Artificial Neural Network-based error compensation procedure for low-cost encoders

An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST

Differential expression of a new isoform of DLG2 in renal oncocytoma

BACKGROUND: Renal oncocytoma, a benign tumour of the kidney, may pose a differential diagnostic problem due to overlapping phenotype with chromophobe renal cell carcinoma or other types of renal cell tumours. Therefore, identification of molecular markers would be of great value for molecular diagnostics of this tumour type. METHODS: In the current study we applied various techniques, including Affymetrix microarray hybridization and semiquantitative RT-PCR, to identify genes expressed differentially in renal oncocytomas. Subsequently, we used RACE and Northern blot hybridization to characterize the potential candidates for molecular diagnosis. RESULTS: We have identified new isoform of DLG2 gene, which contains 3'-end exons of the known DLG2 gene along with the hypothetical gene FLJ37266. The new isoform is specifically upregulated in renal oncocytoma, whereas the known DLG2 gene is downregulated in this type of kidney tumour. CONCLUSION: The new isoform of DLG2 is the promising candidate gene for molecular differential diagnostics of renal oncocytoma

Chronic kidney disease, severe arterial and arteriolar sclerosis and kidney neoplasia: on the spectrum of kidney involvement in MELAS syndrome

<p>Abstract</p> <p>Background</p> <p>MELAS syndrome (MIM ID#540000), an acronym for Mitochondrial Encephalopathy, Lactic Acidosis and Stroke-like episodes, is a genetically heterogeneous mitochondrial disorder with protean manifestations and occasional kidney involvement. Interest in the latter is rising due to the identification of cases with predominant kidney involvement and to the hypothesis of a link between mitochondrial DNA and kidney neoplasia.</p> <p>Case presentation</p> <p>We report the case of a 41-year-old male with full blown MELAS syndrome, with lactic acidosis and neurological impairment, affected by the "classic" 3243A > G mutation of mitochondrial DNA, with kidney cancer. After unilateral nephrectomy, he rapidly developed severe kidney functional impairment, with nephrotic proteinuria. Analysis of the kidney tissue at a distance from the two tumor lesions, sampled at the time of nephrectomy was performed in the context of normal blood pressure, recent onset of diabetes and before the appearance of proteinuria. The morphological examination revealed a widespread interstitial fibrosis with dense inflammatory infiltrate and tubular atrophy, mostly with thyroidization pattern. Vascular lesions were prominent: large vessels displayed marked intimal fibrosis and arterioles had hyaline deposits typical of hyaline arteriolosclerosis. These severe vascular lesions explained the different glomerular alterations including ischemic and obsolescent glomeruli, as is commonly observed in the so-called "benign" arteriolonephrosclerosis. Some rare glomeruli showed focal segmental glomerulosclerosis; as the patient subsequently developed nephrotic syndrome, these lesions suggest that silent ischemic changes may result in the development of focal segmental glomerulosclerosis secondary to nephron loss.</p> <p>Conclusions</p> <p>Nephron loss may trigger glomerular sclerosis, at least in some cases of MELAS-related nephropathy. Thus the incidence of kidney disease in the "survivors" of MELAS syndrome may increase as the support therapy of these patients improves.</p

Reappraisal of Morphological Differences between Renal Medullary Carcinoma, Collecting Duct Carcinoma, and Fumarate Hydratase-Deficient Renal Cell Carcinoma

Renal medullary carcinomas (RMCs) and collecting duct carcinomas (CDCs) are rare subsets of lethal high-stage, high-grade distal nephron-related adenocarcinomas with a predilection for the renal medullary region. Recent findings have established an emerging group of fumarate hydratase (FH)-deficient tumors related to hereditary leiomyomatosis and renal cell carcinoma (HLRCC-RCCs) syndrome within this morphologic spectrum. Recently developed, reliable ancillary testing has enabled consistent separation between these tumor types. Here, we present the clinicopathologic features and differences in the morphologic patterns between RMC, CDC, and FH-deficient RCC in consequence of these recent developments. This study included a total of 100 cases classified using contemporary criteria and ancillary tests. Thirty-three RMCs (SMARCB1/INI1-deficient, hemoglobinopathy), 38 CDCs (SMARCB1/INI1-retained), and 29 RCCs defined by the FH-deficient phenotype (FH/2SC or FH/2SC with FH mutation, regardless of HLRCC syndromic stigmata/history) were selected. The spectrum of morphologic patterns was critically evaluated, and the differences between the morphologic patterns present in the 3 groups were analyzed statistically. Twenty-five percent of cases initially diagnosed as CDC were reclassified as FH-deficient RCC on the basis of our contemporary diagnostic approach. Among the different overlapping morphologic patterns, sieve-like/cribriform and reticular/yolk sac tumor-like patterns favored RMCs, whereas intracystic papillary and tubulocystic patterns favored FH-deficient RCC. The tubulopapillary pattern favored both CDCs and FH-deficient RCCs, and the multinodular infiltrating papillary pattern favored CDCs. Infiltrating glandular and solid sheets/cords/nested patterns were not statistically different among the 3 groups. Viral inclusion-like macronucleoli, considered as a hallmark of HLRCC-RCCs, were observed significantly more frequently in FH-deficient RCCs. Despite the overlapping morphology found among these clinically aggressive infiltrating high-grade adenocarcinomas of the kidney, reproducible differences in morphology emerged between these categories after rigorous characterization. Finally, we recommend that definitive diagnosis of CDC should only be made if RMC and FH-deficient RCC are excluded

The effect of hardware/software features on the performance of an open–source network emulator

Part 5: Distributed ApplicationsInternational audienceThe authors investigate a network emulator performance versus the variability of hardware/software features of the hosting machine. In particular, the evaluation of static and dynamic delays is carried out considering several testing conditions. In detail, as concerns emulator configurations, the influence of packet rates on imposed delays values and distributions are analyzed; as for hardware and software, different values for RAM, CPU cores and operating system are tested. Results, reported as mean values and standard deviation, show two main trends: the resource availability has an important impact on the emulation stability and on the measurement repeatability; secondly, higher differences in performance levels for low imposed delay values, which is the most interesting zone in a few milliseconds latency world. The paper aims to show that the capability to emulate network impairments is generally influenced by hardware/software capabilities and it must be considered when using network emulation for specific test purposes

On Randomizing the Sending Times in TCP and other Window Based Algorithms

Current implementations of TCP suffer from performance problems like bias against flows with higher round trip times (RTTs), synchronization of windows, phase effects in flows and correlated losses leading to throughput degradations with Drop Tail queues. In this paper we propose a solution to these issues by introducing randomization into the network through end-to-end congestion control protocols. For the TCP case we call it Randomized TCP. Instead of sending back-to-back packets, Randomized TCP spaces successive packet transmissions with a time interval = RTT (1 + x)=cwnd, where x is a zero mean random number drawn from an Uniform distribution. Randomized TCP, by introducing randomization in the network, reduces synchronization, phase effects and bias against bursty traffic and longer RTT flows, prevalent with current implementations of TCP and Drop Tail Gateways. Our results suggest that by randomizing the sending times we can successfully emulate almost all the beneficial features of RED except congestion avoidance. Moreover, these benefits of randomization can be achieved even when it is partially deployed. We also introduce randomization in Binomial schemes and show performance improvements with Drop Tail queues