Miniaturized and High-Throughput Assays for Analysis of T-Cell Immunity Specific for Opportunistic Pathogens and HIV

Monitoring of antigen-specific T-cell responses is valuable in numerous conditions that include infectious diseases, vaccinations, and opportunistic infections associated with acquired or congenital immune defects. A variety of assays that make use of peripheral lymphocytes to test activation markers, T-cell receptor expression, or functional responses are currently available. The last group of assays calls for large numbers of functional lymphocytes. The number of cells increases with the number of antigens to be tested. Consequently, cells may be the limiting factor, particularly in lymphopenic subjects and in children, the groups that more often require immune monitoring. We have developed immunochemical assays that measure secreted cytokines in the same wells in which peripheral blood mononuclear cells (PBMC) are cultured. This procedure lent itself to miniaturization and automation. Lymphoproliferation and the enzyme-linked immunosorbent spot (ELISPOT) assay have been adapted to a miniaturized format. Here we provide examples of immune profiles and describe a comparison between miniaturized assays based on cytokine secretion or proliferation. We also demonstrate that these assays are convenient for use in testing antigen specificity in established T-cell lines, in addition to analysis of PBMC. In summary, the applicabilities of miniaturization to save cells and reagents and of automation to save time and increase accuracy were demonstrated in this study using different methodological approaches valuable in the clinical immunology laboratory

The dynamics of motor learning through the formation of internal models

A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user\u2019s actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes

Upper Body-Based Power Wheelchair Control Interface for Individuals with Tetraplegia

Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control

Caratterizzazione dell’ambiente marino dei Campi Flegrei. Risultati preliminari della campagna oceanografica RICAMAR 2013

The caldera of the Phlegraean Fields (also known as Campi Flegrei) is one of the most dangerous and populated volcanic area in the world, covering an area that comprises the western part of Naples and the Gulf of Pozzuoli. The main peculiarity of current volcanic activity is the gradual and periodic lift (positive or negative) of part of the Earth\u27s surface (bradyseism) combined, only during the positive phase, with a strong sismicity and surficial hydrotermal activity. Deformative models, calibrated using land-based measurements, highlighted the Gulf of Pozzuoli as the area with the largest deformation. Although the network of monitoring sensors on land is well developed and structured, there is a lack of sensing systems for the marine deformation. The activities of RIlievi per la Caratterizzazione dell’Ambiente MARino nel Golfo di Pozzuoli 2013 (RICAMAR2013) project - sinergically conducted by the Italian Navy\u27s Survey Vessel Ammiraglio Magnaghi , the Italian Hydrographic Office (IIM) and the Istituto Nazionale di Geofisica e Vulcanologia (INGV)- were targeted to fulfill this gap. In fact, the creation of marine observatories about the caldera\u27s phenomena will be based on the data collected during these bathymetric, magnetometric, stratigrafic and hydrologic surveys

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)