Efficient Hardware Implementation of Probabilistic Gradient Descent Bit Flipping

This paper presents a new Bit Flipping (BF) decoder, called Probabilistic Parallel Bit Flipping (PPBF) for Low-Density Parity-Check (LDPC) codes on the Binary Symmetric Channel. In PPBF, the flipping operation is preceded with a probabilistic behavior which is shown to improve significantly the error correction performance. The advantage of PPBF comes from the fact that, no global computation is required during the decoding process and from that, all the computations can be executed in the local computing units and in-parallel. PPBF provides a considerable improvement of the decoding frequency and complexity, compared to other known BF decoders, while obtaining a significant gain in error correction. One improved version of PPBF, called non-syndrome PPBF (NS-PPBF) is also introduced, in which the global syndrome check is moved out of the critical path and a new terminating mechanism is proposed. In order to show the superiority of the new decoders in terms of hardware efficiency and decoding throughput, the corresponding hardware architectures are presented in the second part of the paper. The ASIC synthesis results confirm that, the decoding frequency of the proposed decoders is significantly improved, much higher than the BF decoders of literature while requiring lower complexity to be efficiently implemented

“EVALUATION OF GALPHIMIA GLAUCA STEM METHANOL EXTRACT FRACTIONS FOR ANALGESIC AND ANTI-INFLAMMATORY ACTIVITIES”

Objective: This current investigation assesses in vivo central and peripheral analgesic effects and anti-inflammatory properties of fractions obtained from Galphimia glauca (GG) stem methanol extract. Methods: The laboratory models such as Swiss albino mice and Wistar albino rats were employed in the studies. The GG stem methanol extract was subjected to fractionation with solvents such as hexane, chloroform, ethyl acetate, and methanol. Orally, the dose range of 100, 200, and 400 mg/kg was given for 1 day for evaluating analgesic (hotplate test, tail clip test, writhing test, and formalin test) and weekdays for assessing anti-inflammatory activity (carrageenan and cotton pellet test methods), respectively. The experimental studies were further conducted for determining the involvement of central and peripheral receptor actions in the analgesic activity of the extract by prechallenging it with naloxone and acetic acid, respectively. The in vivo anti-inflammatory studies were conducted using carrageenan-induced rat paw edema model and cotton pellet granuloma test. Results: The LD50 of the extract was found to be >2000 mg/kg b.w. The methanol fraction of 400 mg/kg dose exhibited significant (p≤0.001) and dose-dependent analgesic and anti-inflammatory activity. It also exhibited central and peripheral analgesic actions when treated with naloxone and acetic acid, respectively. Conclusion: The results revealed that the stem methanol fraction has more potential in terms of analgesic and anti-inflammatory properties

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p

Diuretic activity of <i style="">Bixa orellana</i> Linn. leaf extracts

353-355The present investigation was aimed at investigating the diuretic activity of Bixa orellana Linn. leaves. The dried leaf powder was subjected to successive Soxhlet extraction with petroleum ether, chloroform, ethyl acetate, methanol and water. Among these, petroleum ether, methanolic and aqueous extracts were investigated for diuretic activity in Wistar rats using standard methods. The diuretic activity was assessed in terms of urine output and levels of sodium, potassium and chloride in urine. The results obtained revealed that the methanolic extract showed significant diuretic activity at a dose of 500 mg/kg body weight by increasing the total volume of urine and levels of sodium, potassium and chloride in urine when compared to standard drug, Furosemide and Arachis oil as control and vehicle for the extracts