Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network

In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.

Recurrent input transformations for Hidden Markov models

This paper presents a new architecture which integrates recurrent input transformations (RIT) and continuous density HMMs. The basic HMM structure is extended to accommodate recurrent neural networks which transform the input observations before they enter the Gaussian output distributions associated with the states of the HMM. During training the parameters of both HMM and RIT are simultaneously optimized according to the Maximum Mutual Information (MMI) criterion. Results are presented for the E-set recognition task which demonstrate the ability of recurrent input transformations to exploit longer term correlations in the speech signal and to give improved discrimination.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented.

Optimal assessment of the ability of children with recurrent respiratory tract infections to produce anti-polysaccharide antibodies.

Specific anti-polysaccharide antibody deficiency (SPAD) is an immune disorder. Diagnostic criteria have not yet been defined clearly. One hundred and seventy-six children evaluated for recurrent respiratory tract infections were analysed retrospectively. For each subject, specific anti-pneumococcal antibodies had been measured with two enzyme-linked immunosorbent assays (ELISAs), one overall assay (OA) using the 23-valent pneumococcal polysaccharide vaccine (23-PPSV) as detecting antigen and the other purified pneumococcal polysaccharide serotypes (serotype-specific assay, SSA) (serotypes 14, 19F and 23F). Antibody levels were measured before (n = 176) and after (n = 93) immunization with the 23-PPSV. Before immunization, low titres were found for 138 of 176 patients (78%) with OA, compared to 20 of 176 patients (11%) with the SSA. We found a significant correlation between OA and SSA results. After immunization, 88% (71 of 81) of the patients considered as responders in the OA test were also responders in the SSA; 93% (71 of 76) of the patients classified as responders according to the SSA were also responders in the OA. SPAD was diagnosed in 8% (seven of 93) of patients on the basis of the absence of response in both tests. Thus, we propose to use OA as a screening test for SPAD before 23-PPSV immunization. After immunization, SSA should be used only in case of a low response in OA. Only the absence of or a very low antibody response detected by both tests should be used as a diagnostic criterion for SPAD.

Genetic deficiency of C4 presenting with recurrent infections and a SLE-like disease. Genetic and immunologic studies

info:eu-repo/semantics/published

The reappearance hypothesis revisited: recurrent involuntary memories after traumatic events and in everyday life.

Recurrent involuntary memories are autobiographical memories that come to mind with no preceding retrieval attempt and that are subjectively experienced as being repetitive. Clinically, they are classified as a symptom of posttraumatic stress disorder. The present work is the first to systematically examine recurrent involuntary memories outside clinical settings. Study 1 examines recurrent involuntary memories among survivors of the tsunami catastrophe in Southeast Asia in 2004. Study 2 examines recurrent involuntary memories in a large general population. Study 3 examines whether the contents of recurrent involuntary memories recorded in a diary study are duplicates of, or differ from, one another. We show that recurrent involuntary memories are not limited to clinical populations or to emotionally negative experiences; that they typically do not come to mind in a fixed and unchangeable form; and that they show the same pattern regarding accessibility as do autobiographical memories in general. We argue that recurrent involuntary memories after traumas and in everyday life can be explained in terms of general and well-established mechanisms of autobiographical memory.

A phase 1-2 clinical trial of gene therapy for recurrent glioblastoma multiforme by tumor transduction with the herpes simplex thymidine kinase gene followed by ganciclovir.

This study has investigated the effects of herpes simplex thymidine kinase gene (HSV-tk) transfer followed by ganciclovir treatment as adjuvant gene therapy to surgical resection in patients with recurrent glioblastoma multiforme (GBM). The study was open and single-arm, and aimed at assessing the feasibility and safety of the technique and indications of antitumor activity. In 48 patients a suspension of retroviral vector-producing cells (VPCs) was administered by intracerebral injection immediately after tumor resection. Intravenous ganciclovir was infused daily 14 to 27 days after surgery. Patients were monitored for adverse events and for life by regular biosafety assaying. Tumor changes were monitored by magnetic resonance imaging (MRI). Reflux during injection was a frequent occurrence but serious adverse events during the treatment period (days 1-27) were few and of a nature not unexpected in this population. One patient experienced transient neurological disorders associated with postganciclovir MRI enhancement. There was no evidence of replication-competent retrovirus in peripheral blood leukocytes or in tissue samples of reresection or autopsy. Vector DNA was shown in the leukocytes of some patients but not in autopsy gonadal samples. The median survival time was 8.6 months, and the 12-month survival rate was 13 of 48 (27%). On MRI studies, tumor recurrence was absent in seven patients for at least 6 months and for at least 12 months in two patients, one of whom remains recurrence free at more than 24 months. Treatment-characteristic images of injection tracks and intracavity hemoglobin were apparent. In conclusion, the gene therapy is feasible and appears to be satisfactorily safe as an adjuvant to the surgical resection of recurrent GBM, but any benefit appears to be marginal. Investigation of the precise effectiveness of this gene therapy requires prospective, controlled studies.

Proteomic Analysis of Recurrent Joint Inflammation in Juvenile Idiopathic Arthritis

The synovial fluid proteome in juvenile idiopathic arthritis was investigated to isolate joint-specific biomarkers that are expressed in patients displaying recurrent joint inflammation. To identify the synovial specific proteome, matched synovial fluid and plasma samples were subjected to protein separation by 2-dimension electrophoresis (2DE). Forty-three protein spots, overexpressed in the joint, were identified. Synovial fluids from children with single-event knee joint inflammation were then compared with a group with recurrent knee disease. Nine synovial specific proteins were significantly differentially expressed in the recurrent group. Proteolytic fragments of collagen X, fibrin beta-chain, and T-cell receptor alpha-region have been identified among this protein cluster. Putative biomarkers, overexpressed in the joint and differentially expressed in children with recurrent joint inflammation, have been identified. These proteins may play a significant role determining the pathological state within the chronically inflamed joint and influence disease progression in JIA. This is the first study of the synovial proteome in children.