Multiferroic and Ferroic Topological Order in Ligand-Functionalized Germanene and Arsenene

Two-dimensional (2D) materials that exhibit ferroelectric, ferromagnetic, or topological order have been a major focal topic of nanomaterials research in recent years. The latest efforts in this field explore 2D quantum materials that host multiferroic or concurrent ferroic and topological order. We present a computational discovery of multiferroic state with coexisting ferroelectric and ferromagnetic order in recently synthesized CH2OCH3-functionalized germanene. We show that an electric-field-induced rotation of the ligand CH2OCH3 molecule can serve as the driving mechanism to switch the electric polarization of the ligand molecule, while unpassivated Ge p(z) orbits generate ferromagnetism. Our study also reveals coexisting ferroelectric and topological order in ligand-functionalized arsenene, which possesses a switchable electric polarization and a Dirac transport channel. These findings offer insights into the fundamental physics underlying these coexisting quantum orders and open avenues for achieving states of matter with multiferroic or ferroic-topological order in 2D-layered materials for innovative memory or logic device implementations

CEO Turnover, Equity-Based Compensation And Firms Investment Decisions

This paper examines the impact of the newly appointed CEOs on firms future investment decisions and whether the relation is affected by the equity-based compensation, corporate governance provisions and other CEO characteristics. Using CEO turnover data from 1992-2004, the results show that new CEOs with high options-based compensation, following forced turnover and with shorter organization tenure, are associated with high R&D and advertisement investments. These results are consistent with the managerial incentive effect and the dismissal effect

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.ImportanceTo fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available

Restarted Hessenberg method for solving shifted nonsymmetric linear systems

It is known that the restarted full orthogonalization method (FOM) outperforms the restarted generalized minimum residual (GMRES) method in several circumstances for solving shifted linear systems when the shifts are handled simultaneously. Many variants of them have been proposed to enhance their performance. We show that another restarted method, the restarted Hessenberg method [M. Heyouni, M\'ethode de Hessenberg G\'en\'eralis\'ee et Applications, Ph.D. Thesis, Universit\'e des Sciences et Technologies de Lille, France, 1996] based on Hessenberg procedure, can effectively be employed, which can provide accelerating convergence rate with respect to the number of restarts. Theoretical analysis shows that the new residual of shifted restarted Hessenberg method is still collinear with each other. In these cases where the proposed algorithm needs less enough CPU time elapsed to converge than the earlier established restarted shifted FOM, weighted restarted shifted FOM, and some other popular shifted iterative solvers based on the short-term vector recurrence, as shown via extensive numerical experiments involving the recent popular applications of handling the time fractional differential equations.Comment: 19 pages, 7 tables. Some corrections for updating the reference

Lysosomal acid lipase in mesenchymal stem cell stimulation of tumor growth and metastasis

Bone marrow mesenchymal stem cells (MSCs) are an important participant in the tumor microenvironment, in which they promote tumor growth and progression. Here we report for the first time that depletion of lysosomal acid lipase (LAL) in MSCs impairs their abilities to stimulate tumor growth and metastasis both in allogeneic and syngeneic mouse models. Reduced cell viability was observed in LAL-deficient (lal-/-) MSCs, which was a result of both increased apoptosis and decreased proliferation due to cell cycle arrest. The synthesis and secretion of cytokines and chemokines that are known to mediate MSCs' tumor-stimulating and immunosuppressive effects, i.e., IL-6, MCP-1 and IL-10, were down-regulated in lal-/- MSCs. When tumor cells were treated with the conditioned medium from lal-/- MSCs, decreased proliferation was observed, accompanied by reduced activation of oncogenic intracellular signaling molecules in tumor cells. Co-injection of lal-/- MSCs and B16 melanoma cells into wild type mice not only induced CD8+ cytotoxic T cells, but also decreased accumulation of tumor-promoting Ly6G+CD11b+ myeloid-derived suppressor cells (MDSCs), which may synergistically contribute to the impairment of tumor progression. Furthermore, lal-/- MSCs showed impaired differentiation towards tumor-associated fibroblasts. In addition, MDSCs facilitated MSC proliferation, which was mediated by MDSC-secreted cytokines and chemokines. Our results indicate that LAL plays a critical role in regulating MSCs' ability to stimulate tumor growth and metastasis, which provides a mechanistic basis for targeting LAL in MSCs to reduce the risk of cancer metastasis

Approximation and Generalization of DeepONets for Learning Operators Arising from a Class of Singularly Perturbed Problems

Singularly perturbed problems present inherent difficulty due to the presence of a thin boundary layer in its solution. To overcome this difficulty, we propose using deep operator networks (DeepONets), a method previously shown to be effective in approximating nonlinear operators between infinite-dimensional Banach spaces. In this paper, we demonstrate for the first time the application of DeepONets to one-dimensional singularly perturbed problems, achieving promising results that suggest their potential as a robust tool for solving this class of problems. We consider the convergence rate of the approximation error incurred by the operator networks in approximating the solution operator, and examine the generalization gap and empirical risk, all of which are shown to converge uniformly with respect to the perturbation parameter. By utilizing Shishkin mesh points as locations of the loss function, we conduct several numerical experiments that provide further support for the effectiveness of operator networks in capturing the singular boundary layer behavior

A Mechanism For Converting A Relational Database Into An Object-Oriented Model: An AIS Application

The object-oriented (OO) approach in system design and development is gaining popularity.  In the management information systems literature, OO system development is viewed as superior to conventional systems development because of advantages such as easier modeling, more efficient model reuse, and more convenient maintenance (Booch 1994; Briand, et al. 1999; Coleman, et al. 1994; Cockburn 1999).  Several studies have explored the applicability of the OO paradigm for the design and implementation of accounting information systems (AIS) and the advantages of OO design for this purpose (Adamson and Dilts 1995, Chu 1992a, 1992b; Kandelin and Lin 1992; Murthy and Wiggins 1993; Verdaasdonk 2003).  Nevertheless, OO techniques are often applied only to front-end applications while a relational database is generally used to store data at the back-end.  Based on an existing relational database model for a retail enterprise, this paper contributes to the AIS literature by providing a mechanism for transforming a relational database into an OO data model.

Transthyretin Stimulates Tumor Growth through Regulation of Tumor, Immune, and Endothelial Cells

Early detection of lung cancer offers an important opportunity to decrease mortality while it is still treatable and curable. Thirteen secretory proteins that are Stat3 downstream gene products were identified as a panel of biomarkers for lung cancer detection in human sera. This panel of biomarkers potentially differentiates different types of lung cancer for classification. Among them, the transthyretin (TTR) concentration was highly increased in human serum of lung cancer patients. TTR concentration was also induced in the serum, bronchoalveolar lavage fluid, alveolar type II epithelial cells, and alveolar myeloid cells of the CCSP-rtTA/(tetO)7-Stat3C lung tumor mouse model. Recombinant TTR stimulated lung tumor cell proliferation and growth, which were mediated by activation of mitogenic and oncogenic molecules. TTR possesses cytokine functions to stimulate myeloid cell differentiation, which are known to play roles in tumor environment. Further analyses showed that TTR treatment enhanced the reactive oxygen species production in myeloid cells and enabled them to become functional myeloid-derived suppressive cells. TTR demonstrated a great influence on a wide spectrum of endothelial cell functions to control tumor and immune cell migration and infiltration. TTR-treated endothelial cells suppressed T cell proliferation. Taken together, these 13 Stat3 downstream inducible secretory protein biomarkers potentially can be used for lung cancer diagnosis, classification, and as clinical targets for lung cancer personalized treatment if their expression levels are increased in a given lung cancer patient in the blood