Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area

Geometric Aspects of Composite Pulses

Unitary operations acting on a quantum system must be robust against systematic errors in control parameters for reliable quantum computing. Composite pulse technique in nuclear magnetic resonance (NMR) realises such a robust operation by employing a sequence of possibly poor quality pulses. In this article, we demonstrate that two kinds of composite pulses, one compensates for a pulse length error in a one-qubit system and the other compensates for a J-coupling error in a twoqubit system, have vanishing dynamical phase and thereby can be seen as geometric quantum gates, which implement unitary gates by the holonomy associated with dynamics of cyclic vectors defined in the text.Comment: 20 pages, 4 figures. Accepted for publication in Philosophical Transactions of the Royal Society

Designing Robust Unitary Gates: Application to Concatenated Composite Pulse

We propose a simple formalism to design unitary gates robust against given systematic errors. This formalism generalizes our previous observation [Y. Kondo and M. Bando, J. Phys. Soc. Jpn. 80, 054002 (2011)] that vanishing dynamical phase in some composite gates is essential to suppress amplitude errors. By employing our formalism, we naturally derive a new composite unitary gate which can be seen as a concatenation of two known composite unitary operations. The obtained unitary gate has high fidelity over a wider range of the error strengths compared to existing composite gates.Comment: 7 pages, 4 figures. Major revision: improved presentation in Sec. 3, references and appendix adde

A preliminary census of the macrofungi of Mt Wellington, Tasmania- the sequestrate species

This is the fourth and final contribution in a series of papers providing a preliminary documentation of the macrofungi of Mt Wellington, Tasmania. The earlier papers dealt with the gilled Basidiomycota, the non-gilled Basidiomycota and the Ascomycota, respectively, excluding the sequestrate species. The present paper completes the series by dealing with the sequestrate species, of which seven Ascomycota, 76 Basidiomycota, three Glomeromycota and one Zygomycota were found. Seven new genera and 25 new species to be formally described elsewhere, are recorded

Potential Operation and Maintenance (O&M) Savings in the John Sealy North Building at UTMB

The LoanSTAR Monitoring and Analysis Group, Energy Systems Laboratory at Texas A&M University, was requested by University of Texas Medical Branch at Galveston to investigate O&M measures in their five LoanSTAR program buildings. This report describes the suggested O&Ms in John Sealy North Building, a surgical building of 54,494 ft2,which currently spends 502,100 per year on electricity, steam and chilled water. The suggested O&Ms include optimizing the outside air treatment cold deck reset schedule, the cold deck reset schedule and the hot deck reset schedule. These optimized HVAC operation schedules were determined using an analysis involving a simplified HVAC model, which was calibrated against daily data measured by the LoanSTAR program. It is estimated that annual savings of 67,000, or 13% of the annual costs, can be realized using the optimized operation schedules which can be implemented without additional costs. Our analysis indicates that the room comfort levels will not be degraded by these measures

Isolation, identification and anti-cancer activity of minor alkaloids from Triclisia subcordata

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Statistical Modeling of Daily Energy Consumption in Commercial Buildings Using Multiple Regression and Principal Component Analysis

Statistical models of energy use in commercial buildings are being increasingly used not only for predicting retrofit savings but also for identifying improper operation of HVAC systems. The conventional approach involves using multiple regression analysis to identify these models. However, such models tend to suffer from physically unreasonable regression coefficients and instability due to the fact that the predictor variables (i.e., climatic parameters, building internal loads, etc.) are intercorrelated. A relatively new approach proposed to circumvent these drawbacks is principal component analysis. The objective of this paper is to evaluate the multivariate regression and the principal component analysis approaches, using measured whole-building energy use data from a large commercial building in central Texas. For the types of correlation strengths among the regressor variables present in our data, we find that there does not seem to be much justification in selecting the principal component analysis approach. A more careful and elaborate investigation using data sets which exhibit a wide range of multicollinearity strengths is required in order to ascertain when principal component analysis yields predictive models superior to those of a multiple regression approach

Bias in Predicting Annual Energy Use in Commercial Buildings with Regression Models Developed from Short Data Sets

Issues relating to bias in regression models identified from short data sets are discussed in this paper. First, the physical reasons for the differences between the predictions of the annual energy consumption based on a short data set model and on a long data set model are discussed. Then, the errors associated with the multiple linear regression model are evaluated when applied to short data sets of monitored data from large commercial buildings in Texas. The analysis shows that the seasonal variation of the outdoor dry-bulb and dew-point temperature causes significant errors in the models developed from short data sets. The MBE (mean bias error) from models based on short data sets (one month) varied from +40% to -15%, which is significant. Hence, due care must be exercised when applying the regression modeling approach in such cases.An empirical or regression modeling approach is simple to develop and easy to use compared to use of detailed hourly simulations. Therefore, regression analysis has become a widely used tool in the determination of annual energy savings accruing from energy conserving retrofits. The regression modeling approach is accurate and reliable if several months of data (more than six months) are used to develop the model. If such is not the case, the regression models can, unfortunately, lead to significant errors in the prediction of the annual energy consumption

Cooling-Only Prism Analysis of Eleven College Station Homes and Interpretation of Building Physical Parameters

A cooling-only PRISM analysis has been performed on eleven new residences in College Station using electricity billing data over an entire year. This study revealed that, provided one corrects for effects such as vacation periods, erroneous utility meter readings and abnormal occupancy patterns during holiday periods, the PRISM approach can accurately model whole-building electricity use (R^2 in the range of 0.92 to 0.99). The physical interpretation of the building parameters determined by PRISM has also been evaluated against continuous measurements of indoor temperature and air-conditioned electricity consumption made during the summer as part of another study. We find that the PRISM estimates for balance point temperature are within a few degrees of actually "measured" values and seem to be unbiased. The PRISM estimates for base-load consumption. on the other hand, are consistently higher by 50% to 100% of the measured base-loads, and factors which may contribute to this bias have also been briefly discussed

Using Fourier Series to Model Hourly Energy Use in Commercial Buildings

The procedure for modeling hourly energy use has two steps. Mean daily energy use is different during working weekdays, working weekends, holidays and Christmas due to major change in mode of operation or scheduling. The first step is to do Day-typing to remove such effects. The second step is to develop Fourier series model using the forward selection procedure of Statistical Analysis System (SAS) program. While the model for weather independent energy use is developed from a Fourier series with the hour of the day as the independent variable, the model for weather dependent energy use is developed from a set of variables that has both Fourier frequencies and products of Fourier frequencies and weather variables, e.g., ambient temperature, out door specific humidity and horizontal solar flux.Fourier series analysis is eminently suitable for modeling strongly periodic data. Weather independent energy use such as lighting and equipment load in commercial buildings is strongly periodic and is thus appropriate for Fourier series treatment. Weather dependent energy use such as cooling energy use and heating energy use is dependent both on internal load and weather variables. Both the driving forces have strong periodicity and consequently, weather dependent energy use is equally suitable for Fourier series analysis