Timetable of Gait Cycle Events in Parkinson's Disease.

The study used an algorithmic method to measure fluctuations in the timetable of gait cycle events in patients with Parkinson's disease (PD). Subjects with severe PD (n=10; age 63.6 ± 10.1 years; Hoehn & Yahr [H & Y] disability score 3 or 4), mild PD (n=10; age 65.5 ± 4.3; H & Y ≦ 2), and normal controls (n=10; age 65.1 ± 13.3) were studied. A camera was mounted on the trunk, and the subjects walked in a self-selected manner. Overhead images of the foot path were analyzed to geometrically describe motion in terms of displacement and velocity. The timing of three gait events, i.e.,¹⁾ feet adjacent,²⁾ maximum speed of swinging foot, and³⁾ the trunk climbing to its highest point in mid-stance, was determined for extracted steps during steady-state gait. In severe PD, 74.9 ± 21.7% of steps was timetabled so that the swinging leg and the stance-phase leg became side by side before the trunk rose to its highest point to achieve 'foot clearance'. This pattern was significantly less prevalent in mild PD and controls. An altered timetable of gait cycle events may provide quantitative indices of gait disability during steady-state walking in patients with PD

FUNGAL BIOREACTOR WITH ULTRAMEMBRANE SEPARATION FOR DEGRADATION OF COLORED-AND ENDOCRINE DISRUPTING-SUBSTANCES

Joint Research on Environmental Science and Technology for the Eart

Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network

The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image diagnosis of lung cancer. The deep feedback GMDH-type neural network can identified very complex nonlinear systems using heuristic self-organization method which is a type of evolutionary computation. The deep neural network architectures are organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network. It is shown that the deep feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer because deep neural network architectures are automatically organized using only input and output data

Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network

The deep Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysis of brain X-ray CT image. In this algorithm, the deep neural network architectures which have many hidden layers and fit the complexity of the nonlinear systems, are automatically organized using the heuristic self-organization method so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The learning algorithm is the principal component-regression analysis and the accurate and stable predicted values are obtained. The recognition results show that the deep GMDH-type neural network algorithm is useful for the medical image analysis of brain X-ray CT images

Gastrointestinal cancer occurs as extramuscular manifestation in FSHD1 patients

Facioscapulohumeral dystrophy type1 (FSHD1) patients with a shortened D4Z4 repeat containing the DUX4 gene have a broad spectrum of clinical manifestations. In addition, high expression of DUX4 protein with an aberrant C terminus is frequently identified in B cell acute lymphoblastic leukemia. We investigated clinical manifestations in 31 FSHD1 patients and 30 non-affected individuals. Gastrointestinal cancers (gastric and colorectal cancers) increased after the age of 40 years and were more frequently observed in FSHD1 patients (n = 10) than in non-affected individuals (n = 2, p = 0.0217), though the incidence of cancers occurring in non-gastrointestinal tissues of FSHD1 patients was the same as that of non-affected individuals (p > 0.999). These comorbidities of FSHD1 patients were not associated with D4Z4 repeat number. Our results suggest that gastrointestinal cancers are among the extramuscular manifestations of adult FSHD1 patients, and do not depend on D4Z4 repeat number

Detection of an H-alpha Emission Line on a Quasar, RX J1759.4+6638, at z=4.3 with AKARI

We report the detection of an H-alpha emission line in the low resolution spectrum of a quasar, RX J1759.4+6638, at a redshift of 4.3 with the InfraRed Camera (IRC) onboard the AKARI. This is the first spectroscopic detection of an H-alpha emission line in a quasar beyond z=4. The overall spectral energy distribution (SED) of RX J1759.4+6638 in the near- and mid-infrared wavelengths agrees with a median SED of the nearby quasars and the flux ratio of F(Ly-alpha)/F(H-alpha) is consistent with those of previous reports for lower-redshift quasars.Comment: 9pages, 3 figures, Publications of the Astronomical Society of Japan, in pres