Psychological stress evaluators: EMG correlation with voice tremor

Temporal measurements of frequency changes in the human voice and tremor in the muscles of the vocal area suggest that these phenomena are correlated. A measure of muscular activity was obtained using a non linear filter on the EMG wave. Frequency changes were detected in the third formant of the voice spectrum. The crosscorrelation results indicate that the voice vibrations are forced oscillations generated by central nervous activity, explaining the detection of stress from voice records.     

Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task

 Movement-related potentials (MRPs) recorded from the brain are thought to vary during learning of a motor task. However, since MRPs are recorded at a very low signal-to-noise ratio, it is difficult to measure these variations. In this study we attempt to remove most of the accompanying noise thus enabling the tracking of transient phenomena in MRPs recorded during learning of a motor task. Subjects performed a simple motor task which required learning. A modified version of the matching pursuit algorithm was used in order to remove a significant portion of the electroencephalographic noise overlapping the MRPs recorded in the experiment. Small groups of MRPs were then averaged according to experimental parameters. Our results show that the power of the MRPs does not decay uniformly during learning. Instead, there is a significant peak in their power after 4 or 5 repetitions of the task. This peak is noticeable especially in electrodes placed over the prefrontal region of the cortex at times subsequent to the actual movement. The observed pattern of activity may indicate problem solving related to comprehension of the force against which the user performed the task. It is possible that this problem solving occurs in the prefrontal cortex. Received: 27 December 2000 / Accepted in revised form: 26 April 2001     

A model for learning human reaching movements

 Reaching movement is a fast movement towards a given target. The main characteristics of such a movement are straight path and a bell-shaped speed profile. In this work a mathematical model for the control of the human arm during ballistic reaching movements is presented. The model of the arm contains a 2 degrees of freedom planar manipulator, and a Hill-type, non-linear mechanical model of six muscles. The arm model is taken from the literature with minor changes. The nervous system is modeled as an adjustable pattern generator that creates the control signals to the muscles. The control signals in this model are rectangular pulses activated at various amplitudes and timings, that are determined according to the given target. These amplitudes and timings are the parameters that should be related to each target and initial conditions in the workspace. The model of the nervous system consists of an artificial neural net that maps any given target to the parameter space of the pattern generator. In order to train this net, the nervous system model includes a sensitivity model that transforms the error from the arm end-point coordinates to the parameter coordinates. The error is assessed only at the termination of the movement from knowledge of the results. The role of the non-linearity in the muscle model and the performance of the learning scheme are analysed, illustrated in simulations and discussed. The results of the present study demonstrate the central nervous system’s (CNS) ability to generate typical reaching movements with a simple feedforward controller that controls only the timing and amplitude of rectangular excitation pulses to the muscles and adjusts these parameters based on knowledge of the results. In this scheme, which is based on the adjustment of only a few parameters instead of the whole trajectory, the dimension of the control problem is reduced significantly. It is shown that the non-linear properties of the muscles are essential to achieve this simple control. This conclusion agrees with the general concept that motor control is the result of an interaction between the nervous system and the musculoskeletal dynamics. Received : 21 May 1996 / Accepted in revised form : 10 June 1997     

Movement-related potentials during the performance of a motor task I: The effect of learning and force

 Movement-related potentials (MRPs) recorded from the brain may be affected by several factors. These include the how well the subject knows the task and the load against which he performs it. The objective of this study is to determine how dominant these two factors are in influencing the shape and power of MRPs. MRPs were recorded during performance of a simple motor task that required learning of a force. A stochastic algorithm was used in order to partition a set of MRPs that are embedded in the surrounding electroencephalographic (EEG) activity into distinct classes according to the power of the underlying MRPs. Our results show that the most influential factor in the partition was the load against which the subject performed the task. Furthermore, it was found that learning has a smaller, though not insignificant, influence on the power of the MRPs. Received: 27 December 2000 / Accepted in revised form: 26 April 2001     

Changes in the structural organization of the surface membrane in malignant cell transformation

Summary Malignant transformation of normal cells resulting in agglutinability by the carbohydrate-binding protein Concanavalin A can be explained by three types of changes in the structural organization of sites on the surface membrane. There can be an exposure of cryptic sites, a concentration of exposed sites by a decrease in cell size, and a rearrangement of exposed sites without a decrease in cell size resulting in a clustering of sites.     

Quantitative phosphoproteomic analysis reveals involvement of PD-1 in multiple T cell functions

Programmed cell death protein 1 (PD-1) is a critical inhibitory receptor that limits excessive T cell responses. Cancer cells have evolved to evade these immunoregulatory mechanisms by upregulating PD-1 ligands and preventing T cell–mediated anti-tumor responses. Consequently, therapeutic blockade of PD-1 enhances T cell–mediated anti-tumor immunity, but many patients do not respond and a significant proportion develop inflammatory toxicities. To improve anti-cancer therapy, it is critical to reveal the mechanisms by which PD-1 regulates T cell responses. We performed global quantitative phosphoproteomic interrogation of PD-1 signaling in T cells. By complementing our analysis with functional validation assays, we show that PD-1 targets tyrosine phosphosites that mediate proximal T cell receptor signaling, cytoskeletal organization, and immune synapse formation. PD-1 ligation also led to differential phosphorylation of serine and threonine sites within proteins regulating T cell activation, gene expression, and protein translation. In silico predictions revealed that kinase/substrate relationships engaged downstream of PD-1 ligation. These insights uncover the phosphoproteomic landscape of PD-1–triggered pathways and reveal novel PD-1 substrates that modulate diverse T cell functions and may serve as future therapeutic targets. These data are a useful resource in the design of future PD-1–targeting therapeutic approaches.     

Induction of cadmium tolerance in Escherichia coli K-12

Abstract Cadmium ions are bacteriocidal, resulting in exponential killing that starts immediately after exposure. We have shown that pretreatment with sublethal concentrations of cadmium induces tolerance. Protection against cadmium killing can also be obtained by preincubation at elevated temperatures, known to induce the heat-shock response. However, in contrast to pretreatment at elevated temperatures, exposure to sublethal cadmium concentrations does not induce thermotolerance.