Security Issuance, Institutional Investors and Quid Pro Quo: Insights from SPACs

Security issuance is subject to informational and agency-related frictions. However, their effects on IPO underpricing are difficult to disentangle. We consider SPACs and use their institutional features to study these effects separately. To this end, we identify premium investors who produce information and whose participation is associated with higher SPAC success and announcement-period returns. In contrast, non-premium investors engage in quid pro quo, such that their high returns today means higher participation in weaker future deals. Furthermore, this quid pro quo is not pure agency cost: rather, it acts as insurance for issuers, enabling marginal firms to access financial markets

Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications

The role of the SGK3/TOPK signaling pathway in the transition from acute kidney injury to chronic kidney disease

Introduction: Profibrotic phenotype of renal tubular epithelial cells (TECs) featured with epithelial to mesenchymal transition (EMT) and profibrotic factors secretion, and aberrant accumulation of CD206+ M2 macrophages are the key points in the transition from acute kidney injury (AKI) to chronic kidney disease (CKD). Nevertheless, the underlying mechanisms involved remain incompletely understood. Serum and glucocorticoid-inducible kinase (SGK) is a serine/threonine protein kinase, required for intestinal nutrient transport and ion channels modulation. T-LAK-cell-originated protein kinase (TOPK) is a member of the mitogen activated protein kinase family, linked to cell cycle regulation. However, little is known about their roles in AKI-CKD transition.Methods: In this study, three models were constructed in C57BL/6 mice: low dose and multiple intraperitoneal injection of cisplatin, 5/6 nephrectomy and unilateral ureteral obstruction model. Rat renal tubular epithelial cells (NRK-52E) were dealt with cisplatin to induce profibrotic phenotype, while a mouse monocytic cell line (RAW264.7) were cultured with cisplatin or TGF-β1 to induce M1 or M2 macrophage polarization respectively. And co-cultured NRK-52E and RAW264.7 through transwell plate to explore the interaction between them. The expression of SGK3 and TOPK phosphorylation were detected by immunohistochemistry, immunofluorescence and western blot analysis.Results:In vivo, the expression of SGK3 and p-TOPK were gradually inhibited in TECs, but enhanced in CD206+ M2 macrophages. In vitro, SGK3 inhibition aggravated epithelial to mesenchymal transition through reducing the phosphorylation state of TOPK, and controlling TGF-β1 synthesis and secretion in TECs. However, SGK3/TOPK axis activation promoted CD206+ M2 macrophage polarization, which caused kidney fibrosis by mediating macrophage to myofibroblast transition (MMT). When co-cultured, the TGF-β1 from profibrotic TECs evoked CD206+ M2 macrophage polarization and MMT, which could be attenuated by SGK3/TOPK axis inhibition in macrophages. Conversely, SGK3/TOPK signaling pathway activation in TECs could reverse CD206+ M2 macrophages aggravated EMT.Discussion: We revealed for the first time that SGK3 regulated TOPK phosphorylation to mediate TECs profibrotic phenotype, macrophage plasticity and the crosstalk between TECs and macrophages during AKI-CKD transition. Our results demonstrated the inverse effect of SGK3/TOPK signaling pathway in profibrotic TECs and CD206+ M2 macrophages polarization during the AKI-CKD transition

Optimal Estimation of Ion-Channel Kinetics from Macroscopic Currents

Markov modeling provides an effective approach for modeling ion channel kinetics. There are several search algorithms for global fitting of macroscopic or single-channel currents across different experimental conditions. Here we present a particle swarm optimization(PSO)-based approach which, when used in combination with golden section search (GSS), can fit macroscopic voltage responses with a high degree of accuracy (errors within 1%) and reasonable amount of calculation time (less than 10 hours for 20 free parameters) on a desktop computer. We also describe a method for initial value estimation of the model parameters, which appears to favor identification of global optimum and can further reduce the computational cost. The PSO-GSS algorithm is applicable for kinetic models of arbitrary topology and size and compatible with common stimulation protocols, which provides a convenient approach for establishing kinetic models at the macroscopic level

Simulation study of indirect positron generation by an ultra-short laser

Positron generation by an ultra-short ultra-intense laser in an indirect manner has been studied with two-dimensional particle-in-cell (PIC) and Monte Carlo (MC) simulations. In this generation scheme, positrons are produced with energetic electrons accelerated by an ultra-shot laser pulse propagating through an underdense plasma. The dependence of the positron beam properties on the plasma length and secondary target (converter) thickness was investigated in detail. The simulation results reveal that the positron yield is strongly correlated with the total energy of laser-accelerated electrons; both the temperature and divergence of the positron beam are sensitive to the plasma length; and the positron beam has a pulse duration comparable to the incident electron beam. In addition, it is indicated that even with the optimal converter thickness, only a small fraction (11.4%) of positrons can escape out and most of the detected positrons originate from the back edge of the converter

Ryanodine Receptor as Insecticide Target

The ryanodine receptor (RyR) is one of the primary targets of commercial insecticides. The diamide insecticide family, including flubendiamide, chlorantraniliprole, cyantraniliprole, etc., targets insect RyRs and can be used to control a wide range of destructive agricultural pests. The diamide insecticides are highly selective against lepidopteran and coleopteran pests with relatively low toxicity for non-target species, such as mammals, fishes, and beneficial insects. However, recently mutations identified on insect RyRs have emerged and caused resistance in several major agricultural pests throughout different continents. This review paper summarizes the recent findings on the structure and function of insect RyRs as insecticide targets. Specifically, we examine the structures of RyRs from target and non-target species, which reveals the molecular basis for insecticide action and selectivity. We also examine the structural and functional changes of RyR caused by the resistance mutations. Finally, we examine the progress in RyR structure-based insecticide design and discuss how this might help the development of a new generation of green insecticides

Fit a MWC model C₅-O₅ to the macroscopic currents of BK channels from <i>Xenopus</i> oocyte.

<p>(A) Activation traces of BK currents were recorded from an inside-out patch from a <i>Xenopus</i> oocyte injected with cRNA encoding mSlo1 α subunits. Channels were activated by voltage steps ranging from −200 to +200 mV with 10 mV increments from a holding potential of −180 mV with a cytosolic [Ca²⁺]_i as indicated. The voltage protocol is not shown here. The red lines were coming from the globally fitting the model C₅-O₅ to BK currents by PSO-GSS algorithm. The channel count N_C is 314 for 1 µM, 365 for 10 µM and 433 for 300 µM. The different Nc in the same patch is probably coming from the smaller single-channel conductance at the higher Ca²⁺, which will not change the channel kinetics. (B) Deactivation currents were obtained from the same patch as we described in (A). Currents were elicited by voltage steps ranging from −200 to +180 mV with 10 mV increments from a 20 ms-prepulse of +180 mV with a cytosolic [Ca²⁺]_i as indicated. The red lines are fits by a PSO-GSS algorithm. The channel count N_C is 301 for 1 µM, 354 for 10 µM and 387 for 300 µM. The score σ² is 41.60. All the capacitive currents of 0.15 ms were pre-substituted with straight lines before run and not counted during run. The dash line is zero current.</p

Fit a five-parameter voltage-dependent C-O model to the target current traces.

<p>(A) A five-parameter Markov model consisting of a closed state and an open state labeled with the letter C and O, respectively. The forward and backward rate constants separately are a*exp(v/b) and c*exp(−v/d). Here v represents voltage in mV, a and c the pre-exponential factors in ms⁻¹ and b and d the exponential factors in mV. The fifth parameter is the channel number N_C. (B) The errors relative to their target values were obtained by estimation of initial values (left) or by fit (right). (C) Convergence of PSO-GSS with (solid line) or without (dotted line) direct estimation. (D) In this model, target parameters a = 1 ms⁻¹, b = 50 mV, c = 1 ms⁻¹ and d = 200 mV; the reversal potential of channels V_r = 0 mV; the single-channel conductance G = 250 pS and the channel count N_C = 1. The empty circles represent the target currents at the various voltages shown under each of current traces, and the solid lines represent fitted currents.</p

Schematic diagram for GSS and PSO.

<p>(A) A schematic drawing for golden section search algorithm (GSS). (B) A flow graph for the PSO-GSS algorithm.</p