The Opposing Roles of GluN2C and GluN2D NMDA Receptor Subunits in Modulating Neuronal Oscillations

N-methyl-D-aspartate receptors (NMDARs) are ligand-gated ion channels consisting of two GluN1 subunits and two other subunits from among GluN2A-2D and GluN3A-3B subunits. NMDARs play critical roles in synaptic plasticity, learning and memory, and higher brain function such as cognition and perception. Dysfunction of NMDARs (hyper-function and hypo-function of NMDARs) are related to various diseases, including stroke, schizophrenia, Alzheimer’s disease, and others. However, to date, NMDARs antagonists have mostly failed in clinical trials due to adverse effects. NMDARs antagonists replicate the core symptoms of schizophrenia which may underlie its ability to alter neuronal oscillations in the neural circuitry of different brain regions. Recent evidence has shown that GluN2C subunits of NMDAR are expressed in astrocytes in the cortex, and that GluN2D NMDAR subunits are enriched in the parvalbumin-containing GABAergic inhibitory interneurons in the cortex and midbrain structures. Other studies have shown that both astrocytes and parvalbumin-containing interneurons play an essential role in generating and maintaining neuronal oscillations. These findings imply that GluN2C and GluN2D subunits may be involved in the distinct neural circuitry which regulates neuronal oscillations and thus influence the brain function and contribute to various diseases states. The initial aims of this dissertation are to determine if GluN2C and GluN2D subunits have a role in regulating neuronal oscillations. We also measured the auditory evoked responses in wildtype and GluN2C- and GluN2D-KO mice. Lastly, we use ketamine as the tool drug to determine the role of NMDARs in neuronal oscillations in a CDKL5-KO mouse model. We found that spontaneous basal neuronal oscillations were elevated in GluN2C- and GluN2D-KO mice compared to WT mice. NMDARs antagonists increased the power of neuronal oscillations in WT mice; we found drug-induced power increase is abolished in GluN2D-KO mice and is augmented in GluN2C-KO mice. Furthermore, we also found GluN2D-KO mice displayed abnormal auditory evoked responses. Lastly, we test subunit-selective NMDARs drug and NMDARs allosteric modulators with distinct subunits selectivity developed by our lab, including PAMs and NAMs on these KO models

The NMDA receptor GluN2C subunit controls cortical excitatoryinhibitory balance, neuronal oscillations and cognitive function

Despite strong evidence for NMDA receptor (NMDAR) hypofunction as an underlying factor for cognitive disorders, the precise roles of various NMDAR subtypes remains unknown. The GluN2Ccontaining NMDARs exhibit unique biophysical properties and expression pattern, and lower expression of GluN2C subunit has been reported in postmortem brains from schizophrenia patients. We found that loss of GluN2C subunit leads to a shift in cortical excitatory-inhibitory balance towards greater inhibition. Specifically, pyramidal neurons in the medial prefrontal cortex (mPFC) of GluN2C knockout mice have reduced mEPSC frequency and dendritic spine density and a contrasting higher frequency of mIPSCs. In addition a greater number of perisomatic GAD67 puncta was observed suggesting a potential increase in parvalbumin interneuron inputs. At a network level the GluN2C knockout mice were found to have a more robust increase in power of oscillations in response to NMDAR blocker MK- 801. Furthermore, GluN2C heterozygous and knockout mice exhibited abnormalities in cognition and sensorimotor gating. Our results demonstrate that loss of GluN2C subunit leads to cortical excitatoryinhibitory imbalance and abnormal neuronal oscillations associated with neurodevelopmental disorders

Causal effects of potential risk factors on postpartum depression: a Mendelian randomization study

BackgroundPostpartum depression (PPD) is a type of depressive episode related to parents after childbirth, which causes a variety of symptoms not only for parents but also affects the development of children. The causal relationship between potential risk factors and PPD remains comprehensively elucidated.MethodsLinkage disequilibrium score regression (LDSC) analysis was conducted to screen the heritability of each instrumental variant (IV) and to calculate the genetic correlations between effective causal factors and PPD. To search for the causal effect of multiple potential risk factors on the incidence of PPD, random effects of the inverse variance weighted (IVW) method were applied. Sensitivity analyses, including weighted median, MR-Egger regression, Cochrane’s Q test, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO), were performed to detect potential Mendelian randomization (MR) assumption violations. Multivariable MR (MVMR) was conducted to control potential multicollinearity.ResultsA total of 40 potential risk factors were investigated in this study. LDSC regression analysis reported a significant genetic correlation of potential traits with PPD. MR analysis showed that higher body mass index (BMI) (Benjamini and Hochberg (BH) corrected p = 0.05), major depression (MD) (BH corrected p = 5.04E-19), and schizophrenia (SCZ) (BH corrected p = 1.64E-05) were associated with the increased risk of PPD, whereas increased age at first birth (BH corrected p = 2.11E-04), older age at first sexual intercourse (BH corrected p = 3.02E-15), increased average total household income before tax (BH corrected p = 4.57E-02), and increased years of schooling (BH corrected p = 1.47E-11) led to a decreased probability of PPD. MVMR analysis suggested that MD (p = 3.25E-08) and older age at first birth (p = 8.18E-04) were still associated with an increased risk of PPD.ConclusionIn our MR study, we found multiple risk factors, including MD and younger age at first birth, to be deleterious causal risk factors for PPD

Optimized gene editing technology for Drosophila melanogaster using germ line-specific Cas9

The ability to engineer genomes in a specific, systematic, and cost-effective way is critical for functional genomic studies. Recent advances using the CRISPR-associated single-guide RNA system (Cas9/sgRNA) illustrate the potential of this simple system for genome engineering in a number of organisms. Here we report an effective and inexpensive method for genome DNA editing in Drosophila melanogaster whereby plasmid DNAs encoding short sgRNAs under the control of the U6b promoter are injected into transgenic flies in which Cas9 is specifically expressed in the germ line via the nanos promoter. We evaluate the off-targets associated with the method and establish a Web-based resource, along with a searchable, genome-wide database of predicted sgRNAs appropriate for genome engineering in flies. Finally, we discuss the advantages of our method in comparison with other recently published approaches.Multidisciplinary SciencesSCI(E)46ARTICLE4719012-1901711

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Novel Sketch-Based Three-Dimensional Shape Retrieval Method Using Multi-View Convolutional Neural Network

Retrieving 3D models by adopting hand-drawn sketches to be the input has turned out to be a popular study topic. Most current methods are based on manually selected features and the best view produced for 3D model calculations. However, there are many problems with these methods such as distortion. For the purpose of dealing with such issues, this paper proposes a novel feature representation method to select the projection view and adapt the maxout network to the extended Siamese network architecture. In addition, the strategy is able to handle the over-fitting issue of convolutional neural networks (CNN) and mitigate the discrepancies between the 3D shape domain and the sketch. A pre-trained AlexNet was used to sketch the extract features. For 3D shapes, multiple 2D views were compiled into compact feature vectors using pre-trained multi-view CNNs. Then the Siamese convolutional neural networks were learnt for transforming the two domains’ original characteristics into nonlinear feature space, which mitigated the domain discrepancy and kept the discriminations. Two large data sets were used for experiments, and the experimental results show that the method is superior to the prior art methods in accuracy

A Novel Dynamic Dispatching Method for Bicycle-Sharing System

With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning

Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain

The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users