Loss of FKBP5 Affects Neuron Synaptic Plasticity: An Electrophysiology Insight

FKBP5 (FKBP51) is a glucocorticoid receptor (GR) binding protein, which acts as a co-chaperone of heat shock protein 90 (HSP90) and negatively regulates GR. Its association with mental disorders has been identified, but its function in disease development is largely unknown. Long-term potentiation (LTP) is a functional measurement of neuronal connection and communication, and is considered one of the major cellular mechanisms that underlies learning and memory, and is disrupted in many mental diseases. In this study, a reduction in LTP in Fkbp5 knockout (KO) mice was observed when compared to WT mice, which correlated with changes to the glutamatergic and GABAergic signaling pathways. The frequency of mEPSCs was decreased in KO hippocampus, indicating a decrease in excitatory synaptic activity. While no differences were found in levels of glutamate between KO and WT, a reduction was observed in the expression of excitatory glutamate receptors (NMDAR1, NMDAR2B and AMPAR), which initiate and maintain LTP. The expression of the inhibitory neurotransmitter GABA was found to be enhanced in Fkbp5 KO hippocampus. Further investigation suggested that increased expression of GAD65, but not GAD67, accounted for this increase. Additionally, a functional GABAergic alteration was observed in the form of increased mIPSC frequency in the KO hippocampus, indicating an increase in presynaptic GABA release. Our findings uncover a novel role for Fkbp5 in neuronal synaptic plasticity and highlight the value of Fkbp5 KO as a model for studying its role in neurological function and disease development

MAPK8 and CAPN1 as potential biomarkers of intervertebral disc degeneration overlapping immune infiltration, autophagy, and ceRNA

BackgroundIntervertebral disc degeneration (IDD) is one of the most common health problems in the elderly and a major causative factor in low back pain (LBP). An increasing number of studies have shown that IDD is closely associated with autophagy and immune dysregulation. Therefore, the aim of this study was to identify autophagy-related biomarkers and gene regulatory networks in IDD and potential therapeutic targets.MethodsWe obtained the gene expression profiles of IDD by downloading the datasets GSE176205 and GSE167931 from the Gene Expression Omnibus (GEO) public database. Subsequently, differentially expressed genes (DEGs) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene ontology (GO), and gene set enrichment analysis (GSEA) were performed to explore the biological functions of DEGs. Differentially expressed autophagy-related genes (DE-ARGs) were then crossed with the autophagy gene database. The hub genes were screened using the DE-ARGs protein–protein interaction (PPI) network. The correlation between the hub genes and immune infiltration and the construction of the gene regulatory network of the hub genes were confirmed. Finally, quantitative PCR (qPCR) was used to validate the correlation of hub genes in a rat IDD model.ResultsWe obtained 636 DEGs enriched in the autophagy pathway. Our analysis revealed 30 DE-ARGs, of which six hub genes (MAPK8, CTSB, PRKCD, SNCA, CAPN1, and EGFR) were identified using the MCODE plugin. Immune cell infiltration analysis revealed that there was an increased proportion of CD8+ T cells and M0 macrophages in IDD, whereas CD4+ memory T cells, neutrophils, resting dendritic cells, follicular helper T cells, and monocytes were much less abundant. Subsequently, the competitive endogenous RNA (ceRNA) network was constructed using 15 long non-coding RNAs (lncRNAs) and 21 microRNAs (miRNAs). In quantitative PCR (qPCR) validation, two hub genes, MAPK8 and CAPN1, were shown to be consistent with the bioinformatic analysis results.ConclusionOur study identified MAPK8 and CAPN1 as key biomarkers of IDD. These key hub genes may be potential therapeutic targets for IDD

Active Mask-Box Scoring R-CNN for Sonar Image Instance Segmentation

Instance segmentation of sonar images is an effective method for underwater target recognition. However, the mismatch among positioning accuracy found by boxIoU and classification confidence, which is used as NMS score in current instance segmentation models; and the high annotation cost of sonar images, are two major problems in the task. To tackle these problems, in this paper, we present a novel instance segmentation method called Mask-Box Scoring R-CNN and embedded it in our proposed deep active learning framework. For the mismatch problem between boxIoU and NMS score, Mask-Box Scoring R-CNN uses a boxIoU head to predict the quality of the bounding boxes. We amend the non-maximum suppression (NMS) score predicted by BoxIoU to preserve high-quality bounding boxes in inference flow. To deal with the annotating problem, we propose a triplets-measure-based active learning (TBAL) method and a balanced-sampling method applicable for deep learning. The TBAL method evaluates the amount of information of unlabeled samples from the aspects of classification confidence, positioning accuracy, and mask quality. The balanced-sampling method selects hard samples from the dataset to train the model to improve performance. The experimental results show that Mask-Box Scoring R-CNN achieves improvements of 1% in boxAP and 1.3% boxAP on our sonar image dataset compared with Mask Scoring R-CNN and Mask R-CNN, respectively. The active learning framework with TBAL and balanced sampling can achieve a competitive performance with less labeled samples than other frameworks, which can better facilitate underwater target recognition

Interactive impacts of nitrogen input and water amendment on growing season fluxes of CO2, CH4, and N2O in a semiarid grassland, Northern China

Nitrogen and water are two important factors influencing GHG (primarily CO2 - carbon dioxide; CH4 - methane, and N2O - nitrous oxide) fluxes in semiarid grasslands. However, the interactive effects of nitrogen and water on GHG fluxes remain elusive. A 3-year (2010-2012) manipulative experiment was conducted to investigate the individual and interactive effects of nitrogen and water additions on GHG fluxes during growing seasons (May to September) in a semiarid grassland in Northern China. Accumulated throughout growing seasons, nitrogen input stimulated CO2 uptake by 33 +/- 1.0 g C m(-2) (g N)(-1), enhanced N2O emission by 1.2 +/- 0.3 mg N m(-2) (g N)(-1), and decreased CH4 uptake by 5.2 +/- 0.9 mg N m(-2) (g N)(-1); water amendment stimulated CO2 uptake by 0.2 +/- 0.1 g Cm-2 (mm H2O)(-1) and N2O emission by 0.2 +/- 0.02 mg N m(-2) (mm H2O)(-1) , decreased CH4 uptake by 0.3 +/- 0.1 mg C m(-2) (mm H2O)(-1). A synergistic effect between nitrogen and water was found on N2O flux in normal year while the additive effects of nitrogen and water additions were found on CH4 and CO2 uptakes during all experiment years, and on N2O-emission in dry years. The nitrogen addition had stronger impacts than water amendment on stimulating CH4 uptake in the normal year, while water was the dominant factor affecting CH4 uptake in dry years. For N2O emission, the N-stimulating impact was stronger in un-watered than in watered plots, and the water-stimulating impact was stronger in non-fertilized than in fertilized treatments in dry years. The interactive impacts of nitrogen and water additions on GHG fluxes advance our understanding of GHG fluxes in responses to multiple environmental factors. This data source could be valuable for validating ecosystem models in simulating GHG fluxes in a multiple factors environment. (C) 2016 Elsevier B.V. All rights reserved

Interactive impacts of nitrogen input and water amendment on growing season fluxes of CO2, CH4, and N2O in a semiarid grassland, Northern China

Nitrogen and water are two important factors influencing GHG (primarily CO2 - carbon dioxide; CH4 - methane, and N2O - nitrous oxide) fluxes in semiarid grasslands. However, the interactive effects of nitrogen and water on GHG fluxes remain elusive. A 3-year (2010-2012) manipulative experiment was conducted to investigate the individual and interactive effects of nitrogen and water additions on GHG fluxes during growing seasons (May to September) in a semiarid grassland in Northern China. Accumulated throughout growing seasons, nitrogen input stimulated CO2 uptake by 33 +/- 1.0 g C m(-2) (g N)(-1), enhanced N2O emission by 1.2 +/- 0.3 mg N m(-2) (g N)(-1), and decreased CH4 uptake by 5.2 +/- 0.9 mg N m(-2) (g N)(-1); water amendment stimulated CO2 uptake by 0.2 +/- 0.1 g Cm-2 (mm H2O)(-1) and N2O emission by 0.2 +/- 0.02 mg N m(-2) (mm H2O)(-1) , decreased CH4 uptake by 0.3 +/- 0.1 mg C m(-2) (mm H2O)(-1). A synergistic effect between nitrogen and water was found on N2O flux in normal year while the additive effects of nitrogen and water additions were found on CH4 and CO2 uptakes during all experiment years, and on N2O-emission in dry years. The nitrogen addition had stronger impacts than water amendment on stimulating CH4 uptake in the normal year, while water was the dominant factor affecting CH4 uptake in dry years. For N2O emission, the N-stimulating impact was stronger in un-watered than in watered plots, and the water-stimulating impact was stronger in non-fertilized than in fertilized treatments in dry years. The interactive impacts of nitrogen and water additions on GHG fluxes advance our understanding of GHG fluxes in responses to multiple environmental factors. This data source could be valuable for validating ecosystem models in simulating GHG fluxes in a multiple factors environment. (C) 2016 Elsevier B.V. All rights reserved

Evaluation of Industrial Urea Energy Consumption (EC) Based on Life Cycle Assessment (LCA)

With the increasingly prominent environmental problems and the decline of fossil fuel reserves, the reduction of energy consumption (EC) has become a common goal in the world. Urea industry is a typical energy-intensive chemical industry. However, studies just focus on the breakthrough of specific production technology or only consider the EC in the production stage. This results in a lack of evaluations of the life cycle of energy consumption (LcEC). In order to provide a systematic, scientific, and practical theoretical basis for the industrial upgrading and the energy transformation, LcEC of urea production and the greenhouse gas (GHG) emissions generated in the process of EC are studied in this paper. The results show that the average LcEC is about 30.1 GJ/t urea. The EC of the materials preparation stage, synthesis stage, and waste-treatment stage (ECRMP, ECPP, ECWD) is about 0.388 GJ/t urea, 24.8 GJ/t urea, and 4.92 GJ/t urea, accounting for 1.3%, 82.4%, and 16.3% of LcEC, respectively. Thus, the synthesis stage is a dominant energy-consumer, in which 15.4 GJ/t urea of energy, accounting for 62.0% of ECpp, supports steam consumption. According to the energy distribution analysis, it can be concluded that coal presents the primary energy in the process of urea production, which supports 94.4% of LcEC. The proportion of coal consumption is significantly higher than that of the average of 59% in China. Besides, the GHG emissions in the synthesis stage are obviously larger than that in the other stage, with an average of 2.18 t eq.CO₂/t urea, accounting for 81.3% of the life cycle of GHG (LcGHG) emissions. In detail, CO₂ is the dominant factor accounting for 90.0% of LcGHG emissions, followed by CH₄, while N₂O is negligible. Coal is the primary source of CO₂ emissions. The severe high proportion of coal consumption in the life cycle of urea production is responsible for this high CO₂ content of GHG emissions. Therefore, for industrial urea upgrading and energy transformation, reducing coal consumption will still be an important task for energy structure transformation. At the same time, the reformation of synthesis technologies, especially for steam energy-consuming technology, will mainly reduce the EC of the urea industry. Furthermore, the application of green energy will be conducive to a win-win situation for both economic and environmental benefits.Forestry, Faculty ofNon UBCReviewedFacult