Predictive position and force control for shape memory alloy cylinders

In this paper, a linear lightweight electric cylinder constructed using shape memory alloy (SMA) is proposed. Spring SMA is used as the actuator to control the position and force of the cylinder rod. The model predictive control algorithm is investigated to compensate SMA hysteresis phenomenon and control the cylinder. In the predictive algorithm, the future output of the cylinder is computed based on the cylinder model, and the control signal is computed to minimize the error and power criterion. The cylinder model parameters are estimated by an online identification algorithm. Experimental results show that the SMA cylinder is able to precisely control position and force by using the predictive control strategy though the hysteresis effect existing in the actuator. The performance of the proposed controller is compared with that of a conventional PID controller

Chymotrypsin-like serine proteinases are involved in the maintenance of cell viability

An increasing number of studies have implicated serine proteinases in the development of apoptosis. In this study, we assessed the ability of a set of highly specific irreversible inhibitors (activity probes), incorporating an a-amino alkane diphenyl phosphonate moiety, to modulate cell death. In an initial assessment of the cellular toxicity of these activity probes, we discovered that one example, N-a-tetramethylrhodamine phenylalanine diphenylphosphonate {TMR-Phe^P(OPh)₂} caused a concentration-dependent decrease in the viability of HeLa and U251 mg cells. This reduced cell viability was associated with a time-dependent increase in caspase-3 activity, PARP cleavage and phosphatidylserine translocation, establishing apoptosis as the mechanism of cell death. SDS-PAGE analysis of cell lysates prepared from the HeLa cells treated with TMR-Phe^P(OPh)₂, revealed the presence of a fluorescent band of molecular weight 58 kDa. Given that we have previously reported on the use of this type of activity probe to reveal active proteolytic species, we believe that we have identified a chymotrypsin-like serine proteinase activity integral to the maintenance of cell viability.

Maintenance therapy with cisapride after healing of erosive oesophagitis:a double-blind placebo-controlled trial

There are few data on the role of prokinetic agents as maintenance therapy in moderately severe reflux oesophagitis despite the high relapse rate of this condition after healing.

Predictive markers for colorectal cancer:current status and future prospects

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. Although there is clear evidence of the benefit of chemotherapy in adjuvant and metastatic settings, its use continues to be suboptimal because of intrinsic or acquired drug resistance. 5-Fluorouracil continues to be the mainstay of CRC therapy, and combinations with newer chemotherapeutic agents such as irinotecan and oxaliplatin have resulted in improved response rates and survival. The role of other agents including cyclooxygenase-2 inhibitors, epidermal growth factor receptor, and farnsyl transferase inhibitors remains to be elucidated. Despite these improvements, many patients undergo chemotherapy without benefit. Increased understanding of the biology of CRC has led to the identification of prognostic markers that may help identify patients who will benefit from chemotherapy. Furthermore, studies have also begun to identify markers that predict whether a tumor will respond to a particular chemotherapy. The ultimate goal of this research is to prospectively identify patients who should receive chemotherapy and, thus, to tailor treatment to the molecular profile of the tumor and patient. Such an approach has the potential to dramatically improve response rates. This review highlights potentially important prognostic and predictive factors in CRC and discusses the potential for their use in the treatment of this disease.

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Maintenance treatment of renal anaemia in haemodialysis patients with methoxy polyethylene glycol-epoetin beta versus darbepoetin alfa administered monthly:a randomized comparative trial

Several studies with erythropoiesis-stimulating agents claim that maintenance therapy of renal anaemia may be possible at extended dosing intervals; however, few studies were randomized, results varied, and comparisons between agents were absent. We report results of a multi-national, randomized, prospective trial comparing haemoglobin maintenance with methoxy polyethylene glycol-epoetin beta and darbepoetin alfa administered once monthly.

Efficiently exploring architectural design spaces via predictive modeling

Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude. Copyright © 2006 ACM.

Entinostat prevents leukemia maintenance in a collaborating oncogene-dependent model of CN-AML.

The incidence of refractory acute myeloid leukemia (AML) is on the increase due in part to an aging population that fails to respond to traditional therapies. High throughput genomic analysis promises better diagnosis, prognosis and therapeutic intervention based on improved patient stratification. Relevant pre-clinical models are urgently required to advance drug development in this area. The collaborating oncogenes, HOXA9 and MEIS1, are frequently co-overexpressed in cytogenetically normal AML (CN-AML) and a conditional transplantation mouse model was developed that demonstrated oncogene-dependency and expression levels comparable to CN-AML patients. Integration of gene signatures obtained from the mouse model and a cohort of CN-AML patients using statistically significant connectivity Map (sscMap) analysis identified Entinostat as a drug with the potential to alter the leukemic condition towards the normal state. Ex vivo treatment of leukemic cells, but not age-matched normal bone marrow controls, with Entinostat validated the gene signature and resulted in reduced viability in liquid culture, impaired colony formation and loss of the leukemia initiating cell. Furthermore, in vivo treatment with Entinostat resulted in prolonged survival of leukemic mice. This study demonstrates that the HDAC inhibitor Entinostat inhibits disease maintenance and prolongs survival in a clinically relevant murine model of cytogenetically normal AML. © 2013 AlphaMed Press

Efficient Architectural Design Space Exploration via Predictive Modeling

Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable.We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1–2% error. In an experimental study using the approach, training on 1% of a 250-K-point CMP design space allows our models to predict performance with only 4–5% error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude.

Late-stage, primary open-angle glaucoma in Europe:Social and health care maintenance costs and quality of life of patients from 4 countries

Objective: The purpose of this study was to estimate costs and quality of life (QoL) of late-stage glaucoma patients in 4 European countries. Methods: Retrospective review of medical charts of patients with POAG who were followed in a low-vision or vision rehabilitation center in one of 4 countries for at least 1 year was used to determine patient characteristics, health status, and health care resource use. Visual impairment was measured by best-corrected visual acuity (Snellen score). Patients were also interviewed over the telephone in order to assess their health-related QoL (using EuroQol EQ-5D) and use of resources including: the number of visits to rehabilitation centers, visits to hospital and non-hospital specialists, the use of low-vision devices, medication, tests, and the use of hired home help. The costs associated with resource use were calculated from the perspective of a third-party payer of health and social care based on resource usage and unit costs in each country. Results: Patients undergoing visual rehabilitation in France (n=21), Denmark (n=59), Germany (n=60), and the United Kingdom (n=22) were identified, interviewed and had their medical charts reviewed. Annual maintenance costs of late-stage glaucoma amounted to €830 (±445) on average. Average home help costs were more than 3 times higher. QoL, on average, was 0.65 (±0.28). QoL was positively correlated with the level of visual acuity in the patients' best eye. On the other hand, visual acuity was also positively correlated to health care costs, but negatively correlated to costs of home help. Conclusions: The study was limited by its observational, uncontrolled design. The finding that late-stage glaucoma is associated with higher home help costs than health care maintenance costs suggests that potential savings from a better preventive treatment are to be found for social care payers rather than health care payers. © 2008 Informa UK Ltd All rights reserved.

In silico prediction of the effects of mutations in the human UDP-galactose 4'-epimerase gene:Towards a predictive framework for type III galactosemia

The enzyme UDP-galactose 4'-epimerase (GALE) catalyses the reversible epimerisation of both UDP-galactose and UDP-N-acetyl-galactosamine. Deficiency of the human enzyme (hGALE) is associated with type III galactosemia. The majority of known mutations in hGALE are missense and private thus making clinical guidance difficult. In this study a bioinformatics approach was employed to analyse the structural effects due to each mutation using both the UDP-glucose and UDP-N-acetylglucosamine bound structures of the wild-type protein. Changes to the enzyme's overall stability, substrate/cofactor binding and propensity to aggregate were also predicted. These predictions were found to be in good agreement with previous in vitro and in vivo studies when data was available and allowed for the differentiation of those mutants that severely impair the enzyme's activity against UDP-galactose. Next this combination of techniques were applied to another twenty-six reported variants from the NCBI dbSNP database that have yet to be studied to predict their effects. This identified p.I14T, p.R184H and p.G302R as likely severely impairing mutations. Although severely impaired mutants were predicted to decrease the protein's stability, overall predicted stability changes only weakly correlated with residual activity against UDP-galactose. This suggests other protein functions such as changes in cofactor and substrate binding may also contribute to the mechanism of impairment. Finally this investigation shows that this combination of different in silico approaches is useful in predicting the effects of mutations and that it could be the basis of an initial prediction of likely clinical severity when new hGALE mutants are discovered.