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In this study, we examine the effect of clients' real activities manipulation (RAM) on auditors' client-retention decisions. We find that, with the exception of RAM through overproduction, clients' opportunistic operating decisions are positively associated with the likelihood of auditor resignations. We also provide evidence that auditors are especially sensitive to clients' RAM to just meet or beat earnings benchmarks in their client-retention decisions. In addition, we find that clients whose auditors resign from engagements tend to hire smaller auditors and these clients engage in RAM more aggressively. Our additional analysis shows that, with the exception of RAM through overproduction, clients' abnormal operating decisions are significantly associated with litigation risk against auditors. Overall, our evidence suggests that auditors drop clients with aggressive RAM to avoid excessive risk.

Challenges in drug development of neurological diseases remain mainly ascribed to the blood–brain barrier (BBB). Despite the valuable contribution of animal models to drug discovery, it remains difficult to conduct mechanistic studies on the barrier function and interactions with drugs at molecular and cellular levels. Here we present a microphysiological platform that recapitulates the key structure and function of the human BBB and enables 3D mapping of nanoparticle distributions in the vascular and perivascular regions. We demonstrate on-chip mimicry of the BBB structure and function by cellular interactions, key gene expressions, low permeability, and 3D astrocytic network with reduced reactive gliosis and polarized aquaporin-4 (AQP4) distribution. Moreover, our model precisely captures 3D nanoparticle distributions at cellular levels and demonstrates the distinct cellular uptakes and BBB penetrations through receptor-mediated transcytosis. Our BBB platform may present a complementary in vitro model to animal models for prescreening drug candidates for the treatment of neurological diseases. Developing an in vitro blood-brain-barrier (BBB) model that reproduces the organ’s complex structure and function is an open challenge. Here the authors present a BBB-on-a-chip that includes endothelial cells, pericytes and a 3D astrocytic network which resembles the morphology and function of astrocytes in the BBB in vivo.

In humans, lipid nanoparticles (LNPs) have safely delivered therapeutic RNA to hepatocytes after systemic administration and to antigen-presenting cells after intramuscular injection. However, systemic RNA delivery to non-hepatocytes remains challenging, especially without targeting ligands such as antibodies, peptides, or aptamers. Here we report that piperazine-containing ionizable lipids (Pi-Lipids) preferentially deliver mRNA to immune cells in vivo without targeting ligands. After synthesizing and characterizing Pi-Lipids, we use high-throughput DNA barcoding to quantify how 65 chemically distinct LNPs functionally delivered mRNA (i.e., mRNA translated into functional, gene-editing protein) in 14 cell types directly in vivo. By analyzing the relationships between lipid structure and cellular targeting, we identify lipid traits that increase delivery in vivo. In addition, we characterize Pi-A10, an LNP that preferentially delivers mRNA to the liver and splenic immune cells at the clinically relevant dose of 0.3 mg/kg. These data demonstrate that high-throughput in vivo studies can identify nanoparticles with natural non-hepatocyte tropism and support the hypothesis that lipids with bioactive small-molecule motifs can deliver mRNA in vivo. Next-generation lipid nanoparticles that target non-hepatocytes could be important clinical tools. Using in vivo DNA barcoding, the authors identify piperazine-containing lipids deliver mRNA to immune cells without targeting ligands.

Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.

Lipid nanoparticles (LNPs) for the efficient delivery of drugs need to be designed for the particular administration route and type of drug. Here we report the design of LNPs for the efficient delivery of therapeutic RNAs to the lung via nebulization. We optimized the composition, molar ratios and structure of LNPs made of lipids, neutral or cationic helper lipids and poly(ethylene glycol) (PEG) by evaluating the performance of LNPs belonging to six clusters occupying extremes in chemical space, and then pooling the lead clusters and expanding their diversity. We found that a low (high) molar ratio of PEG improves the performance of LNPs with neutral (cationic) helper lipids, an identified and optimal LNP for low-dose messenger RNA delivery. Nebulized delivery of an mRNA encoding a broadly neutralizing antibody targeting haemagglutinin via the optimized LNP protected mice from a lethal challenge of the H1N1 subtype of influenza A virus, and delivered mRNA more efficiently than LNPs previously optimized for systemic delivery. A cluster approach to LNP design may facilitate the optimization of LNPs for other administration routes and therapeutics. Lipid nanoparticles can be optimized for the efficient delivery of therapeutic mRNAs to the lung via nebulization, as shown for the delivery of a therapeutic antibody in mice challenged with a lethal dose of the H1N1 influenza A virus.

Recent developments of organoids engineering and organ-on-a-chip microfluidic technologies have enabled the recapitulation of the major functions and architectures of microscale human tissue, including tumor pathophysiology. Nevertheless, there remain challenges in recapitulating the complexity and heterogeneity of tumor microenvironment. The integration of these engineering technologies suggests a potential strategy to overcome the limitations in reconstituting the perfusable microvascular system of large-scale tumors conserving their key functional features. Here, we review the recent progress of in vitro tumor-on-a-chip microfluidic technologies, focusing on the reconstruction of microvascularized organoid models to suggest a better platform for personalized cancer medicine.

The sonic hedgehog subtype of medulloblastoma (SHH MB) is associated with treatment failure and poor outcome. Current strategies utilizing whole brain radiation therapy result in deleterious off-target effects on the normal developing childhood brain. Most conventional chemotherapies remain limited by ineffective blood–brain barrier (BBB) penetrance. These challenges signify an unmet need for drug carriers that can cross the BBB and deliver drugs to targeted sites with high drug-loading efficiency and long-term stability. We herein leverage the enhanced stability and targeting ability of engineered highdensity lipoprotein-mimetic nanoparticles (eHNPs) to cross the BBB and deliver a SHH inhibitor effectively to the cancer stem-like cell population in SHH MB. Our microfluidic technology enabled highly reproducible production of multicomponent eHNPs incorporated with apolipoprotein A1, anti-CD15, and a SHH inhibitor (LDE225).We demonstrate the dual-targeted delivery and enhanced therapeutic effect of eHNP-A1-CD15-LDE225 via scavenger receptor class B type 1 (SR-B1) and CD15 on brain SHH MB cells in vitro, ex vivo, and in vivo. Moreover, we show that eHNP-A1 not only serves as a stable drug carrier, but also has a therapeutic effect itself through SR-B1-mediated intracellular cholesterol depletion in SHH MB cells. Through the facilitated and targeted cellular uptake of drugs and direct therapeutic role of this engineered biomimetic nanocarrier in SHH MB, our multifunctional nanoparticle provides intriguing therapeutic promise as an effective and potent nanomedicine for the treatment of SHH MB.

We examine whether board connections through shared directors influence firm disclosure policies. To overcome endogeneity challenges, we focus on an event that represents a significant change in firm disclosure policy: the cessation of quarterly earnings guidance. Our research design allows us to exploit the timing of director interlocks and therefore differentiate the director interlock effect on disclosure policy contagion from alternative explanations, such as endogenous director-firm matching or strategic board stacking. We find that firms are more likely to stop providing quarterly earnings guidance if they share directors with previous guidance stoppers. We also find that director-specific experience from prior guidance cessations matters for disclosure policy contagion. The positive effect of interlocked directors on the likelihood of quarterly earnings guidance cessation is particularly strong for firms with interlocked directors who experienced positive outcomes from prior guidance cessation decisions. Overall, our evidence is consistent with interlocked directors serving as conduits for information sharing that leads to the spread of corporate disclosure policies.

The endothelial microenvironment is critical in maintaining the health and function of the intimal layer in vasculature. In the context of cardiovascular disease (CVD), the vascular endothelium is the layer of initiation for the progression of atherosclerosis. While laminar blood flows are known to maintain endothelial homeostasis, disturbed flow conditions including those the endothelium experiences in the carotid artery are responsible for determining the fate of CVD progression. We present a microfluidic device designed to monitor the endothelium on two fronts: the real-time monitoring of the endothelial permeability using integrated electrodes and the end-point characterization of the endothelium through immunostaining. Our key findings demonstrate endothelial monolayer permeability and adhesion protein expression change in response to oscillatory shear stress frequency. These changes were found to be significant at certain frequencies, suggesting that a frequency threshold is needed to elicit an endothelial response. Our device made possible the real-time monitoring of changes in the endothelial monolayer and its end-point inspection through a design previously absent from the literature. This system may serve as a reliable research platform to investigate the mechanisms of various inflammatory complications of endothelial disorders and screen their possible therapeutics in a mechanistic and high-throughput manner.

This paper presents a delay- and energy-efficient approximate adder design exploiting an effective carry speculation scheme with error reduction. The proposed scheme reduces the delay and improves the energy efficiency without any significant accuracy degradation by effectively adding the predicted carry input using the OR operation. Additionally, the error reduction technique improves the overall computation accuracy at the expense of a few logic gates. As a result, the proposed adder achieves 3.84- and 7.79-times greater energy and energy-delay product (EDP) efficiencies than the traditional adder when implemented in 65-nm CMOS technology. In particular, when jointly analyzed with hardware accuracy, our design attains 69% and 70% reductions of the energy- and EDP-normalized mean error distance (NMED) products, respectively, compared to the other approximate adders under consideration. Furthermore, the proposed adder’s efficacy over the existing adders is demonstrated by adopting it in a machine learning application.