Guest editorial

The 21st International EurOMA (EurOMA, 2014) Conference was hosted by Università degli Studi di Palermo. The conference theme was Operations Management in an Innovation Economy. According to innovation economists what primarily drives economic growth in today’s knowledge-based economy is not capital accumulation but innovative capacity spurred by appropriable knowledge and technological externalities. Economics growth in innovation economics is the end- product of knowledge, R&D expenditures, licenses, technological spillovers, and externalities between collaborative firms, i.e. supply chains and networks of innovation. When firms do not explicitly acknowledge and manage their operations as a concurrent activity to the management of innovation, they often encounter problems late in product development, or with manufacturing launch, logistical support, quality control, and production costs. As such, innovation process and operations management should be coordinated, rather than being viewed as separate sets of decisions and activities. We received 592 abstracts and used a doubled-blind review process, involving 127 members of the Scientific Committee, to review 586 abstracts (six abstracts were desk rejected) and provide feedback to the authors. Of these, 513 were accepted and 73 rejected. The accepted abstracts resulted in 405 full papers in the Scientific Programme. With three papers subsequently withdrawn, there were 402 paper presentations in prospect. The most recurrent OM themes were: sustainability in operations and logistics (42 papers); supply chain management (35 papers); innovation, product and service development (32 papers); managing inter-firm relationships in supply chains (30 papers); healthcare OM (21 papers); lean and agile operations (21 papers). The Scientific Programme incorporated 134 parallel sessions and was enriched by two keynote speakers: Professor Robert Handfield (Bank of America University Distinguished Professor of Supply Chain Management, North Carolina State University) and the Chief Operations Officer of Luxottica, Massimo Vian, who provided insightful reflections on the conference theme from their academic and industry perspectives, respectively. In addition there were six special sessions providing unique opportunities for engagement and insights on teaching in OM, crowdsourcing and open innovation in OM, OM as practice, OM research in the fashion industry, new supply chains, and the role of social media in OM and EurOMA. Also, besides this interesting topic-specific special sessions, the conference hosted a “Meet the Editors” session with editors and co-editors from eight OM journals

Introducing \u201chealthcare resilience\u201d in clinical risk management

This paper reports the results of an explorative case study in an Italian hospital and presents a conceptual framework for healthcare resilience, which clarifies why and how healthcare structures need to be resilient in order to fully deal with clinical risk. In other words, it is not sufficient to take the \u201cpath to zero harm\u201d. Healthcare companies have also to pursue strategies for maximizing the organizational resilience, in terms of resistance, reliability, redundancy and flexibility. This is quite important because, no matter which CRM technique and/or best practice is adopted, sometime adverse events occur. And, in this case, being resilient can help in dampening their negative consequences. By extending the focus of traditional clinical risk management to different kinds of risk sources (not just patient safety threats) and to different kinds of risk minimization strategies (not just minimize the likelihood of occurrence but also the risk magnitude) this paper contributes to the literatures on operations management in healthcare. The conceptualization of \u201chealthcare resilience\u201d and the in-depth case results allow us to offer a number of suggestions and ideas for developing further research in the field of healthcare operations management

Inventory record inaccuracy in supply chains: the role of workers’ behavior

Purpose This research aims at exploring the effect of inventory record inaccuracy due to behavioral aspects of workers on the order and inventory variance amplification. Design/methodology/approach We adopt a continuous-time analytical approach to describe the effect of inbound throughput on the inventory and order variance amplification due to the workload pressure and arousal of workers. The model is numerically solved through simulation and results are analyzed with statistical general linear model. Findings Inventory management policies that usually dampen variance amplification are not effective when inaccuracy is generated due to workers’ behavioral aspects. Specifically, the psychological sensitivity and stability of workers to deal with a given range of operational conditions have a combined and multiplying effect over the amplification of order and inventory variance generated by her/his errors. Research limitations/implications The main limitation of our research is that we model workers’ behavior by inheriting a well-known theory from psychology that assumes a U-shaped relationship between stress and errors. We do not validate this relationship in the specific context of inventory operations. Practical implications The paper gives suggestions for managers who are responsible for designing order and inventory policies on how to take into account workers’ behavioral reaction to work pressure. Originality/value The logistics management literature does not lack of research works on behavioral decision making causes of order and inventory variance amplification. Contrarily, this paper investigates a new kind of behavioral issue, namely the impact of psycho-behavioral aspects of workers on variance amplification

Protein dynamics with off-lattice Monte Carlo moves

A Monte Carlo method for dynamics simulation of all-atom protein models is introduced, to reach long times not accessible to conventional molecular dynamics. The considered degrees of freedom are the dihedrals at C$_\alpha$-atoms. Two Monte Carlo moves are used: single rotations about torsion axes, and cooperative rotations in windows of amide planes, changing the conformation globally and locally, respectively. For local moves Jacobians are used to obtain an unbiased distribution of dihedrals. A molecular dynamics energy function adapted to the protein model is employed. A polypeptide is folded into native-like structures by local but not by global moves.Comment: 10 pages, 4 Postscript figures, uses epsf.sty and a4.sty; scheduled tentatively for Phys.Rev.E issue of 1 March 199

Terahertz underdamped vibrational motion governs protein-ligand binding in solution

Low-frequency collective vibrational modes in proteins have been proposed as being responsible for efficiently directing biochemical reactions and biological energy transport. However, evidence of the existence of delocalized vibrational modes is scarce and proof of their involvement in biological function absent. Here we apply extremely sensitive femtosecond optical Kerr-effect spectroscopy to study the depolarized Raman spectra of lysozyme and its complex with the inhibitor triacetylchitotriose in solution. Underdamped delocalized vibrational modes in the terahertz frequency domain are identified and shown to blue-shift and strengthen upon inhibitor binding. This demonstrates that the ligand-binding coordinate in proteins is underdamped and not simply solvent-controlled as previously assumed. The presence of such underdamped delocalized modes in proteins may have significant implications for the understanding of the efficiency of ligand binding and protein–molecule interactions, and has wider implications for biochemical reactivity and biological function

Establishment of a national network of cetacean monitoring within the marine strategy

CONISMA, CNR and CIRCE, involved Italian research units (RUs) working on cetaceans to joina National Network answering the Marine Strategy Framework Directive (MSFD) requirements by sharing monitoring data. Data obtained during the 2016 monitoring campaigns by 13 RUs are presented here

R2R - software to speed the depiction of aesthetic consensus RNA secondary structures

<p>Abstract</p> <p>Background</p> <p>With continuing identification of novel structured noncoding RNAs, there is an increasing need to create schematic diagrams showing the consensus features of these molecules. RNA structural diagrams are typically made either with general-purpose drawing programs like Adobe Illustrator, or with automated or interactive programs specific to RNA. Unfortunately, the use of applications like Illustrator is extremely time consuming, while existing RNA-specific programs produce figures that are useful, but usually not of the same aesthetic quality as those produced at great cost in Illustrator. Additionally, most existing RNA-specific applications are designed for drawing single RNA molecules, not consensus diagrams.</p> <p>Results</p> <p>We created R2R, a computer program that facilitates the generation of aesthetic and readable drawings of RNA consensus diagrams in a fraction of the time required with general-purpose drawing programs. Since the inference of a consensus RNA structure typically requires a multiple-sequence alignment, the R2R user annotates the alignment with commands directing the layout and annotation of the RNA. R2R creates SVG or PDF output that can be imported into Adobe Illustrator, Inkscape or CorelDRAW. R2R can be used to create consensus sequence and secondary structure models for novel RNA structures or to revise models when new representatives for known RNA classes become available. Although R2R does not currently have a graphical user interface, it has proven useful in our efforts to create 100 schematic models of distinct noncoding RNA classes.</p> <p>Conclusions</p> <p>R2R makes it possible to obtain high-quality drawings of the consensus sequence and structural models of many diverse RNA structures with a more practical amount of effort. R2R software is available at <url>http://breaker.research.yale.edu/R2R</url> and as an Additional file.</p

The Presence of the Iron-Sulfur Motif Is Important for the Conformational Stability of the Antiviral Protein, Viperin

Viperin, an antiviral protein, has been shown to contain a CX3CX2C motif, which is conserved in the radical S-adenosyl-methionine (SAM) enzyme family. A triple mutant which replaces these three cysteines with alanines has been shown to have severe deficiency in antiviral activity. Since the crystal structure of Viperin is not available, we have used a combination of computational methods including multi-template homology modeling and molecular dynamics simulation to develop a low-resolution predicted structure. The results show that Viperin is an α -β protein containing iron-sulfur cluster at the center pocket. The calculations suggest that the removal of iron-sulfur cluster would lead to collapse of the protein tertiary structure. To verify these predictions, we have prepared, expressed and purified four mutant proteins. In three mutants individual cysteine residues were replaced by alanine residues while in the fourth all the cysteines were replaced by alanines. Conformational analyses using circular dichroism and steady state fluorescence spectroscopy indicate that the mutant proteins are partially unfolded, conformationally unstable and aggregation prone. The lack of conformational stability of the mutant proteins may have direct relevance to the absence of their antiviral activity

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data