Perineuronal nets (PNNs), a complex of extracellular matrix molecules that mostly surround GABAergic neurons in various brain regions, play a critical role in synaptic plasticity. The function and cellular mechanisms of PNNs in memory consolidation and reconsolidation processes are still not well understood. We hypothesized that PNNs protect long-term memory by limiting feedback inhibition from parvalbumin (PV) interneurons to projection neurons. Using behavioral, electrophysiological, and optogenetic approaches, we investigated the role of PNNs in fear memory consolidation and reconsolidation and GABAergic long-term potentiation (LTP). We made the discovery that the formation of PNNs was promoted by memory events in the hippocampus (HP), and we also demonstrated that PNN formation in both the HP and the anterior cingulate cortex (ACC) is essential for memory consolidation and reconsolidation of recent and remote memories. Removal of PNNs resulted in evident LTP impairments, which were rescued by acute application of picrotoxin, a GABAAreceptor blocker, indicating that enhanced inhibition was the cause of the LTP impairments induced by PNN removal. Moreover, removal of PNNs switched GABAAreceptor-mediated long-term depression to LTP through a presynaptic mechanism. Furthermore, the reduced activity of PV interneurons surrounded by PNNs regulated theta oscillations during fear memory consolidation. Finally, optogenetically suppressing PV interneurons rescued the memory impairment caused by removal of PNNs. Altogether, these results unveil the function of PV interneurons surrounding PNNs in protecting recent and remote contextual memory through the regulation of PV neuron GABA release.

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Post-traumatic elbow stiffness is a disabling condition that remains challenging for upper limb surgeons. Open elbow arthrolysis is commonly used for the treatment of stiff elbow when conservative therapy has failed. Multiple questions commonly arise from surgeons who deal with this disease. These include whether the patient has post-traumatic stiff elbow, how to evaluate the problem, when surgery is appropriate, how to perform an excellent arthrolysis, what the optimal postoperative rehabilitation is, and how to prevent or reduce the incidence of complications. Following these questions, this review provides an update and overview of post-traumatic elbow stiffness with respect to the diagnosis, preoperative evaluation, arthrolysis strategies, postoperative rehabilitation, and prevention of complications, aiming to provide a complete diagnosis and treatment path. Cite this article: Bone Joint Open 2020;1-9:576–584.

Curved displays have recently become very popular, with wide applications for both industry and consumers. However, built upon initially flat films, most flexible displays are often incompatible with general nondevelopable surfaces. In this paper, we report a method for producing curved displays of nondevelopable shapes by using a structure-mechanics-inspired functional optimization method to design tessellation patterns that fold into the desired shapes. Representative displays in spherical and saddle shapes are demonstrated. The microfabrication process is employed for manufacturing 2D flexible foldable circuit boards, pick-and-place technology is used for placing illuminant elements onto the boards, and mold guidance is used for folding 2-D sheets into curved 3D display prototypes. The proposed technology is feasible for mass production and advances the application of next-generation curved displays.

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Motivation Biological networks can provide a system level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e., they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module’s information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. Results In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome wide association study (GWAS) is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms (SNP) than the conventional network representation. Availability R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy Supplementary information Supplementary data are available at Bioinformatics online.

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Passenger flow is the basis for bus operation scheduling. Huge advances are being made to develop smart city traffic using big data. Intelligent bus systems based on bus integrated circuit (IC) card systems are constantly developing and improving. Compared with traditional manual survey data, bus IC data is low-cost, real-time and accurate with a simple acquisition method. Bus IC data is an important basic data resource and data mining of bus IC cards can obtain dynamic information about urban bus passenger flow and help improve urban bus planning and service levels. The crucial factor in determining whether this data can be reasonably applied to the optimization of urban bus systems is whether spatial and temporal characteristics of the passenger bus trip can be obtained through bus IC data mining, and there is much current research interest into this topic. In this paper, the characteristics of one-day passenger flow and timedivision passenger flow are analyzed based on data obtained from swiping IC cards for one week on a bus in Qingdao. Then, based on a GA-NARX neural network model, the passenger flow is forecast using the IC card swipe data for five working days of Qingdao No. 1 bus (using ten minutes as the time interval). The forecasting results show that the passenger flow can be successfully predicted using this method and thus this method can be used for short-term passenger flow forecasting using bus IC cards. INDEX TERMS Bus IC card data, bus passenger flow characteristics, genetic algorithms, neural network, short-term passenger flow forecasting.