Partitioned partial least squares regression with application to a batch fermentation process

The performance of Partial Least Squares regression (PLS) in predicting the output with multivariate cross‐ and autocorrelated data is studied. With many correlated predictors of varying importance PLS does not always predict well and we propose a modified algorithm, Partitioned Partial Least Squares (PPLS). In PPLS the predictors are partitioned into smaller subgroups and the important subgroups with high prediction power are identified. Finally, regular PLS analysis using only those subgroups is performed. The proposed Partitioned PLS (PPLS) algorithm is used in the analysis of data from a real pharmaceutical batch fermentation process for which the process variables follow certain profiles during a specific fermentation period. We observed that PPLS leads to a more accurate prediction of the yield of the fermentation process and an easier interpretation, since fewer predictors are used in the final PLS prediction. In the application important issues such as alignment of the profiles from one batch to another and standardization of the predictors are also addressed. For instance, in PPLS noise magnification due to standardization does not seem to create problems as it might in regular PLS. Finally, PPLS is compared to several recently proposed functional PLS and PCR methods and a genetic algorithm for variable selection. More specifically for a couple of publicly available data sets with near infrared spectra it is shown that overall PPLS has lower cross‐validated error than PLS, PCR and the functional modifications hereof, and is similar in performance to a more complex genetic algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

A Bayesian method for construction of Markov models to describe dynamics on various time-scales

The dynamics of many biological processes of interest, such as the folding of a protein, are slow and complicated enough that a single molecular dynamics simulation trajectory of the entire process is difficult to obtain in any reasonable amount of time. Moreover, one such simulation may not be sufficient to develop an understanding of the mechanism of the process, and multiple simulations may be necessary. One approach to circumvent this computational barrier is the use of Markov state models. These models are useful because they can be constructed using data from a large number of shorter simulations instead of a single long simulation. This paper presents a new Bayesian method for the construction of Markov models from simulation data. A Markov model is specified by (τ,P,T), where τ is the mesoscopic time step, P is a partition of configuration space into mesostates, and T is an N(P)×N(P) transition rate matrix for transitions between the mesostates in one mesoscopic time step, where N(P) is the number of mesostates in P. The method presented here is different from previous Bayesian methods in several ways. (1) The method uses Bayesian analysis to determine the partition as well as the transition probabilities. (2) The method allows the construction of a Markov model for any chosen mesoscopic time-scale τ. (3) It constructs Markov models for which the diagonal elements of T are all equal to or greater than 0.5. Such a model will be called a "consistent mesoscopic Markov model" (CMMM). Such models have important advantages for providing an understanding of the dynamics on a mesoscopic time-scale. The Bayesian method uses simulation data to find a posterior probability distribution for (P,T) for any chosen τ. This distribution can be regarded as the Bayesian probability that the kinetics observed in the atomistic simulation data on the mesoscopic time-scale τ was generated by the CMMM specified by (P,T). An optimization algorithm is used to find the most probable CMMM for the chosen mesoscopic time step. We applied this method of Markov model construction to several toy systems (random walks in one and two dimensions) as well as the dynamics of alanine dipeptide in water. The resulting Markov state models were indeed successful in capturing the dynamics of our test systems on a variety of mesoscopic time-scales.     

Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Network ensemble clustering using latent roles

We present a clustering method for collections of graphs based on the assumptions that graphs in the same cluster have a similar role structure and that the respective roles can be founded on implicit vertex types. Given a network ensemble (a collection of attributed graphs with some substantive commonality), we start by partitioning the set of all vertices based on attribute similarity. Projection of each graph onto the resulting vertex types yields feature vectors of equal dimensionality, irrespective of the original graph sizes. These feature vectors are then subjected to standard clustering methods. This approach is motivated by social network concepts, and we demonstrate its utility on an ensemble of personal networks of migrants, where we extract structurally similar groups and show their resemblance to predicted acculturation strategies.     

Controlled coupling of a single nitrogen-vacancy center to a silver nanowire

We report on the controlled coupling of a single nitrogen-vacancy (NV) center to a surface plasmon mode propagating along a chemically grown silver nanowire (NW). We locate and optically characterize a single NV center in a uniform dielectric environment before we controllably position this emitter in the close proximity of the NW. We are thus able to control the coupling of this particular emitter to the NW and directly compare the photon emission properties before and after the coupling. The excitation of single plasmonic modes is witnessed and a total rate enhancement by a factor of up to 4.6 is demonstrated.     

Entangling different degrees of freedom by quadrature squeezing cylindrically polarized modes

Quantum systems such as, for example, photons, atoms, or Bose-Einstein condensates, prepared in complex states where entanglement between distinct degrees of freedom is present, may display several intriguing features. In this Letter we introduce the concept of such complex quantum states for intense beams of light by exploiting the properties of cylindrically polarized modes. We show that already in a classical picture the spatial and polarization field variables of these modes cannot be factorized. Theoretically it is proven that by quadrature squeezing cylindrically polarized modes one generates entanglement between these two different degrees of freedom. Experimentally we demonstrate amplitude squeezing of an azimuthally polarized mode by exploiting the nonlinear Kerr effect in a specially tailored photonic crystal fiber. These results display that such novel continuous-variable entangled systems can, in principle, be realized.     

Counting atoms in a deep optical microtrap

We demonstrate a method to count small numbers of atoms held in a deep, microscopic optical dipole trap by collecting fluorescence from atoms exposed to a standing wave of light that is blue detuned from resonance. While scattering photons, the atoms are cooled by a Sisyphus mechanism that results from the spatial variation in light intensity. The use of a small blue detuning limits the losses due to light-assisted collisions, thereby making the method suitable for counting several atoms in a microscopic volume.     

Dielectric properties of thin-film ZrO<Subscript>2</Subscript> up to 50 GHz for RF MEMS switches

The dielectric properties of zirconium dioxide (ZrO₂) ceramic thin films were characterized up to 50 GHz using coplanar waveguides (CPWs) and metal–insulator–metal (MIM) capacitors with top circular electrodes. The ZrO₂ films were deposited using a chemical solution onto high-resistivity Si wafers and metal layers. The real part of the dielectric constant of approximately 22 and 26 was extracted at 50 GHz for CPW and MIM structures, respectively, and the loss tangent was approximately 0.09 at 50 GHz. C–V and I–V measurements were carried out to determine low-frequency and DC dielectric properties. The measurement results indicate that ZrO₂ is a promising material to be used as a dielectric layer for radio-frequency (RF) microelectromechanical systems (MEMS) capacitive switches.