stress and nerve damage are major factors in brain injury caused by HIE. Catalpol, an iridoid glucoside found in the root of Rehmannia glutinosa, has antioxidant and neuroprotective effects. This study examined the neuroprotective effects of catalpol using a neonatal rat HIE model and found that catalpol might protect the brain through inhibiting neuronal ferroptosis and ameliorating oxidative stress. Behavior tests suggested that catalpol treatment improved functions of motor, learning, and memory abilities after hypoxic-ischemic injury. Catalpol treatment inhibited changes to several ferroptosis-related proteins, including p-PI3K, p-AKT, NRF2, GPX4, SLC7A11, SLC3A2, GCLC, and GSS in HIE neonatal rats. Catalpol also prevented changes to several ferroptosis-related proteins in PC12 cells after oxygen-glucose deprivation. The ferroptosis inducer erastin reversed the protective effects of catalpol both in vitro and in vivo. We concluded that catalpol protects against hypoxic-ischemic brain damage (HIBD) by inhibiting ferroptosis through the PI3K/NRF2/system Xc-/GPX4 axis.

Clock knockout in inhibitory neurons reduces predisposition to epilepsy and influences anxiety-like behaviors in mice.

Epilepsy is a brain disorder affecting up to 1 in 26 individuals. Despite its clinical importance, the molecular mechanisms of epileptogenesis are still far from clarified. Our previous study showed that disruption of Clock in excitatory neurons alters cortical circuits and leads to generation of focal epilepsy. In this study, a GAD-Cre;Clockflox/flox mouse line with conditional Clock gene knockout in inhibitory neurons was established. We observed that seizure latency was prolonged, the severity and mortality of pilocarpine-induced seizure were significantly reduced, and memory was improved in GAD-Cre;Clockflox/flox mice. We hypothesize that mice with CLOCK knockout in inhibitory neurons have increased threshold for seizure, opposite from mice with CLOCK knockout in excitatory neurons. Further investigation showed Clock knockout in inhibitory neurons upregulated the basal protein level of ARC, a synaptic plasticity-associated immediate-early gene product, likely through the BDNF-ERK pathway. Altered basal levels of ARC may play an important role in epileptogenesis after Clock deletion in inhibitory neurons. Although sEPSCs and intrinsic properties of layer 5 pyramidal neurons in the somatosensory cortex exhibit no changes, the spine density increased in apical dendrite of pyramidal neurons in CLOCK knockout group. Our results suggest an underlying mechanism by which the circadian protein CLOCK in inhibitory neurons participates in neuronal activity and regulates the predisposition to epilepsy.

Application of TCN-biGRU neural network in [Formula: see text] concentration prediction.

Fine particulate matter ([Formula: see text]) poses a significant threat to human life and health, and therefore, accurately predicting [Formula: see text] concentration is critical for controlling air pollution. Two improved types of recurrent neural networks (RNNs), the long short-term memory (LSTM) and gated recurrent unit (GRU), have been widely used in time series data prediction due to their ability to capture temporal features. However, both degrade into random guessing as the time length increases. In order to enhance the accuracy of [Formula: see text] concentration prediction and address the issue of random guessing in RNNs neural networks, this study introduces a TCN-biGRU neural network model. This model is a hybrid prediction approach based on combining temporal convolutional networks (TCN) and bidirectional gated recurrent units (bi-GRU). TCN extracts higher-level feature information from longer time series data of [Formula: see text] concentrations, while bi-GRU captures features from past and future data to achieve more accurate predictive outcomes. This case study utilizes data from monitoring stations in Beijing in 2021 for conducting [Formula: see text] prediction experiments. The TCN-biGRU model achieves an average absolute error, root mean square error, and [Formula: see text] of 4.20, 7.71, and 0.961 in its predictive outcomes. When compared to the predictive outcomes of individual LSTM, GRU, and bi-GRU models, it is evident that the TCN-biGRU model exhibits smaller errors and superior predictive performance.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction.

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

BER improvement in SPAD-based photon-counting optical communication system by using automatic attenuation control technique.

In order to overcome the saturation of a single-photon avalanche diode (SPAD)-based receiver and keep the output counts at optimum level automatically, a new, to the best of our knowledge, scheme using the automatic attenuation control (AAC) technique is proposed. In the scheme, an AAC module is applied to attenuate excess incident photons. Furthermore, on the foundation of the bit error rate (BER) model of a photon-counting optical communication system, a reliable and efficient AAC algorithm is developed to compute the optimal attenuation factor. Based on the AAC algorithm, the optimal attenuation factors under different operation conditions are investigated. The results indicate that the incident optical intensity and signal-to-background ratio play an important role in determining the optimal attenuation factor. Moreover, at high incident optical intensity, the system BER utilized AAC module can be improved by 0.5 to 3 orders of magnitude. The AAC technique can effectively expand the dynamic range of the SPAD-based receiver.