B-spline collocation simulation of non-linear transient magnetic nanobio-tribological squeeze-film flow

A mathematical model is presented for magnetized nanofluid bio-tribological squeeze film flow between two approaching disks. The nanofluid comprises a suspension of metal oxide nanoparticles with an electrically-conducting base fluid, making the nano-suspension responsive to applied magnetic field. The governing viscous momentum, heat and species (nano-particle) conservation equations are normalized with appropriate transformations which renders the original coupled, nonlinear partial differential equation system into a more amenable ordinary differential boundary value problem. The emerging model is shown to be controlled by a number of parameters, viz nanoparticle volume fraction, squeeze number, Hartmann magnetic body force number, disk surface transpiration parameter, Brownian motion parameter, thermophoretic parameter, Prandtl number and Lewis number. Computations are conducted with a B-spline collocation numerical method. Validation with previous homotopy solutions is included. The numerical spline algorithm is shown to achieve excellent convergence and stability in nonlinear bio-tribological boundary value problems. The interaction of heat and mass transfer with nanofluid velocity characteristics is explored. In particular smaller nanoparticle (high Brownian motion parameter) suspensions are studied. The study is relevant to enhanced lubrication performance in novel bio-sensors and intelligent knee joint (orthopaedic) systems

A leucine aminopeptidase is involved in kinetoplast DNA segregation in <i>Trypanosoma brucei</i>

The kinetoplast (k), the uniquely packaged mitochondrial DNA of trypanosomatid protists is formed by a catenated network of minicircles and maxicircles that divide and segregate once each cell cycle. Although many proteins involved in kDNA replication and segregation are now known, several key steps in the replication mechanism remain uncharacterized at the molecular level, one of which is the nabelschnur or umbilicus, a prominent structure which in the mammalian parasite Trypanosoma brucei connects the daughter kDNA networks prior to their segregation. Here we characterize an M17 family leucyl aminopeptidase metalloprotease, termed TbLAP1, which specifically localizes to the kDNA disk and the nabelschur and represents the first described protein found in this structure. We show that TbLAP1 is required for correct segregation of kDNA, with knockdown resulting in delayed cytokinesis and ectopic expression leading to kDNA loss and decreased cell proliferation. We propose that TbLAP1 is required for efficient kDNA division and specifically participates in the separation of daughter kDNA networks

Biomass production of site selective 13C/15N nucleotides using wild type and a transketolase E. coli mutant for labeling RNA for high resolution NMR

Characterization of the structure and dynamics of nucleic acids by NMR benefits significantly from position specifically labeled nucleotides. Here an E. coli strain deficient in the transketolase gene (tktA) and grown on glucose that is labeled at different carbon sites is shown to facilitate cost-effective and large scale production of useful nucleotides. These nucleotides are site specifically labeled in C1′ and C5′ with minimal scrambling within the ribose ring. To demonstrate the utility of this labeling approach, the new site-specific labeled and the uniformly labeled nucleotides were used to synthesize a 36-nt RNA containing the catalytically essential domain 5 (D5) of the brown algae group II intron self-splicing ribozyme. The D5 RNA was used in binding and relaxation studies probed by NMR spectroscopy. Key nucleotides in the D5 RNA that are implicated in binding Mg2+ ions are well resolved. As a result, spectra obtained using selectively labeled nucleotides have higher signal-to-noise ratio compared to those obtained using uniformly labeled nucleotides. Thus, compared to the uniformly 13C/15N-labeled nucleotides, these specifically labeled nucleotides eliminate the extensive 13C–13C coupling within the nitrogenous base and ribose ring, give rise to less crowded and more resolved NMR spectra, and accurate relaxation rates without the need for constant-time or band-selective decoupled NMR experiments. These position selective labeled nucleotides should, therefore, find wide use in NMR analysis of biologically interesting RNA molecules

MicroRNA Involvement in Immune Activation During Heart Failure

Heart failure is one of the common end stages of cardiovascular diseases, the leading cause of death in developed countries. Molecular mechanisms underlying the development of heart failure remain elusive but there is a consistent observation of chronic immune activation and aberrant microRNA (miRNA) expression that is present in failing hearts. This review will focus on the interplay between the immune system and miRNAs as factors that play a role during the development of heart failure. Several studies have shown that heart failure patients can be characterized by a sustained innate immune activation. The role of inflammatory signaling is discussed and TLR4 signaling, IL-1β, TNFα and IL-6 expression appears to coincide with the development of heart failure. Furthermore, we describe the implication of the renin angiotensin aldosteron system in immunity and heart failure. In the past decade microRNAs (miRNAs), small non-coding RNAs that translationally repress protein synthesis by binding to partially complementary sequences of mRNA, have come to light as important regulators of several kinds of cardiovascular diseases including cardiac hypertrophy and heart failure. The involvement of differentially expressed miRNAs in the inflammation that occurs during the development of heart failure is still subject of investigation. Here, we summarize and comment on the first studies in this field and hypothesize on the putative involvement of certain miRNAs in heart failure. MicroRNAs have been shown to be critical regulators of cardiac function and inflammation. Future research will have to point out if dampening the immune response, and the miRNAs associated with it, during the development of heart failure is a therapeutically plausible route to follow

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues