Investigating the use of sweet sorghum as a model for sugar accumulation in sugarcane

This paper outlines a current investigation of sugar accumulation in sweet sorghum to assist in understanding and simplifying this complex trait in sugarcane. A recombinant inbred line (RIL) sorghum population, between a sweet and a grain sorghum, has been developed and phenotyped for various morphological and agronomic traits related to grain yield, biomass and stem sugar content. A genetic linkage map will be constructed for the sweet sorghum population with the objective of identifying genomic regions associated with sucrose accumulation in sweet sorghum. This will lead to further work, including comparative mapping in sugarcane, to identify the extent to which sweet sorghum can be used as a model for investigating sugar accumulation in sugarcane.

Interface adaptor logic: A new model for interfacing peripherals in IP based designs

The introduction of standard on-chip buses has eased integration and boosted the production of IP functional cores. However, once an IP is bus specific retargeting to a different bus is time-consuming and tedious, and this reduces the reusability of the bus-specific IP. As new bus standards are introduced and different interconnection methods are proposed, this problem increases. Many solutions have been proposed, however these solutions either limit the IP block performance or are restricted to a particular platform. A new concept is presented that can connect IP blocks to a wide variety of interface architectures with low overhead. This is achieved through the use a special interface adaptor logic layer.

S. cerevisiae as a model for studying mutations in the human gene OPA1 associated with dominant optic atrophy and for drug discovery

L’atrofia ottica dominante (ADOA) è una malattia mitocondriale caratterizzata da difetti visivi, che si manifestano durante l’infanzia, causati da progressiva degenerazione delle cellule gangliari della retina (RGC). ADOA è una malattia genetica associata, nella maggior parte dei casi, a mutazioni nel gene OPA1 che codifica per la GTPasi mitocondriale OPA1, appartenente alla famiglia delle dinamine, principalmente coinvolta nel processo di fusione mitocondriale e nel mantenimento del mtDNA. Finora sono state identificate più di 300 mutazioni patologiche nel gene OPA1. Circa il 50% di queste sono mutazioni missenso, localizzate nel dominio GTPasico, che si pensa agiscano come dominanti negative. Questa classe di mutazioni è associata ad una sindrome più grave nota come “ADOA-plus”. Nel lievito Saccharomyces cerevisiae MGM1 è l’ortologo del gene OPA1: nonostante i due geni abbiano domini funzionali identici le sequenze amminoacidiche sono scarsamente conservate. Questo costituisce una limitazione all’uso del lievito per lo studio e la validazione di mutazioni patologiche nel gene OPA1, infatti solo poche sostituzioni possono essere introdotte e studiate nelle corrispettive posizioni del gene di lievito. Per superare questo ostacolo è stato pertanto costruito un nuovo modello di S. cerevisiae, contenente il gene chimerico MGM1/OPA1, in grado di complementare i difetti OXPHOS del mutante mgm1Δ. Questo gene di fusione contiene una larga parte di sequenza corrispondente al gene OPA1, nella quale è stato inserito un set di nuove mutazioni trovate in pazienti affetti da ADOA e ADOA-plus. La patogenicità di queste mutazioni è stata validata sia caratterizzando i difetti fenotipici associati agli alleli mutati, sia la loro dominanza/recessività nel modello di lievito. A tutt’oggi non è stato identificato alcun trattamento farmacologico per la cura di ADOA e ADOA-plus. Per questa ragione abbiamo utilizzato il nostro modello di lievito per la ricerca di molecole che agiscono come soppressori chimici, ossia composti in grado di ripristinare i difetti fenotipici indotti da mutazioni nel gene OPA1. Attraverso uno screening fenotipico high throughput sono state testate due differenti librerie di composti chimici. Questo approccio, noto con il nome di drug discovery, ha permesso l’identificazione di 23 potenziali molecole attive.

'Taking a sickie':Job satisfaction and job involvement as interactive predictors of absenteeism in a public organization

Correlations between absenteeism and work attitudes such as job satisfaction have often been found to be disappointingly weak. As prior work reveals, this might be due to ignoring interactive effects of attitudes with different attitude targets (e.g. job involvement and organizational commitment). Drawing on basic principles in personality research and insights about the situational variability of job satisfaction judgments, we proposed that similar interactions should be present also for attitudes with the same target. More specifically, it was predicted that job involvement affects absenteeism more if job satisfaction is low as this indicates a situation with weak constraints. Both attitudes were assessed in a sample of 436 employees working in a large civil service organization, and two indexes of absence data (frequency and time lost) were drawn from personnel records covering a 12-month period following the survey. Whereas simple correlations were not significant, a moderated regression documented that the hypothesized interaction was significant for both indicators of absence behaviour. As a range of controls (e.g. age, gender, job level) were accounted for, these findings lend strong support to the importance of this new, specific form of attitude interaction. Thus, we encourage researchers not only to consider interactions of attitudes with a different focus (e.g. job vs. organization) but also interactions between job involvement and job satisfaction as this will yield new insights into the complex function of attitudes in influencing absenteeism. © 2007 The British Psychological Society.

Probabilistic resource space model for managing resources in cyber-physical society

Classification is the most basic method for organizing resources in the physical space, cyber space, socio space and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multi-dimensional classification space. It supports generalization and specialization on multi-dimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM’s normal forms, operations and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.

A new DEA model for technology selection in the presence of ordinal data

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach. © 2012 Springer-Verlag London Limited.

Dynamic supply chain alignment:a new business model for peak performance in enterprise supply chains across all geographies

Dynamic supply chain alignment: a new business model for peak performance in enterprise supply chains across all geographies John Gattorna and friends, Farnham, Gower Publishing, 2009, 440pp., £60, ISBN 978-0-566-08822-3.

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.