Nitric oxide (NO) is believed to act as an intercellular signal that regulates synaptic plasticity in mature neurons. We now report that NO also regulates the proliferation and differentiation of mouse brain neural progenitor cells (NPCs). Treatment of dissociated mouse cortical neuroepithelial cluster cell cultures with the NO synthase inhibitor L-NAME or the NO scavenger hemoglobin increased cell proliferation and decreased differentiation of the NPCs into neurons, whereas the NO donor sodium nitroprusside inhibited NPC proliferation and increased neuronal differentiation. Brain-derived neurotrophic factor (BDNF) reduced NPC proliferation and increased the expression of neuronal NO synthase (nNOS) in differentiating neurons. The stimulatory effect of BDNF on neuronal differentation of NPC was blocked by L-NAME and hemoglobin, suggesting that NO produced by the latter cells inhibited proliferation and induced neuronal differentiation of neighboring NPCs. A similar role for NO in regulating the switch of neural stem cells from proliferation to differentiation in the adult brain is suggested by data showing that NO synthase inhibition enhances NPC proliferation and inhibits neuronal differentiation in the subventricular zone of adult mice. These findings identify NO as a paracrine messenger stimulated by neurotrophin signaling in newly generated neurons to control the proliferation and differentiation of NPC, a novel mechanism for the regulation of developmental and adult neurogenesis.

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

ROS-responsive biomaterials alleviate the oxidative stress in tissue microenvironments, promoting tissue regeneration and disease therapy.

Cu-MOF nanoparticles with an average diameter of 550 nm were synthesized from 2-aminoterephthalic acid and Cu(NO) by a mixed solvothermal method. The Cu-MOF nanoparticles can show peroxidase-like activity that can catalyze 3,3',5,5'-tetramethylbenzidine to produce a yellow chromogenic reaction in the presence of HO. The presence of abundant amine groups on the surfaces of Cu-MOF nanoparticles enables facile modification of Staphylococcus aureus (S. aureus) aptamer on Cu-MOF nanoparticles. By combining Cu-MOF-catalyzed chromogenic reaction with aptamer recognition and magnetic separation, a simple, sensitive, and selective colorimetric method for the detection of S. aureus was developed.