A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain's computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain

Odanacatib Inhibits Resistin-induced Hypertrophic H9c2 Cardiomyoblast Cells Through LKB1/AMPK Pathway

ABSTRACT Odanacatib (ODN) is a selective inhibitor of cathepsin K. The cysteine protease cathepsin K has been implicated in cardiac hypertrophy. Resistine is an adipokine which is identified to promote cardiac hypertrophy. Here, we hypothesize that ODN mitigates resistin-induced myocyte hypertrophy. Cell surface area and protein synthesis were measured after treatment with resistin and ODN in H9c2 cells. The expression of cardiomyocyte hypertrophy marker BNP and β-MHC was detected by RT-qPCR. The expression and phosphorylation of AMPK and LKB1 were analyzed with Western blot. Resistin could significantly increase cardiomyocyte cell surface area, protein synthesis, and embryonic gene BNP and β-MHC expression, inhibit phosphorylation of AMPK and LKB1. ODN could significantly reverse the effects of resistin. Collectively, our data suggest that ODN can inhibit cardiomyocyte hypertrophy induced by resistin and the underlying mechanism may be involved in LKB1/AMPK pathway

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate

Separated parabiont reveals the fate and lifespan of peripheral-derived immune cells in normal and ischaemia-induced injured kidneys

Immune cell infiltration plays a key role in acute kidney injury (AKI) to chronic kidney disease (CKD) progression. T lymphocytes, neutrophils, monocytes/macrophages and other immune cells regulate inflammation, tissue remodelling and repair. To determine the kinetics of accumulation of various immune cell populations, we established an animal model combining parabiosis and separation surgery to explore the fate and lifespan of peripheral leucocytes that migrate to the kidney. We found that peripheral T lymphocytes could survive for a long time (more than 14 days), whereas peripheral neutrophils survived for a short time in both healthy and ischaemia-induced damaged kidneys. Nearly half of the peripheral-derived macrophages disappeared after 14 days in normal kidneys, while their existing time in the inflammatory kidneys was prolonged. A fraction of F4/80high macrophages were renewed from the circulating monocyte pool. In addition, we found that after renal ischaemia reperfusion, neutrophils increased significantly in the early phase, and T lymphocytes mainly accumulated in the late stage, whereas macrophages infiltrated throughout AKI-CKD progression and were sustained longer in injured as opposed to normal kidneys. In conclusion, peripheral-derived macrophages, T lymphocytes and neutrophils exhibit different lifespans in the kidney, which may play different roles during AKI-CKD progression

Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005–2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models’ predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate