Michael J. Piovoso

Silver Bullet or Voodoo Statistics? A Primer for Using the Partial Least Squares Data Analytic Technique in Group and Organization Research

Much of group and organization research is constrained by either limited sample sizes and/or nascent theoretical development. Wold developed the partial least squares (PLS) data analytical technique to help overcome these and other challenges facing researchers. PLS represents a powerful and effective means to test multivariate structural models with latent variables. Although PLS is used by researchers and practitioners in many scientific disciplines, some misunderstanding remains among group and organization researchers regarding the legitimacy and usefulness of PLS. To help allay these concerns, this article provides a nontechnical primer on PLS and its advantages, limitations, and application to group and organization research using a data set collected in an experiment on the effects of leadership styles and communication format on the group potency of computer-mediated work groups.

Long-Term Antiretroviral Treatment Initiated at Primary HIV-1 Infection Affects the Size, Composition, and Decay Kinetics of the Reservoir of HIV-1-Infected CD4 T Cells

ABSTRACT Initiation of antiretroviral therapy during the earliest stages of HIV-1 infection may limit the seeding of a long-lasting viral reservoir, but long-term effects of early antiretroviral treatment initiation remain unknown. Here, we analyzed immunological and virological characteristics of nine patients who started antiretroviral therapy at primary HIV-1 infection and remained on suppressive treatment for >10 years; patients with similar treatment duration but initiation of suppressive therapy during chronic HIV-1 infection served as controls. We observed that independently of the timing of treatment initiation, HIV-1 DNA in CD4 T cells decayed primarily during the initial 3 to 4 years of treatment. However, in patients who started antiretroviral therapy in early infection, this decay occurred faster and was more pronounced, leading to substantially lower levels of cell-associated HIV-1 DNA after long-term treatment. Despite this smaller size, the viral CD4 T cell reservoir in persons with early treatment initiation consisted more dominantly of the long-lasting central-memory and T memory stem cells. HIV-1-specific T cell responses remained continuously detectable during antiretroviral therapy, independently of the timing of treatment initiation. Together, these data suggest that early HIV-1 treatment initiation, even when continued for >10 years, is unlikely to lead to viral eradication, but the presence of low viral reservoirs and durable HIV-1 T cell responses may make such patients good candidates for future interventional studies aiming at HIV-1 eradication and cure. IMPORTANCE Antiretroviral therapy can effectively suppress HIV-1 replication to undetectable levels; however, HIV-1 can persist despite treatment, and viral replication rapidly rebounds when treatment is discontinued. This is mainly due to the presence of latently infected CD4 T cells, which are not susceptible to antiretroviral drugs. Starting treatment in the earliest stages of HIV-1 infection can limit the number of these latently infected cells, raising the possibility that these viral reservoirs are naturally eliminated if suppressive antiretroviral treatment is continued for extremely long periods of time. Here, we analyzed nine patients who started on antiretroviral therapy within the earliest weeks of the disease and continued treatment for more than 10 years. Our data show that early treatment accelerated the decay of infected CD4 T cells and led to very low residual levels of detectable HIV-1 after long-term therapy, levels that were otherwise detectable in patients who are able to maintain a spontaneous, drug-free control of HIV-1 replication. Thus, long-term antiretroviral treatment started during early infection cannot eliminate HIV-1, but the reduced reservoirs of HIV-1 infected cells in such patients may increase their chances to respond to clinical interventions aiming at inducing a drug-free remission of HIV-1 infection.

PCA of Wavelet Transformed Process Data for Monitoring

Producing a uniform product is important for several reasons such as maintenance of a competitive position, reduction in the number of shutdowns and startups, and the elimination of the sources of variability. Multivariate statistical methods can assist in the identification of process correlations and the development of process monitoring models. This work extends these concepts by demonstrating that the correlations and resulting monitoring models can be improved greatly with the addition of pre-filtering the time signals using a median filter, and time-scale decomposition using a multi-resolution wavelet function. After the data are filtered and decomposed, the multivariate statistical method of principal component analysis PCA is used to develop a process monitoring model. Data that was taken from a difficult-to-operate industrial process are used to demonstrate these ideas.

HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.

Kalman filter recipes for real-time image processing

Kalman filters are an important technique for building fault-tolerance into a wide range of systems, including real-time imaging. From a software engineering perspective, however, it is not easy to build Kalman filters, Each has to be custom designed and most software engineers are not sufficiently grounded in the necessary systems theory to perform this design.The contributions of this paper, therefore, are a set of recipes for implementation of the Kalman filter to a variety of real-time imaging settings, the presentation of a set of object-oriented requirements, and a design for a class of Kalman filters suitable for real-time image processing.First, we describe the Kalman filter and motivate its use as a mechanism for fault-tolerant computing and sensor fusion. Next, the details of using Kalman filters in imaging applications are discussed and several associated algorithms presented. Then, the advantages of using object-oriented specification, design and languages for the implementation of Kalman filters are explored. Finally, we present a specification and design for a class of Kalman filters, which is suitable for coding. This work extends significantly upon that first appearing in 2003 at an SPIE conference (Laplante and Neill, proceedings of the real-time imaging conference, SPIE, Santa Clara, January 2003, pp. 22-29).

Modelling HIV-1 2-LTR dynamics following raltegravir intensification

A model of reservoir activation and viral replication is introduced accounting for the production of 2-LTR HIV-1 DNA circles following antiviral intensification with the HIV integrase inhibitor raltegravir, considering contributions of de novo infection events and exogenous sources of infected cells, including quiescent infected cell activation. The model shows that a monotonic increase in measured 2-LTR concentration post intensification is consistent with limited de novo infection primarily maintained by sources of infected cells unaffected by raltegravir, such as quiescent cell activation, while a transient increase in measured 2-LTR concentration is consistent with significant levels of efficient (R0 > 1) de novo infection. The model is validated against patient data from the INTEGRAL study and is shown to have a statistically significant fit relative to the null hypothesis of random measurement variation about a mean. We obtain estimates and confidence intervals for the model parameters, including 2-LTR half-life. Seven of the 13 patients with detectable 2-LTR concentrations from the INTEGRAL study have measured 2-LTR dynamics consistent with significant levels of efficient replication of the virus prior to treatment intensification.

Award-winning control applications

Virtually all dynamical systems, whether mechanical, electrical, chemical, economic, or social, can be improved with control technology. Most practical implementations, however, still require challenging innovations by designers to fully realize the potential of our diverse methodologies. This article reviews three recent examples of such innovations.

Application of a neural network to improve an automated thermoplastic tow-placement process

Abstract This study demonstrates the use of an on-line neural network to calculate process set points for PID controllers in a manufacturing process such as the automated thermoplastic tow-placement (ATP) technique. The set points are computed by the neural network so that the throughput is near maximum and a desired minimum quality is maintained. A novel neural network predictive scheme is developed to enable performance over a wide range of processing inputs. Process history can greatly affect the final part quality and, therefore, is an integral part of the method for determining the set points. The system is first trained and tested in simulation and then validated for the highly non-linear ATP process resulting in significantly improved process operation. The developed approach is applicable to many other manufacturing processes where process simulations exist and conventional control techniques are lacking.

Improvements in the predictive capability of neural networks

Abstract Neural networks can be used to develop effective models of nonlinear systems. Their main advantage being that they can model the vast majority of nonlinear systems to any arbitrary degree of accuracy. The ability of a neural network to predict the behavior of a nonlinear system accurately ought to be improved if there was some mechanism that allows the incorporation of first-principles model information into their training. This study proposes to use information obtained from a first-principle model to impart a sense of “direction” to the neural network model estimate. This is accomplished by modifying the objective function so as to include an additional term that is the difference between the time derivative of the outputs, as predicted by the neural network, and that of the outputs of the first-principles model during the training phase. The performance of a feedforward neural network model that uses this modified objective function is demonstrated on a chaotic process and compared to the conventional feedforward network trained on the usual objective function.

Multivariate statistics for process control

Our experience with the application of multivariate statistics to process control began in the late 1980s. Bruce Kowalski, founder of the Center for Process Analytic Chemistry (CPAC), had coined the term chemometrics to describe the application of mathematics and statistics to chemical processes. At that time, the authors were employed by the DuPont C hemical C o., which was a member of CPAC. Bruce educated the members of CPAC about principal component analysis (PCA) and projections to latent structures, also known as partial least squares (PLS).