Background & AimsWith the increasing prevalence of liver disease worldwide, there is an urgent clinical need for reliable methods to diagnose and stage liver pathology. Liver biopsy, the current gold standard, is invasive and limited by sampling and observer dependent variability. In this study, we aimed to assess the diagnostic accuracy of a novel magnetic resonance protocol for liver tissue characterisation.MethodsWe conducted a prospective study comparing our magnetic resonance technique against liver biopsy. The individual components of the scanning protocol were T1 mapping, proton spectroscopy and T2⁎ mapping, which quantified liver fibrosis, steatosis and haemosiderosis, respectively. Unselected adult patients referred for liver biopsy as part of their routine care were recruited. Scans performed prior to liver biopsy were analysed by physicians blinded to the histology results. The associations between magnetic resonance and histology variables were assessed. Receiver-operating characteristic analyses were also carried out.ResultsPaired magnetic resonance and biopsy data were obtained in 79 patients. Magnetic resonance measures correlated strongly with histology (rs = 0.68 p <0.0001 for fibrosis; rs = 0.89 p <0.001 for steatosis; rs = −0.69 p <0.0001 for haemosiderosis). The area under the receiver operating characteristic curve was 0.94, 0.93, and 0.94 for the diagnosis of any degree of fibrosis, steatosis and haemosiderosis respectively.ConclusionThe novel scanning method described here provides high diagnostic accuracy for the assessment of liver fibrosis, steatosis and haemosiderosis and could potentially replace liver biopsy for many indications. This is the first demonstration of a non-invasive test to differentiate early stages of fibrosis from normal liver.

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ALT/WDL and lipoma have overlapping MR imaging characteristics. The most reliable imaging discriminators of ALT/WDL were size of lesion and lipomatous content, but due to the overlap in the MRI appearances of lipoma and ALT/WDL, discrimination should be based on molecular pathology rather than imaging.

Pleomorphic rhabdomyosarcoma is the most common variant of this tumour in adults and has a very poor outcome. Two genes which are known to play a role in rhabdomyosarcoma development are KRas and p53. In the majority of human tumours, p53 abnormalities are point mutations that result in the expression of a mutant form of the protein. It is now hypothesized that these mutant forms of p53 may be playing an oncogenic role, over and above simple loss of the wild-type function. In this study, we use Cre-LoxP technology to develop a novel mouse model of rhabdomyosarcoma, crossing mice expressing a common KRas mutation (G12V) with mice that either lose p53 expression or express a mutant form of p53. We use this model to explore the different effects of p53 loss and mutation in the setting of an activating KRas mutation. We found that either complete loss of p53 (p53(fl/fl)) or the expression of one mutant p53 allele with concomitant loss of the second allele (p53(R172H/+)) resulted in the rapid development of rhabdomyosarcoma in 15/16 and 19/19 mice, respectively. In contrast, there was a marked difference between mice which lose a single copy of p53 (p53(fl/+)) and mice expressing a single copy of mutant p53 (p53(172H/+)). Fourteen out of 16 p53(R172H/) mice developed rhabdomyosarcoma, compared with two out of 31 p53(fl/+) mice. As a consequence of this, p53(fl/+) mice had a median lifespan nearly double that of the p53(R172H/+) mice. To underline the enhanced effect of p53 mutation in tumour progression, metastases were seen only in those mice which expressed the mutant form. These data demonstrate that mutant p53 can co-operate with activated, mutant KRas to influence tumourigenesis and metastatic potential, over and above simple loss of normal protein function.

A diagnosis of classical adamantinoma is suggested by an extensive lesion with moth-eaten margins and complete involvement of the medullary cavity on axial MR imaging. Misdiagnosis on needle biopsy may occur in up to one fifth of cases, and radiological features can assist in making the correct diagnosis.

In modern energy aware buildings, lighting control systems are put in place so to maximise the energy-efficiency of the lighting system without effecting the comfort of the occupant. In many cases this involves utilising a set of presence sensors, with actuators, to determine when to turn on/off or dim lighting, when it is deemed necessary. Such systems are installed using standard tuning values statically fixed by the system installer. This can cause inefficiencies and energy wastage as the control system is never optimised to its surrounding environment. In this paper, we investigate a Wireless Sensor Network (WSN) as a viable tool that can help in analysing and evaluating the energy-efficiency of an existing lighting control system in a low-cost and portable solution. We introduce LightWiSe (LIGHTting evaluation through WIreless SEnsors), a wireless tool which aims to evaluate lighting control systems in existing office buildings. LightWiSe determines points in the control system that exhibit energy wastage and to highlight areas that can be optimised to gain a greater efficiency in the system. It will also evaluate the effective energy saving to be obtained by replacing the control system with a more judicious energy saving solution. During a test performed in an office space, with a number of different lighting control systems we could highlight a number of areas to reduce waste and save energy. Our findings show that each system tested can be optimised to achieve greater efficiency. LightWiSe can highlight savings in the region of 50% to 70% that are achievable through optimising the current control system or installing an alternative.

This study assessed whether analysis of MDM2 copy number by fluorescence in situ hybridization (FISH) would help distinguish lipomas from atypical lipomatous tumors, otherwise referred to as well-differentiated liposarcomas, using a commercially available MDM2 FISH kit. 227 lipomatous and 201 non-lipomatous tumors were analyzed to assess its sensitivity and specificity. Of 178 mature lipomatous tumors, 86 were classified histologically as lipoma and 92 as atypical lipomatous tumor. Two of the lipomas harboring MDM2 amplification were reclassified as atypical lipomatous tumors. Overall, 13 atypical lipomatous tumors did not reveal MDM2 or CDK4 amplification, although this was reduced to 12 following analysis of multiple slides. Three of these cases revealed very occasional tumor cells harboring high-level MDM2 amplification, two had a dedifferentiated component, and MDM2 amplification was detected when one tumor recurred. The remaining six cases exhibited reactive/inflammatory features and were reclassified as lipomas. The findings indicate that MDM2 amplification is 93.5% sensitive for diagnosing atypical lipomatous tumor. A total of 2 of the 20 dedifferentiated liposarcomas failed to reveal MDM2 amplification. All atypical lipomatous tumors measured 410 cm, two dedifferentiated liposarcoma presented de novo at o10 cm, and B50% of lipomas measured 410 cm. Spindle cell lipomas, lipoblastomas, hibernomas and pleomorphic liposarcomas did not reveal MDM2 amplification. Of 201 nonlipomatous tumors, eight revealed MDM2 amplification or multiple faint alphoid 12 signals and were reclassified as dedifferentiated liposarcoma. Multiple faint alphoid 12 signals were observed in nine tumors from seven patients, an observation not previously reported on paraffin sections: these included four atypical lipomatous tumors, and three dedifferentiated liposarcomas, one previously diagnosed as a myxofibrosarcoma, all of which also revealed amplification of CDK4, although two lacked MDM2 amplification. MDM2 FISH test is a useful adjunct to histology for distinguishing lipoma from atypical lipomatous tumor. The limitations of molecular genetic tests must be known before introducing them into a clinical service.

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212)