Cloning and characterisation of chlorophyll synthase from Avena sativa

The chlorophyll synthase gene from oat (Avena sativa) was cloned and expressed in Escherichia coli. The deduced amino acid sequence consists of 378 amino acids including a presequence, of 46 amino acids. Deletion mutants show that a core protein comprising amino acid residues 88 to 377 is enzymatically active. The sequence of the mature protein shows 85% identity with the chlorophyll synthase of Arabidopsis thaliana and 62% identity with the chlorophyll synthase of Synechocystis PCC 6803. The gene is constitutively expressed as the same transcript level is found in dark-grown and in light-grown seedlings. The enzyme requires magnesium ions for activity; manganese ions can reconstitute only part of the activity. Diacetyl and N-phenylmaleimide (NPM) inhibit the enzyme activity. Site-directed mutagenesis reveals that, out of the 4 Arg residues present in the active core protein, Arg-91 and Arg-161 are essential for the activity. Five cysteine residues are present in the core protein, of which only Cys-109 is essential for the enzyme activity. Since the wild-type and all other Cys-mutants with the exception of the mutant C304A are inhibited by N-phenylmaleimide, we conclude that the inhibitor binds to a non-essential Cys residue to abolish activity. The role of the various Arg and Cys residues is discussed

Stochastic Methods for Zero Energy Quantum Scattering

We investigate the use of stochastic methods for zero energy quantum scattering based on a path integral approach. With the application to the scattering of a projectile from a nuclear many body target in mind, we use the potential scattering of a particle as a test for the accuracy and efficiency of several methods. To be able to deal with complex potentials, we introduce a path sampling action and a modified scattering observable. The approaches considered are the random walk, where the points of a path are sequentially generated, and the Langevin algorithm, which updates an entire path. Several improvements are investigated. A cluster algorithm for dealing with scattering problems is finally proposed, which shows the best accuracy and stability.Comment: 40 pages LaTeX, 1 Postscript file containig 20 figures; execute main.tex file, which automatically will include other file

A Monte-Carlo Approach to Zero Energy Quantum Scattering

Monte-Carlo methods for zero energy quantum scattering are developed. Starting from path integral representations for scattering observables, we present results of numerical calculations for potential scattering and scattering off a schematic $^4 \rm He$ nucleus. The convergence properties of Monte-Carlo algorithms for scattering systems are analyzed using stochastic differential equation as a path sampling method.Comment: 30 pages, LaTeX, 8 (uuencoded, tared and gziped) postscript figure

Distributed regression modeling for selecting markers under data protection constraints

Data protection constraints frequently require a distributed analysis of data, i.e., individual-level data remains at many different sites, but analysis nevertheless has to be performed jointly. The corresponding aggregated data is often exchanged manually, requiring explicit permission before transfer, i.e., the number of data calls and the amount of data should be limited. Thus, only simple aggregated summary statistics are typically transferred with just a single call. This does not allow for more complex tasks such as variable selection. As an alternative, we propose a multivariable regression approach for identifying important markers by automatic variable selection based on aggregated data from different locations in iterative calls. To minimize the amount of transferred data and the number of calls, we also provide a heuristic variant of the approach. When performing a global data standardization, the proposed methods yields the same results as when pooling individual-level data. In a simulation study, the information loss introduced by a local standardization is seen to be minimal. In a typical scenario, the heuristic decreases the number of data calls from more than 10 to 3, rendering manual data releases feasible. To make our approach widely available for application, we provide an implementation on top of the DataSHIELD framework

The JuliaConnectoR: a functionally oriented interface for integrating Julia in R

Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we decided to base communication between R and Julia on TCP, using an optimized binary format for exchanging data. Our package also specifically contains features that allow for a convenient interactive use in R. This makes it easy to develop R extensions with Julia or to simply call functionality from Julia packages in R. Interacting with Julia objects and calling Julia functions becomes user-friendly, as Julia functions and variables are made directly available as objects in the R workspace. We illustrate the further features of our package with code examples, and also discuss advantages over the two alternative packages JuliaCall and XRJulia. Finally, we demonstrate the usage of the package with a more extensive example for employing neural ordinary differential equations, a recent deep learning technique that has received much attention. This example also provides more general guidance for integrating deep learning techniques from Julia into R.Comment: 23 pages, 3 figures, 4 table

Attracting Talent through Sustainability: Leading question - Does Sustainability Help Attract and Retain Talent

Facing significant talent shortages, many hospitality organisations struggle to attract, develop, and retain talent, and ultimately are not able to develop sustainable talent pipelines. In this chapter, we consider how organisations in the hospitality industry can introduce sustainable practices to manage talent. The chapter first presents an overview of the meaning of talent and talent management. We then introduce sustainability in the context of talent management and present three aspects that relate to a sustainable approach: (1) diverse and creative talent, (2) employer branding and employee value proposition, and (3) coopetition as an alternative to the competitive narrative in the hospitality industry. We conclude that sustainable approaches to talent management can aid talent attraction and retention. However, they must meet the organisational needs and employee needs alike, and therefore we advocate a differentiated approach which that allows for strategic differentiation while equally providing learning, development, and growth for all employees. The hospitality industry needs to become better in at addressing foundational issues such as compensation, working conditions, and career paths, while at the same time exploring more innovative approaches to managing talent in the future

On the spin--boson model with a sub--Ohmic bath

We study the spin--boson model with a sub--Ohmic bath using infinitesimal unitary transformations. Contrary to some results reported in the literature we find a zero temperature transition from an untrapped state for small coupling to a trapped state for strong coupling. We obtain an explicit expression for the renormalized level spacing as a function of the bare papameters of the system. Furthermore we show that typical dynamical equilibrium correlation functions exhibit an algebaric decay at zero temperature.Comment: 9 pages, 2 Postscript figure