How Does the Spacer Length of Cationic Gemini Lipids Influence the Lipoplex Formation with Plasmid DNA? Physicochemical and Biochemical Characterizations and their Relevance in Gene Therapy

Lipoplexes formed by the pEGFP-C3 plasmid DNA (pDNA) and lipid mixtures containing cationic gemini surfactant of the 1,2-bis(hexadecyl dimethyl ammonium) Acmes family referred to as C16CnC16, where n = 2 3, 5, or 12, and the zwitterionic helper lipid, 1,2-dioleoyl-sn-glycero-3-phosphatidylethanolamine (DOPE) have been studied from a wide variety of physical, chemical, and biological standpoints. The study has been carried out using several experimental methods, such as zeta potential, gel electrophoresis, small-angle X-ray scattering (SAXS), cryo-TEM, gene transfection, cell viability/cytotoxicity, and confocal fluorescence microscopy. As reported recently in a communication (J. Am. Chem. Soc. 2011, 133, 18014), the detailed physicochemical and biological studies confirm that, in the presence of the studied series lipid mixtures, plasmid DNA is compacted with a large number of its associated Na+ counterions. This in turn yields a much lower effective negative charge, q(pDNA)(-), a value that has been experimentally obtained for each mixed lipid mixture. Consequently, the cationic lipid (CL) complexes prepared with pDNA and CL/DOPE mixtures to be used in gene transfection require significantly less amount of CL than the one estimated assuming a value of q(DNA)(-) = -2. This drives to a considerably lower cytotoxicity of the gene vector. Depending on the CL molar composition, alpha, of the lipid mixture, and the effective charge ratio of the lipoplex, rho(eff), the reported SAXS data indicate the presence of two or three structures in the same lipoplex, one in the DOPE-rich region, other in the CL-rich region, and another one present at any CL composition. Cryo-TEM and SAXS studies with C16CnC16/DOPE-pDNA lipoplexes indicate that pDNA is localized between the mixed lipid bilayers of lamellar structures within a monolayer of similar to 2 nm. This is consistent with a highly compacted supercoiled pDNA conformation compared with that of linear DNA. Transfection studies were carried out with HEK293T, HeLa, CHO, U343, and H460 cells. The alpha and rho(eff) values for each lipid mixture were optimized on HEK293T cells for transfection, and using these values, the remaining cells were also transfected in absence (-FBS-FBS) and presence (-FBS+FBS) of serum. The transfection efficiency was higher with the CLs of shorter gemini spacers (n = 2 or 3). Each formulation expressed GFP on pDNA transfection and confocal fluorescence microscopy corroborated the results. C16C2C16/DOPE mixtures were the most efficient toward transfection among all the lipid mixtures and, in presence of serum, even better than the Lipofectamine2000, a commercial transfecting agent Each lipid combination was safe and did not show any significant levels of toxicity. Probably, the presence of two coexisting lamellar structures in lipoplexes synergizes the transfection efficiency of the lipid mixtures which are plentiful in the lipoplexes formed by CLs with short spacer (n = 2, 3) than those with the long spacer (n = 5, 12).

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

YGa1-xMnxO3: A novel purple inorganic pigment

A purple inorganic pigment, YGa1-xMnxO3 (0 < x <= 0.10), based on hexagonal YGaO3 is reported here. The metastable series of oxides were prepared by a sol-gel technique where the dried gels, obtained from aqueous solutions of metal nitrate-citric acid mixtures, were calcined for a short duration in a preheated furnace around 850 degrees C. The purple colour of the oxides arises from the specific trigonal bipyramidal ligand field around Mn-III in a YGaO3 host. Other hexagonal RGaO3 hosts for R = Lu, Tm and Ho substituted with Mn-III also produce similar purple coloured materials.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.