Lithium promotes neural precursor cell proliferation: evidence for the involvement of the non-canonical GSK-3β-NF-AT signaling

Lithium, a drug that has long been used to treat bipolar disorder and some other human pathogenesis, has recently been shown to stimulate neural precursor growth. However, the involved mechanism is not clear. Here, we show that lithium induces proliferation but not survival of neural precursor cells. Mechanistic studies suggest that the effect of lithium mainly involved activation of the transcription factor NF-AT and specific induction of a subset of proliferation-related genes. While NF-AT inactivation by specific inhibition of its upstream activator calcineurin antagonized the effect of lithium on the proliferation of neural precursor cells, specific inhibition of the NF-AT inhibitor GSK-3β, similar to lithium treatment, promoted neural precursor cell proliferation. One important function of lithium appeared to increase inhibitory phosphorylation of GSK-3β, leading to GSK-3β suppression and subsequent NF-AT activation. Moreover, lithium-induced proliferation of neural precursor cells was independent of its role in inositol depletion. These findings not only provide mechanistic insights into the clinical effects of lithium, but also suggest an alternative therapeutic strategy for bipolar disorder and other neural diseases by targeting the non-canonical GSK-3β-NF-AT signaling

Uncovering Digital Platform Generativity: A Systematic Literature Review

Generativity is identified as the driver for digital innovation and platform growth by engaging a large number of actors with diverse skills. Generativity is also the signal of innovation, and it enables innovative process self-reinforcement, which leads the digital platforms to evolve in unanticipated ways. However, with the proliferation of generativity in the Information Systems (IS) literature growing, we find the understanding of generativity is inconsistent. We conduct a systematic literature review to clear the understanding mist and advance the understanding of generativity. Our study shows that generativity is a social-technical system in which social actors interact with each other by employing digital technologies. Generativity is not unequivocally positive to the digital platform due to the inherent tension but requires deliberate actions by the platform owners. Our study contributes to IS research by providing a comprehensive conceptual framework of digital platform generativity

HOW PARTS CONNECT TO WHOLE IN BUILDING DIGITAL GENERATIVITY IN DIGITAL PLATFORM ECOSYSTEMS

Generativity drives digital innovation and platform growth by engaging many other businesses with diverse digital skills and resources in a digital platform. As the proliferation of generativity research grows, the Information Systems (IS) literature demonstrates the basic understanding of this notion in the areas of properties of digital technologies, social events, and/or the interaction between these two without an integrated view of how generativity is raised to enable the digital innovation. Therefore, considering that digital platforms are a kind of ecosystem, we aim to develop a new understanding of this emerging phenomenon by employing a holistic perspective. Through the information ecology theoretical lens, we develop a digital generativity process model that explains how the technological and social resources interact to generate perpetual digital innovation in digital platform ecosystems (DPE). This study contributes to generativity research by providing a dynamic and holistic view of generativity formalization in DPEs

Essays in corporate finance and investments

This dissertation studies the interplay of asset and liability sides of balance sheets, and considers both the level and the risk attributions of investments and financing sources. The first chapter links financing frictions on the liability side to investment risk on the asset side. It studies the effect of financial constraints on equity holders' risk-shifting incentives within a real options framework. Within this framework, shareholders trade off the benefit of risk-shifting with the cost of financial constraints. Therefore risk-shifting is avoided ex post for highly constrained firms because the cost outweighs the benefit. In fact, both the risk-shifting incentive and the agency cost of risk-shifting are monotonically decreasing in financial constraints costs. In addition, the effect of debt maturity is also examined in this framework, and without financial constraints, there is no short-term debt effect. These model implications are supported in a large sample of firms over the 1965 to 2009 period: (1) financial constraints help to reduce risk-shifting incentives; (2) complementing the current view, financially unconstrained firms tend to shift risk even when they are still healthy; (3) short-term debt helps to strengthen the effect of financial constraints on reducing risk-shifting incentives; (4) the agency cost of risk-shifting is smaller for more constrained firms. The results are robust to the availability of internal financings. The second chapter studies the opposite direction: the effect goes from the asset side to the liability side. It studies corporate investment and financing in a dynamic trade-off model with a sequence of irreversible investments. Conditional on future investment and financing opportunities, juvenile firms underutilize debt when financing investment the first time to retain financial flexibility. Underutilization of debt persists when adolescent firms mature (i.e. exercise their last investment options), and it is more (less) severe for more back-loaded (front-loaded) investment opportunities. Thus, leverage dynamics crucially hinge upon the structure of the investment process and otherwise identical firms appear to have significantly different target leverage ratios. Structural estimation of key parameters reveals that simulated model moments can match data moments. Furthermore, capital structure regressions using model simulated data based on these parameter estimates produce results in line with the empirical evidence, and explain the empirical puzzle that average leverage ratios are path dependent and persistent for very long periods of time. The third chapter narrows down to the liability side and studies the puzzle of whether idiosyncratic risk predicts the cross-section of stock returns and the direction of the prediction. This chapter examines this relationship by extracting implied idiosyncratic variances from option prices and decomposing past realized idiosyncratic variances into expected and unexpected components. The Fama-MacBeth (1973) regressions using different samples show mixed results. The significant positive (negative) relationship between cross-sectional stock returns and implied idiosyncratic variance (past realized idiosyncratic variance) is mainly driven by the sample with low (high) idiosyncratic variances. It is plausible that the mixed results in the literature are caused by the conflicting effects of implied idiosyncratic variance for low idiosyncratic variance stocks and persistent idiosyncratic variance shock for high idiosyncratic variance stocks

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Spectral Efficiency and Scalability Analysis for Multi-Level Cooperative Cell-Free Massive MIMO Systems

This paper proposes a multi-level cooperative architecture to balance the spectral efficiency and scalability of cell-free massive multiple-input multiple-output (MIMO) systems. In the proposed architecture, spatial expansion units (SEUs) are introduced to avoid a large amount of computation at the access points (APs) and increase the degree of cooperation among APs. We first derive the closed-form expressions of the uplink user achievable rates under multi-level cooperative architecture with maximal ratio combination (MRC) and zero-forcing (ZF) receivers. The accuracy of the closed-form expressions is verified. Moreover, numerical results have demonstrated that the proposed multi-level cooperative architecture achieves a better trade-off between spectral efficiency and scalability than other forms of cell-free massive MIMO architectures.Comment: 5 pages, 3 figure