Evaluation of Coulomb potential in a triclinic cell with periodic boundary conditions

Lekner and Sperb's work on the evaluation of Coulomb energy and forces under periodic boundary conditions is generalized that makes it possible to use a triclinic unit cell in simulations in 3D rather than just an orthorhombic cell. The expressions obtained are in a similar form as previously obtained by Lekner and Sperb for the especial case of orthorhombic cell

Treatment options for severe hypertriglyceridemia (SHTG): the role of apheresis

Hypertriglyceridemia is associated with a number of severe diseases such as acute pancreatitis and coronary artery disease. In severe hypertriglyceridemia (SHTG, triglycerides > 1,000 mg/dL), rapid lowering of plasma triglycerides (TG) has to be achieved. Treatment regimes include nutritional intervention, the use of antihyperlipidemic drugs, and therapeutic apheresis. Apheretic treatment is indicated in medical emergencies such as hypertriglyceridemic pancreatitis

Cell-based screen for altered nuclear phenotypes reveals senescence progression in polyploid cells after Aurora kinase B inhibition.

Cellular senescence is a widespread stress response and is widely considered to be an alternative cancer therapeutic goal. Unlike apoptosis, senescence is composed of a diverse set of subphenotypes, depending on which of its associated effector programs are engaged. Here we establish a simple and sensitive cell-based prosenescence screen with detailed validation assays. We characterize the screen using a focused tool compound kinase inhibitor library. We identify a series of compounds that induce different types of senescence, including a unique phenotype associated with irregularly shaped nuclei and the progressive accumulation of G1 tetraploidy in human diploid fibroblasts. Downstream analyses show that all of the compounds that induce tetraploid senescence inhibit Aurora kinase B (AURKB). AURKB is the catalytic component of the chromosome passenger complex, which is involved in correct chromosome alignment and segregation, the spindle assembly checkpoint, and cytokinesis. Although aberrant mitosis and senescence have been linked, a specific characterization of AURKB in the context of senescence is still required. This proof-of-principle study suggests that our protocol is capable of amplifying tetraploid senescence, which can be observed in only a small population of oncogenic RAS-induced senescence, and provides additional justification for AURKB as a cancer therapeutic target.This work was supported by the University of Cambridge, Cancer Research UK, Hutchison Whampoa; Cancer Research UK grants A6691 and A9892 (M.N., N.K., C.J.T., D.C.B., C.J.C., L.S.G, and M.S.); a fellowship from the Uehara Memorial Foundation (M.S.).This is the author accepted manuscript. The final version is available from the American Society for Cell Biology via http://dx.doi.org/10.1091/mbc.E15-01-000

Trust, control and knowledge transfer in small business networks

The ability to transfer knowledge effectively in the networks of small and medium-sized firms (SMEs) is paramount for supporting firm competitiveness. Our research is the first one that explores the joint effect of trust and control mechanisms on knowledge transfer in the case of networks of SMEs. We use a multiple case study approach based on six Italian networks of SMEs. We analyse the joint impact of different ethical based trustworthiness factors—namely benevolence and integrity—and the levers of control (LOCs)—namely, belief, boundary, diagnostic and interactive LOCs—on knowledge transfer between SMEs in networks. We find that trust substitutes for the implementation of boundary, diagnostic, and belief tools, while it works jointly with interactive tools in order to support knowledge transfer. These insights not only provide a rich foundation for follow-up research, but also inform SME managers about how to increase the effectiveness and efficiency of knowledge transfer with their network partners

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure