Factors impacting knowledge transfer success in information systems outsourcing

Despite increased research interest on knowledge transfer in information systems (IS) outsourcing, the field still lacks sound and holistic understanding of the key factors influencing knowledge transfer success. The present paper attempts to provide a synthesis of existing theoretical perspectives and empirical findings related to the factors that facilitate or hamper knowledge transfer success in IS outsourcing. The data collection method is discussed and the key findings are presented. Conclusion is drawn and further research is suggested

T-government for benefit realisation

This paper proposes a model for t-Government and highlights the research agenda needed to increase understanding of transformational government and the processes involved in furthering the agenda of the t-Government. In particular, both an operational and a conceptual model for the effective involvement of citizens and businesses in government functioning have been proposed. This will help to define an agenda for t-Government research that emerges from national UK strategy and policy for e-Government. The main threads of t- Government encompass: (1) A citizen-centric delivery of public services or e-inclusion, (2) A shared services culture to maximize value added to clients, (3) The effective delivery and management of resources and skills within government or professionalism. All three threads should be addressed principally from the perspectives of delivery, evaluation and participation in view of benefit realisation as envisioned by Government strategic planning and policy directives (CabinetOffice, 2005). The management of change dimension of these phenomena have been included in the research agenda. In particular, research is needed to reshape the discourse towards emphasising a citizen centric approach that defines, develops, and benefits from public service. Decision makers in Government will need models of Governance that fulfil transformational objectives. They will also need models of benefits realisation within a strategic Governance framework. It has been argued that t-Government research should be addressing these relative voids

Leptin concentration in breast milk and its relationship to duration of lactation and hormonal status

BACKGROUND: Leptin, a hormone present in breast milk, is involved in energy regulation and metabolism. The objectives of this study were to assess leptin concentrations in breast milk during the first 180 days postpartum, and to determine the relationship between the concentrations of milk leptin and circulating hormone levels in lactating women. METHODS: Between April 2005 and January 2006, blood and breast milk samples were collected from 160 breastfeeding women enrolled either in the first three days (n = 37; colostrum), days 4–14 (n = 27; transitional milk), days 15–30 (n = 16; early mature milk), days 31–90 (n = 37; mature milk) or days 91–180 (n = 43; late mature milk) postpartum. Milk and serum leptin levels were measured by immunoradiometric assay. Cortisol was measured by radioimmunoassay method. Serum insulin, estradiol, prolactin and thyroxine were measured by chemiluminescent immunometric method. RESULTS: Leptin concentrations in breast milk were highest (3.28 ± 0.41 ng/ml) in colostrum, decreased during the first 180 days of lactation, showing a significant inverse relation (r = -0.694, p < 0.001) with the days of lactation. Colostrum leptin concentrations correlated with maternal serum leptin (r = 0.425, p < 0.01), cortisol (r = 0.549, p < 0.01) and thyroxine (r = -0.530, p < 0.01). Mature milk leptin concentrations correlated with maternal serum leptin (r = 0.547, p < 0.001), insulin (r = 0.331, p < 0.05) and thyroxine (r = -0.329, p < 0.01). Serum leptin concentrations correlated with serum insulin (r = 0.648, p < 0.001), estradiol (r = 0.639, p < 0.001), prolactin (r = 0.530, p < 0.001) and thyroxine (r = -0.327, p < 0.05) concentrations during days 1–3 postpartum. During 15–180 postpartum days, serum leptin concentrations correlated with serum insulin (r = 0.271, p < 0.01), and thyroxine (r = -0.345, p < 0.001). CONCLUSION: Leptin concentrations in breast milk decrease with time during lactation and show significant relationships with other maternal hormones

Nitrogen and sulfur deprivation differentiate lipid accumulation targets of Chlamydomonas reinhardtii

Cataloged from PDF version of article.Nitrogen (N) and sulfur (S) have inter-related and distinct impacts on microalgal metabolism; with N starvation having previously been reported to induce elevated levels of the biodiesel feedstock material triacylglycerol (TAG), while S deprivation is extensively studied for its effects on biohydrogen production in microalgae.1,2 We have previously demonstrated that N- and S-starved cells of Chlamydomonas reinhardtii display different metabolic trends, suggesting that different response mechanisms exist to compensate for the absence of those two elements.3 We used C. reinhardtii CC-124 mt(-) and CC-125 mt(+) strains to test possible metabolic changes related to TAG accumulation in response to N and S deprivation, considering that gamete differentiation in this organism is mainly regulated by N.4 Our findings contribute to the understanding of microalgal response to element deprivation and potential use of element deprivation for biodiesel feedstock production using microalgae, but much remains to be elucidated on the precise contribution of both N and S starvation on microalgal metabolism. © 2012 Landes Bioscience

Collective Dynamics Differentiates Functional Divergence in Protein Evolution

Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function

Improving Prostate Cancer Detection with Breast Histopathology Images

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.Comment: 9 pages, 2 figure

Evaluating the predictive value of quantec rectum tolerance dose suggestions on acute rectal toxicity in prostate carcinoma patients treated with IMRT

AimTo investigate the predictive value of convenience of rectum dosimetry with Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) dose limits, maximum rectum dose (Dmax), total rectal volume (TVrectum), rectal volume included in PTV (VrectumPTV) on Grade 2–3 acute rectal toxicity for utilization in clinical practice.BackgroundNumerous previous data have reported frequent acute proctitis after external-beam RT of prostate cancer. Predicting toxicity limited with dose information is inadequate in clinical practice due to comorbidities and medications used.Materials and MethodSixty-four non-metastatic prostate cancer patients treated with IMRT were enrolled. Patients were treated to a total dose of 70–76Gy. Rectal dose volume histograms (DVH) of all patients were evaluated retrospectively, and a QUANTEC Score between 0 and 5 was calculated for each patient. The correlation between the rectal DVH data, QUANTEC score, TVrectum, VrectumPTV, rectum Dmax and Grade 2–3 rectal toxicity was investigated.ResultsIn the whole group grade 1, 2 and 3 acute rectal toxicities were 25%, 18.8% and 3.1%, respectively. In the DVH data, rectum doses of all patients were under RTOG dose limits. Statistically significant correlation was found between grade 2–3 rectal toxicity and TVrectum (p=0,043); however. It was not correlated with QUANTEC score, VrectumPTV and Dmax.ConclusionOur results were not able to show any significant correlation between increasing convenience with QUANTEC limits and lower rectal toxicity. Conclusively, new dosimetric definitions are warranted to predict acute rectal toxicity more accurately in prostate cancer patients during IMRT treatment

A Casebase of Intelligent Buildings for E-learning Systems

This paper presents the conceptual description of a knowledge base for teaching and learning the programme of MSc Intelligent Buildings, which is being developed at the University of Reading and being developed into a global Master degree program. The knowledge base aims to support currently used e-learning tools such as Blackboard, and to make a knowledge engine to facilitate other relevant research into intelligent buildings. A pilot knowledge base is introduced to demonstrate the usability and acceptability of the knowledge base, which is designed according to a standard classification of intelligent buildings and a generic knowledge format to regulate case collection and reuse in teaching and learning. This paper initially covers undetermined issues such as case-based teaching and learning in intelligent buildings related programs

Aviation to Grid: Airport Charging Infrastructure for Electric Aircraft

To decarbonise air transport sector, all-electric and hybrid-electric aircraft have advanced rapidly, particularly for small or regional electric aircraft (EA). However, the airport energy infrastructure for EA charging remains a key challenge owing to the high-power charging demand with highly-scheduled charging patterns. This paper develops an optimal airport charging infrastructure for EA. Battery swap and plug-in charging systems are proposed and compared in terms of charging schedule flexibility, costs and revenue. The novel mechanism “Aviation to Grid†is proposed to enable the bi-directional power flow interaction between power grid and EA charging system. The two alternative charging systems are implemented with different penetration levels of electric domestic flights in five case studies of London Gatwick airport. The optimal EA charging schedules with hourly generation dispatch and EA charging demand are developed. A conclusion is made that the battery swap is more economic when the EA penetration level is lower than 10%. The plug-in charge becomes a cost-effective option when the EA penetration level increases above 10%.EPSRC Supergen Energy Networks Hub Flexible Fund: ‘GREEN Airport - Grid flexibility by Electrifying Energy Networks for Airport (SENFC1-023)’; Department for Transport Transport - Technology Research Innovation Grant (T-TRIG): ‘Aircraft to Grid: Hybrid and Smart Charging for Electric Aircraft’, in the United Kingdom.http://www.energy-proceedings.org/wp-content/uploads/enerarxiv/1607610466.pd

Effects of laser ablated silver nanoparticles on Lemna minor

Cataloged from PDF version of article.The present study investigates and models the effect of laser ablated silver nanoparticles (AgNPs) on the development of the aquatic macrophyte Lemna minor. Toxic effects of five different AgNP concentrations (8, 16, 32, 96 and 128μgL-1) on L. minor were recorded over seven days under simulated natural conditions. Biosorption of AgNPs by L. minor was modeled using four sorption isotherms, and the sorption behavior was found to agree most closely with the Langmuir-Freundlich model (R2=0.997). While toxic effects of AgNPs could be observed in all models and concentrations, the greatest increase in toxicity was in the 8-32μgL-1 range. Dry weight- and frond number-based inhibition experiments suggest that growth inhibition does not necessarily scale with AgNP concentration, and that slight fluctuations in inhibition rates exist over certain concentration ranges. Very close fits (R2=0.999) were obtained for all removal models, suggesting that the fluctuations are not caused by experimental variation. In addition, L. minor was found to be a successful bioremediation agent for AgNPs, and displayed higher removal rates for increasing AgNP doses. FT-IR spectroscopy suggests that carbonyl groups are involved in AgNP remediation. © 2014 Elsevier Ltd