Antiproteinuric effect predicts renal protection by angiotensin-converting enzyme inhibition in rats with established adriamycin nephrosis

1. The mechanism of renal protection by angiotensin-converting enzyme inhibition is still the subject of debate. Inhibition of proteinuria might play a role. If so, a good antiproteinuric response to angiotensin-converting enzyme inhibition should predict subsequent protection against renal structural damage. This hypothesis has not been tested in models where treatment is started after the renal disease is well established, i.e. models that mimic the clinical situation. 2. We therefore investigated this hypothesis in 96 male Wistar rats with established adriamycin nephrosis. Reduction of proteinuria was achieved by lisinopril (0, 2, 5 and 10 mg day-1 kg-1) on two different sodium diets (0.3% and 0.05% NaCl). Therapy started 6 weeks after adriamycin (at stable proteinuria) and was continued for 6 weeks. 3. Lisinopril reduced blood pressure by 32 +/- 4% and proteinuria by an average of 72 +/- 7%, with stabilization after 2 weeks. Considerable interindividual differences in antiproteinuric response was found. Glomerulosclerosis score was reduced by 15 +/- 5%. All the effects of angiotensin-converting enzyme inhibitors were enhanced by sodium depletion, but sodium depletion in itself did not affect blood pressure (124 +/- 4 mmHg), proteinuria (664 +/- 68 mg/day) or glomerulosclerosis score (30 +/- 5%). Interestingly, the more proteinuria was reduced initially in an individual rat, the less sclerosis was found in the long term in that rat. 4. In conclusion, angiotensin-converting enzyme inhibition lowers proteinuria and prevents glomerulosclerosis in established adriamycin nephrosis. These effects are enhanced by sodium depletion. The individual short-term antiproteinuric effect predicts the protection against ultimate glomerular damage. This is consistent with the hypothesis that reduction of proteinuria is a mechanism by which angiotensin-converting enzyme inhibitors exert renoprotection.     

Monolithic SiGe Up‐/Down‐Conversion Mixers with Active Baluns

The purpose of this paper is to describe the implementation of monolithically matching circuits, interface circuits, and RF core circuits to the same substrate. We designed and fabricated on‐chip 1 to 6 GHz up‐conversion and 1 to 8 GHz down‐conversion mixers using a 0.8 µm SiGe hetero‐junction bipolar transistor (HBT) process technology. To fabricate a SiGe HBT, we used a reduced pressure chemical vapor deposition (RPCVD) system to grow a base epitaxial layer, and we adopted local oxidation of silicon (LOCOS) isolation to separate the device terminals. An up‐conversion mixer was implemented on‐chip using an intermediate frequency (IF) matching circuit, local oscillator (LO)/radio frequency (RF) wideband matching circuits, LO/IF input balun circuits, and an RF output balun circuit. The measured results of the fabricated up‐conversion mixer show a positive power conversion gain from 1 to 6 GHz and a bandwidth of about 4.5 GHz. Also, the down‐conversion mixer was implemented on‐chip using LO/RF wideband matching circuits, LO/RF input balun circuits, and an IF output balun circuit. The measured results of the fabricated down‐conversion mixer show a positive power conversion gain from 1 to 8 GHz and a bandwidth of about 4.5 GHz.     

Computer modeling of the continuous annealing furnace

A computer program to calculate the strip temperature heated in the continuous annealing furnace was developed, using the zone method for radiative heat transfer analysis with the measured gas temperature in the furnace. Using theF _E Operator, the present study considered the effects of soot and transient species, in addition to the H₂O−CO₂ gas mixture on the gas radiative heat transfer. The predicted strip temperature distribution forF _E=1.05 represented well the measured data. The maximum difference in the heat flux transfered to the strip from the combustion gas forF _E=1.0 (without soot and transient species gas radiation) and 1.05 (with soot and transient species gas radiation) was about 15%. The present study also investigated the effects of line speed and thickness variations on the strip temperature, establishing the bases for the on-line computer model.     

Cramer-Rao bounds for parametric shape estimation in inverse problems

We address the problem of computing fundamental performance bounds for estimation of object boundaries from noisy measurements in inverse problems, when the boundaries are parameterized by a finite number of unknown variables. Our model applies to multiple unknown objects, each with its own unknown gray level, or color, and boundary parameterization, on an arbitrary known background. While such fundamental bounds on the performance of shape estimation algorithms can in principle be derived from the Cramer-Rao lower bounds, very few results have been reported due to the difficulty of computing the derivatives of a functional with respect to shape deformation. We provide a general formula for computing Cramer-Rao lower bounds in inverse problems where the observations are related to the object by a general linear transform, followed by a possibly nonlinear and noisy measurement system. As an illustration, we derive explicit formulas for computed tomography, Fourier imaging, and deconvolution problems. The bounds reveal that highly accurate parametric reconstructions are possible in these examples, using severely limited and noisy data.     

STATIONARY AND NON-STATIONARY STATE SPACE MODELS

Abstract. The concepts of time invariance, stationarity, non-stationarity and immemorial time are considered for state space models (SSMs). Necessary and sufficient conditions for stationarity are given and connected with standard conditions. Expressions are given for the unconditional mean and covariance matrix of the state of a time-immemorial SSM. Application of the results is made to a variety of theoretical and empirical models and the initialization of the Kalman filter in the non-stationary case and for the ARIMA ( p, d, q ) model.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

New approach to enhance adhesions between carbon nanotube emitters and substrate by the combination of electrophoresis and successive electroplating

Complementary electroplating combined with electrophoresis enhanced the field emission characteristics of emitters by improving the adhesions between CNT emitters and substrate. The emitting current of the CNT emitters prepared by our combined method increased nine times higher than that of CNT emitters prepared by electrophoresis only, since electroplating improved the adhesion of CNT emitters. During the life-time measurement for 10 h, the emitting current of CNT emitters fabricated by electrophoresis only was drastically decreased to 13% of the initial current, while that prepared by the combination of electrophoresis and successive electroplating decreased to 64% of the initial current. We suggest that our method is a promising approach for the efficient fabrication of reliable CNT emitters.     

Motion based object tracking with mobile camera

An efficient algorithm which detects and tracks a moving object is proposed. The global motion energy caused by camera movement is eliminated by finding the maximal matching position with the cross-correlation value between two consecutive frames using the spiral scanning technique. The region of a moving object is segmented exactly by combining the results of the temporal derivatives and edge map from one image, and the actual centroid of the moving object is determined as the mid-point of the cumulative distribution of the projection profile of the region