Cellulose-binding domains: potential for purification of complex proteins

The endoglucanase CenA and the exoglucanase Cex from Cellulomonasfimi each contain a discrete cellulose-binding domain (CBD),at the amino-terminus or carboxyl-terminus respectively. Thegene fragment encoding the CBD can be fused to the gene of aprotein of interest. Using this approach hybrid proteins canbe engineered which bind reversibly to cellulose and exhibitthe biological activity of the protein partner. Alkaline phosphatase(PhoA) from Escherichia coli, and a ß-glucosidase(Abg) from an Agrobacterium sp. are dimeric proteins. The fusionpolypeptides CenA-PhoA and Abg-CBC_cex are sensitive to proteolysisat the junctions between the fusion partners. Proteolysis resultsin a mixture of homo- and heterodimers; these bind to celluloseif one or both of the monomers carry a CBD, e.g. CenA-PhoA/CenA-PhoAand CenA-PhoA/PhoA. CBD fusion polypeptides could be used inthis way to purify polypeptides which associate with the fusionpartner.     

Fourier transform infrared (FTIR) authentication and batch-to-batch consistency for different types of paints using benchtop and handheld FTIR spectrophotometers for oil and gas industry

Standardization of Fourier transform infrared (FTIR) fingerprint region for paints and assessment on the reproducibility using different spectrophotometers were investigated. While selective fingerprint regions may be confusing for technicians/analysts who are non-chemists, we attempt to generalize these regions (e.g., 1300–1000 cm⁻¹ for Epoxy part A and 1400–1000 cm⁻¹ for Epoxy part B) by choosing a universal region (2000–900 cm⁻¹) that works for different paints. Comparison result using a paired student t-test shows that the degree of similarity (r) values from the studied regions are not statistically different. The paint fails the screening analysis occasionally on-site when analyzed using handheld FTIR due to the higher level of noise that gives low r values (r < 0.900 ± 0.002). The same samples were analyzed using a benchtop FTIR and the r values are above 0.900 ± 0.002. While the screening may lead to a false rejection of the sample on-site, there could be occurrence of false acceptance. The on-site screening of EPZ part A with different formulations, for instance, shows that the r values over the entire IR spectrum are above 0.900 ± 0.002 when analyzed using handheld FTIR. After the samples were analyzed using the benchtop, the r values fall below 0.900 ± 0.002.     

Analytic center cutting plane method for multiple kernel learning

Multiple Kernel Learning (MKL) is a popular generalization of kernel methods which allows the practitioner to optimize over convex combinations of kernels. We observe that many recent MKL solutions can be cast in the framework of oracle based optimization, and show that they vary in terms of query point generation. The popularity of such methods is because the oracle can fortuitously be implemented as a support vector machine. Motivated by the success of centering approaches in interior point methods, we propose a new approach to optimize the MKL objective based on the analytic center cutting plane method (accpm). Our experimental results show that accpm outperforms state of the art in terms of rate of convergence and robustness. Further analysis sheds some light as to why MKL may not always improve classification accuracy over naive solutions.     

Feature selection via sensitivity analysis of SVM probabilistic outputs

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications. Editor: Risto Miikkulainen.     

Experimental dynamic characterizations and modelling of disk vibrations for HDDs

Currently, the rotational speed of spindle motors in HDDs (Hard-Disk Drives) are increasing to improve high data throughput and decrease rotational latency for ultra-high data transfer rates. However, the disk platters are excited to vibrate at their natural frequencies due to higher air-flow excitation as well as eccentricities and imbalances in the disk-spindle assembly. These factors contribute directly to TMR (Track Mis-Registration) which limits achievable high recording density essential for future mobile HDDs. In this paper, the natural mode shapes of an annular disk mounted on a spindle motor used in current HDDs are characterized using FEM (Finite Element Methods) analysis and verified with SLDV (Scanning Laser Doppler Vibrometer) measurements. The identified vibration frequencies and amplitudes of the disk ODS (Operating Deflection Shapes) at corresponding disk mode shapes are modelled as repeatable disturbance components for servo compensation in HDDs. Our experimental results show that the SLDV measurements are accurate in capturing static disk mode shapes without the need for intricate air-flow aero-elastic models, and the proposed disk ODS vibration model correlates well with experimental measurements from a LDV.     

Prefrontal cortical projections to the hypothalamus in macaque monkeys

The organization of projections from the macaque orbital and medial prefrontal cortex (OMPFC) to the hypothalamus and related regions of the diencephalon and midbrain was studied with retrograde and anterograde tracing techniques. Almost all of the prefrontal cortical projections to the hypothalamus arise from areas within the "medial prefrontal network," as defined previously by Carmichael and Price ([1996] J. Comp. Neurol. 371:179-207). Outside of the OMPFC, only a few neurons in the temporal pole, anterior cingulate and insular cortex project to the hypothalamus. Axons from the OMPFC also innervate the basal forebrain, zona incerta, and ventral midbrain. Within the medial prefrontal network, different regions project to distinct parts of the hypothalamus. The medial wall areas 25 and 32 send the heaviest projections to the hypothalamus; axons from these areas are especially concentrated in the anterior hypothalamic area and the ventromedial hypothalamic nucleus. Orbital areas 13a, 12o, and Iai, which are related to the medial prefrontal network, selectively innervate the lateral hypothalamic area, especially its posterior part. The cellular regions of the paraventricular, supraoptic, suprachiasmatic, arcuate, and mammillary nuclei are conspicuously devoid of cortical axons, but many axons abut the borders of these nuclei and may contact dendrites that extend from them. Areas within the orbital prefrontal network on the posterior orbital surface and agranular insula send only weak projections to the posterior lateral hypothalamic area. The rostral orbital surface does not contribute to the cortico-hypothalamic projection.     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

A novel AR-based robot programming and path planning methodology

This paper discusses the benefits of applying Augmented Reality (AR) to facilitate intuitive robot programming, and presents a novel methodology for planning collision-free paths for an n degree-of-freedom (DOF) manipulator in an unknown environment. The targeted applications are where the end-effector is constrained to move along a visible 3D path/curve, which position is unknown, at a particular orientation with respect to the path, such as arc welding and laser cutting. The methodology is interactive as the human is involved in obtaining the 3D data points of the desired curve to be followed through performing a number of demonstrations, defining the free space relevant to the task, and planning the orientations of the end-effector along the curve. A Piecewise Linear Parameterization (PLP) algorithm is used to parameterize the data points using an interactively generated piecewise linear approximation of the desired curve. A curve learning method based on Bayesian neural networks and reparameterization is used to learn and generate 3D parametric curves from the parameterized data points. Finally, the orientation of the end-effector along the learnt curve is planned with the aid of AR. Two case studies are presented and discussed.     

Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection

We present a novel method for detecting malaria parasites and determining the stage of infection from digital images comprising red blood cells (RBCs). The proposed method is robust under varying conditions of image luminance, contrast and clumping of RBCs. Both strong and weak boundary edges of the RBCs and parasites are detected based on the similarity measure between local image neighborhoods and predefined edge filters. A rule-based algorithm is applied to link edge fragments to form closed contours of the RBCs and parasite regions, as well as to split clumps into constituent cells. A radial basis support vector machine determines the stage of infection from features extracted from each parasite region. The proposed method achieves 97% accuracy in cell segmentation and 86% accuracy in parasite detection when tested on a total of 530 digitally captured images of three species of malaria parasites: Plasmodium falciparum, Plasmodium yoelii and Plasmodium berghei.