Unraveling the Intricate Organization of Mammalian Mitochondrial Presequence Translocases: Existence of Multiple Translocases for Maintenance of Mitochondrial Function

Mitochondria are indispensable organelles implicated in multiple aspects of cellular processes, including tumorigenesis. Heat shock proteins play a critical regulatory role in accurately delivering the nucleus-encoded proteins through membrane-bound presequence translocase (Tim23 complex) machinery. Although altered expression of mammalian presequence translocase components had been previously associated with malignant phenotypes, the overall organization of Tim23 complexes is still unsolved. In this report, we show the existence of three distinct Tim23 complexes, namely, B1, B2, and A, involved in the maintenance of normal mitochondrial function. Our data highlight the importance of Magmas as a regulator of translocase function and in dynamically recruiting the J-proteins DnaJC19 and DnaJC15 to individual translocases. The basic housekeeping function involves translocases B1 and B2 composed of Tim17b isoforms along with DnaJC19, whereas translocase A is nonessential and has a central role in oncogenesis. Translocase B, having a normal import rate, is essential for constitutive mitochondrial functions such as maintenance of electron transport chain complex activity, organellar morphology, iron-sulfur cluster protein biogenesis, and mitochondrial DNA. In contrast, translocase A, though dispensable for housekeeping functions with a comparatively lower import rate, plays a specific role in translocating oncoproteins lacking presequence, leading to reprogrammed mitochondrial functions and hence establishing a possible link between the TIM23 complex and tumorigenicity.

Selection and thermodynamic analysis of a turbocharger for a producer gas-fuelled multi-cylinder engine

The current work addresses the use of producer gas, a bio-derived gaseous alternative fuel, in engines designed for natural gas, derived from diesel engine frames. Impact of the use of producer gas on the general engine performance with specific focus on turbo-charging is addressed. The operation of a particular engine frame with diesel, natural gas and producer gas indicates that the peak load achieved is highest with diesel fuel (in compression ignition mode) followed by natural gas and producer gas (both in spark ignite mode). Detailed analysis of the engine power de-rating on fuelling with natural gas and producer gas indicates that the change in compression ratio (migration from compression to spark ignited mode), difference in mixture calorific value and turbocharger mismatch are the primary contributing factors. The largest de-rating occurs due to turbocharger mismatch. Turbocharger selection and optimization is identified as the strategy to recover the non-thermodynamic power loss, identified as the recovery potential (the loss due to mixture calorific value and turbocharger mismatch) on operating the engine with a fuel different from the base fuel. A turbocharged after-cooled six cylinder, 5.9 l, 90 kWe (diesel rating) engine (12.2 bar BMEP) is available commercially as a naturally aspirated natural gas engine delivering a peak load of 44.0 kWe (6.0 bar BMEP). The engine delivers a load of 27.3 kWe with producer gas under naturally aspirated mode. On charge boosting the engine with a turbocharger similar in configuration to the diesel engine turbocharger, the peak load delivered with producer gas is 36 kWe (4.8 bar BMEP) indicating a de-rating of about 60% over the baseline diesel mode. Estimation of knock limited peak load for producer gas-fuelled operation on the engine frame using a Wiebe function-based zero-dimensional code indicates a knock limited peak load of 76 kWe, indicating the potential to recover about 40 kWe. As a part of the recovery strategy, optimizing the ignition timing for maximum brake torque based on both spark sweep tests and established combustion descriptors and engine-turbocharger matching for producer gas-fuelled operation resulted in a knock limited peak load of 72.8 kWe (9.9 bar BMEP) at a compressor pressure ratio of 2.30. The de-rating of about 17.0 kWe compared to diesel rating is attributed to the reduction in compression ratio. With load recovery, the specific biomass consumption reduces from 1.2 kg/kWh to 1.0 kg/kWh, an improvement of over 16% while the engine thermal efficiency increases from 28% to 32%. The thermodynamic analysis of the compressor and the turbine indicates an isentropic efficiency of 74.5% and 73%, respectively.

Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices) - one based solely on topological considerations without discriminating atomor bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Lovasz theta function, SVMs and Finding Dense Subgraphs

In this paper we establish that the Lovasz theta function on a graph can be restated as a kernel learning problem. We introduce the notion of SVM-theta graphs, on which Lovasz theta function can be approximated well by a Support vector machine (SVM). We show that Erdos-Renyi random G(n, p) graphs are SVM-theta graphs for log(4)n/n <= p < 1. Even if we embed a large clique of size Theta(root np/1-p) in a G(n, p) graph the resultant graph still remains a SVM-theta graph. This immediately suggests an SVM based algorithm for recovering a large planted clique in random graphs. Associated with the theta function is the notion of orthogonal labellings. We introduce common orthogonal labellings which extends the idea of orthogonal labellings to multiple graphs. This allows us to propose a Multiple Kernel learning (MKL) based solution which is capable of identifying a large common dense subgraph in multiple graphs. Both in the planted clique case and common subgraph detection problem the proposed solutions beat the state of the art by an order of magnitude.

From (Au5Sn + AuSn) physical mixture to phase pure AuSn and Au5Sn intermetallic nanocrystals with tailored morphology: digestive ripening assisted approach

Here we present digestive ripening facilitated interatomic diffusion for the phase controlled synthesis of homogeneous intermetallic nanocrystals of Au-Sn system. Au and Sn metal nanoparticles synthesized by a solvated metal atom dispersion (SMAD) method are employed as precursors for the fabrication of AuSn and Au5Sn which are Au-rich Au-Sn intermetallic nanocrystals. By optimizing the stoichiometry of Au and Sn in the reaction mixture, and by employing growth directing agents, the formation of phase pure intermetallic AuSn and Au5Sn nanocrystals could be realized. The as-prepared Au and Sn colloidal nanoparticles and the resulting intermetallic nanocrystals are thoroughly characterized by powder X-ray diffraction, transmission electron microscopy (TEM and STEM-EDS), and optical spectroscopy. The results obtained here demonstrate the potential of solution chemistry which allows synthesizing phase pure Au-Sn intermetallics with tailored morphology.

On the possible variation of the lightning striking distance as assumed in the IEC lightning protection standard as a function of structure height

The effect of structure height on the lightning striking distance is estimated using a lightning strike model that takes into account the effect of connecting leaders. According to the results, the lightning striking distance may differ significantly from the values assumed in the IEC standard for structure heights beyond 30m. However, for structure heights smaller than about 30m, the results show that the values assumed by IEC do not differ significantly from the predictions based on a lightning attachment model taking into account the effect of connecting leaders. However, since IEC assumes a smaller striking distance than the ones predicted by the adopted model one can conclude that the safety is not compromised in adhering to the IEC standard. Results obtained from the model are also compared with Collection Volume Method (CVM) and other commonly used lightning attachment models available in the literature. The results show that in the case of CVM the calculated attractive distances are much larger than the ones obtained using the physically based lightning attachment models. This indicates the possibility of compromising the lightning protection procedures when using CVM. (C) 2014 Elsevier B.V. All rights reserved.

The generalized Onsager model for a binary gas mixture

The Onsager model for the secondary flow field in a high-speed rotating cylinder is extended to incorporate the difference in mass of the two species in a binary gas mixture. The base flow is an isothermal solid-body rotation in which there is a balance between the radial pressure gradient and the centrifugal force density for each species. Explicit expressions for the radial variation of the pressure, mass/mole fractions, and from these the radial variation of the viscosity, thermal conductivity and diffusion coefficient, are derived, and these are used in the computation of the secondary flow. For the secondary flow, the mass, momentum and energy equations in axisymmetric coordinates are expanded in an asymptotic series in a parameter epsilon = (Delta m/m(av)), where Delta m is the difference in the molecular masses of the two species, and the average molecular mass m(av) is defined as m(av) = (rho(w1)m(1) + rho(w2)m(2))/rho(w), where rho(w1) and rho(w2) are the mass densities of the two species at the wall, and rho(w) = rho(w1) + rho(w2). The equation for the master potential and the boundary conditions are derived correct to O(epsilon(2)). The leading-order equation for the master potential contains a self-adjoint sixth-order operator in the radial direction, which is different from the generalized Onsager model (Pradhan & Kumaran, J. Fluid Mech., vol. 686, 2011, pp. 109-159), since the species mass difference is included in the computation of the density, viscosity and thermal conductivity in the base state. This is solved, subject to boundary conditions, to obtain the leading approximation for the secondary flow, followed by a solution of the diffusion equation for the leading correction to the species mole fractions. The O(epsilon) and O(epsilon(2)) equations contain inhomogeneous terms that depend on the lower-order solutions, and these are solved in a hierarchical manner to obtain the O(epsilon) and O(epsilon(2)) corrections to the master potential. A similar hierarchical procedure is used for the Carrier-Maslen model for the end-cap secondary flow. The results of the Onsager hierarchy, up to O(epsilon(2)), are compared with the results of direct simulation Monte Carlo simulations for a binary hard-sphere gas mixture for secondary flow due to a wall temperature gradient, inflow/outflow of gas along the axis, as well as mass and momentum sources in the flow. There is excellent agreement between the solutions for the secondary flow correct to O(epsilon(2)) and the simulations, to within 15 %, even at a Reynolds number as low as 100, and length/diameter ratio as low as 2, for a low stratification parameter A of 0.707, and when the secondary flow velocity is as high as 0.2 times the maximum base flow velocity, and the ratio 2 Delta m/(m(1) + m(2)) is as high as 0.5. Here, the Reynolds number Re = rho(w)Omega R-2/mu, the stratification parameter A = root m Omega R-2(2)/(2k(B)T), R and Omega are the cylinder radius and angular velocity, m is the molecular mass, rho(w) is the wall density, mu is the viscosity and T is the temperature. The leading-order solutions do capture the qualitative trends, but are not in quantitative agreement.

De novo inference of protein function from coarse-grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 mu s coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. (C) 2014 Wiley Periodicals, Inc.

In-cylinder investigations and analysis of a SI gas engine fuelled with H-2 and CO rich syngas fuel: Sensitivity analysis of combustion descriptors for engine diagnostics and control

The sensitivity of combustion phasing and combustion descriptors to ignition timing, load and mixture quality on fuelling a multi-cylinder natural gas engine with bio-derived H-2 and CO rich syngas is addressed. While the descriptors for conventional fuels are well established and are in use for closed loop engine control, presence of H-2 in syngas potentially alters the mixture properties and hence combustion phasing, necessitating the current study. The ability of the descriptors to predict abnormal combustion, hitherto missing in the literature, is also addressed. Results from experiments using multi-cylinder engines and numerical studies using zero dimensional Wiebe function based simulation models are reported. For syngas with 20% H-2 and CO and 2% CH4 (producer gas), an ignition retard of 5 +/- 1 degrees was required compared to natural gas ignition timing to achieve peak load of 72.8 kWe. It is found that, for syngas, whose flammability limits are 0.42-1.93, the optimal engine operation was at an equivalence ratio of 1.12. The same methodology is extended to a two cylinder engine towards addressing the influence of syngas composition, especially H-2 fraction (varying from 13% to 37%), on the combustion phasing. The study confirms the utility of pressure trace derived combustion descriptors, except for the pressure trace first derivative, in describing the MBT operating condition of the engine when fuelled with an alternative fuel. Both experiments and analysis suggest most of the combustion descriptors to be independent of the engine load and mixture quality. A near linear relationship with ignition angle is observed. The general trend(s) of the combustion descriptors for syngas fuelled operation are similar to those of conventional fuels; the differences in sensitivity of the descriptors for syngas fuelled engine operation requires re-calibration of control logic for MBT conditions. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Thermally Induced Demixing in an LCST Mixture in the Presence of Densely Grafted Nanoparticles: Tuning the Graft Chain Length To Induce Thermodynamic Miscibility

The demixing in an LCST mixture of PS/PVME (polystyrene/poly(vinyl methyl ether)) was probed here by melt rheology in the presence of gold nanoparticles which were densely coated with varying graft lengths of PS. The graft density for the gold nanoparticles coated with 3 kDa PS was ca. Sigma = 1.7 chains/nm(2), and that for 53 kDa PS was ca. Sigma = 1.2 chains/nm(2). The evolution of morphology, as the blends transit through the metastable and the unstable envelopes of the phase diagram, and the localization of the gold nanoparticles upon demixing were monitored using in situ hot-stage AFM and confocal Raman imaging. Interestingly, gold nanoparticles coated with 3 kDa polystyrene (PS(3 kDa)-g-nAu) were localized in the PVME phase, whereas gold nanoparticles coated with 53 kDa polystyrene (PS(53 kDa)-g-nAu) were localized in the PS phase of the blend. While the localization of PS(3 kDa)-g-nAu in the PVME phase can be expected to be of entropic origin due to expulsion from the PS phase as R-g,R-matrix chains > R-g,R-grafted chains (where R-g is the radius of gyration of the polymer chain), the localization of PS(53 kDa)-g-nAu in the PS phase is believed to be facilitated by favorable melt/graft interactions. The latter nanoparticles also delayed the demixing by 12 degrees C with respect to the neat mixture. The observed changes were addressed in context to enthalpic interactions between the grafted PS and the free PS, the entropic losses (deformational entropic losses on blending, translational entropic loss of the free PS, and the conformational entropic loss of the grafted PS), and the interface of the grafted and the free chains.

Electromagnetic shielding materials and coatings derived from gelation of multiwall carbon nanotubes in an LCST mixture

Thermally induced demixing in an LCST mixture, polystyrene (PS)/polyvinyl methyl ether] (PVME), was used as a template to design materials with high electrical conductivity. This was facilitated by gelation of multiwall carbon nanotubes (MWNTs) in a given phase of the blends. The MWNTs were mixed in the miscible blends and the thermodynamic driven demixing further resulted in selective localization in the PVME phase of the blends. This was further confirmed by atomic force microscopy (AFM). The time dependent gelation of MWNTs at shallow quench depth, evaluated using isochronal temperature sweep by rheology, was studied by monitoring the melt electrical conductivity of the samples in situ by an LCR meter coupled to a rheometer. By varying the composition in the mixture, several intricate shapes like gaskets and also coatings capable of attenuating the EM radiation in the microwave frequency can be derived. For instance, the PVME rich mixtures can be molded in the form of a gasket, O-ring and other intricate shapes while the PS rich mixtures can be coated onto an insulating polymer to enhance the shielding effectiveness (SE) for EM radiation. The SE of the various materials was analyzed using a vector network analyzer in both the X-band (8.2 to 12 GHz) and the K-u-band (12 to 18 GHz) frequency. The improved SE upon gelation of MWNTs in the demixed blends is well evident by comparing the SE before and after demixing. A reflection loss of -35 dB was observed in the blends with 2 wt% MWNTs. Further, by coating a layer of ca. 0.15 mm of PS/PVME/MWNT, a SE of -15 dB at 18 GHz could be obtained.

Mixture of Fuels Approach for the Synthesis of SrFeO3-delta Nanocatalyst and Its Impact on the Catalytic Reduction of Nitrobenzene

A modified solution combustion approach was applied in the synthesis of nanosize SrFeO3-delta (SFO) using single as well as mixture of citric acid, oxalic acid, and glycine as fuels with corresponding metal nitrates as precursors. The synthesized and calcined powders were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis and derivative thermogravimetric analysis (TG-DTG), scanning electron microscopy, transmission electron microscopy, N-2 physisorption methods, and acidic strength by n-butyl amine titration methods. The FT-IR spectra show the lower-frequency band at 599 cm(-1) corresponds to metal-oxygen bond (possible Fe-O stretching frequencies) vibrations for the perovskite-structure compound. TG-DTG confirms the formation temperature of SFO ranging between 850-900 degrees C. XRD results reveal that the use of mixture of fuels in the preparation has effect on the crystallite size of the resultant compound. The average particle size of the samples prepared from single fuels as determined from XRD was similar to 50-35 nm, whereas for samples obtained from mixture of fuels, particles with a size of 30-25 nm were obtained. Specifically, the combination of mixture of fuels for the synthesis of SFO catalysts prevents agglomeration of the particles, which in turn leads to decrease in crystallite size and increase in the surface area of the catalysts. It was also observed that the present approach also impacted the catalytic activity of the SFO in the catalytic reduction of nitrobenzene to azoxybenzene.

Beadex Function in the Motor Neurons Is Essential for Female Reproduction in Drosophila melanogaster

Drosophila melanogaster has served as an excellent model system for understanding the neuronal circuits and molecular mechanisms regulating complex behaviors. The Drosophila female reproductive circuits, in particular, are well studied and can be used as a tool to understand the role of novel genes in neuronal function in general and female reproduction in particular. In the present study, the role of Beadex, a transcription co-activator, in Drosophila female reproduction was assessed by generation of mutant and knock down studies. Null allele of Beadex was generated by transposase induced excision of P-element present within an intron of Beadex gene. The mutant showed highly compromised reproductive abilities as evaluated by reduced fecundity and fertility, abnormal oviposition and more importantly, the failure of sperm release from storage organs. However, no defect was found in the overall ovariole development. Tissue specific, targeted knock down of Beadex indicated that its function in neurons is important for efficient female reproduction, since its neuronal knock down led to compromised female reproductive abilities, similar to Beadex null females. Further, different neuronal class specific knock down studies revealed that Beadex function is required in motor neurons for normal fecundity and fertility of females. Thus, the present study attributes a novel and essential role for Beadex in female reproduction through neurons.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

Synergetic effect of size and morphology of cobalt ferrite nanoparticles on proton relaxivity

Cobalt ferrite nanoparticles with average sizes of 14, 9 and 6 nm were synthesised by the chemical co-precipitation technique. Average particle sizes were varied by changing the chitosan surfactant to precursor molar ratio in the reaction mixture. Transmission electron microscopy images revealed a faceted and irregular morphology for the as-synthesised nanoparticles. Magnetic measurements revealed a ferromagnetic nature for the 14 and 9 nm particles and a superparamagnetic nature for the 6 nm particles. An increase in saturation magnetisation with increasing particle size was noted. Relaxivity measurements were carried out to determine T-2 value as a function of particle size using nuclear magnetic resonance measurements. The relaxivity coefficient increased with decrease in particle size and decrease in the saturation magnetisation value. The observed trend in the change of relaxivity value with particle size was attributed to the faceted nature of as-synthesised nanoparticles. Faceted morphology results in the creation of high gradient of magnetic field in the regions adjacent to the facet edges increasing the relaxivity value. The effect of edges in increasing the relaxivity value increases with decrease in the particle size because of an increase in the total number of edges per particle dispersion.