A NASTRAN/TREETOPS solution to a flexible, multi-body dynamics and controls problem on a UNIX workstation

Demands for nonlinear time history simulations of large, flexible multibody dynamic systems has created a need for efficient interfaces between finite-element modeling programs and time-history simulations. One such interface, TREEFLX, an interface between NASTRAN and TREETOPS, a nonlinear dynamics and controls time history simulation for multibody structures, is presented and demonstrated via example using the proposed Space Station Mobile Remote Manipulator System (MRMS). The ability to run all three programs (NASTRAN, TREEFLX and TREETOPS), in addition to other programs used for controller design and model reduction (such as DMATLAB and TREESEL, both described), under a UNIX Workstation environment demonstrates the flexibility engineers now have in designing, developing and testing control systems for dynamically complex systems

Structure-function study of a plant protein proteinase inhibitor: site-directed mutagenesis of Cucurbita maxima trypsin inhibitor V

Cucurbita marima Trypsin Inhibitor-V (CMTI-V), isolated from pumpkin, is an inhibitor of trypsin and [beta]-Factor XIIa (FXIIa). CMTI-V binds to enzyme as would a substrate, but whereas substrate is cleaved at its scissile bond and the resulting fragments released, CMTI-V remains bound to its target enzyme, thus inhibiting activity. Amino acids at the amino side of the scissile bond are referred to as P1, P2, etc., away from the bond. Carboxyl-side amino acids are referred to as P1\u27, P2\u27, etc., away from the bond. We focused on the inhibitor amino acid positions P1\u27 (D45) and P3 (V42). CMTI-V belongs to the Potato I inhibitor family, all members of which have a negatively-charged residue at P1\u27 and conserve a hydrophobic (usually Val) residue at P3. We hypothesized that the P1\u27 residue affects stabilization of the transition-state inhibitor-enzyme complex, preventing inhibitor cleavage and release. We additionally hypothesized that the P3 Val side chain is important in positioning the main chain for optimal inhibitor-enzyme contacts between P1-P4. Mutants D45L, D45E, D45N, D45V, V42G, V42M and V42S were constructed and activities against trypsin and FXIIa were compared to wildtype. Inhibitory assay data on activity against trypsin and FXIIa indicate a loss of function for mutants D45L, D45N and D45V. Little change was seen for mutants D45E, V42G, V42M and V42S. We also tested the ability of all mutants versus wildtype to bind over longer periods of time (24-48 hours) to target enzymes. We found an inability of mutants D45L and D45V to bind to both trypsin and FXIIa. D45E bound well to both enzymes. There were slighter decreases in the ability of all other mutants (D45N, V42G, V42M and V42S) to bind to target enzyme. These data suggest that the negatively-charged side chain at the P1\u27 position is essential to CMTI-V\u27s inhibitory function. The hydrophobic side chain at the P3 position appears to play a role in the ability of the inhibitor to bind for long periods of time to target enzyme although loss of hydrophobicity at that position does not affect inhibitory activity as measured by enzyme inhibition assays

Valoración de escenarios futuros a través de la conectividad del paisaje

En este trabajo se presenta una metodología SIG de valoración de la conectividad del paisaje aplicada a la valoración de escenarios futuros para el Área Metropolitana de Granada. Esta metodología será empleada para realizar un análisis comparado de las pérdidas de conectividad que los tres escenarios introducen unos con respecto a otros y se revela, junto con la generación de escenarios futuros como un instrumento útil para ayudar en la toma de decisiones en lo que respecta a las formas, lugares e intensidades del crecimiento urbano.This paper presents a GIS methodology to assess landscape connectivity through urban growth scenarios in the Metropolitan Area of Granada. This methodology will be useful to assess landscape connectivity loss in a compared evaluation of the scenarios. Thus, this evaluation will be show as a useful tool, combined with the scenario simulation, to help in the metropolitan decision making process

Efficacy of antiplatelet therapy in secondary prevention following lacunar stroke:Pooled analysis of randomized trials

Background and Purpose: Lacunar stroke accounts for ≈25% of ischemic stroke, but optimal antiplatelet regimen to prevent stroke recurrence remains unclear. We aimed to evaluate the efficacy of antiplatelet agents in secondary stroke prevention after a lacunar stroke. Methods: We searched MEDLINE, Embase, and the Cochrane library for randomized controlled trials that reported risk of recurrent stroke or death with antiplatelet therapy in patients with lacunar stroke. We used random effects meta-analysis and evaluated heterogeneity with I2. Results: We included 17 trials with 42 234 participants (mean age 64.4 years, 65% male) and follow up ranging from 4 weeks to 3.5 years. Compared with placebo, any single antiplatelet agent was associated with a significant reduction in recurrence of any stroke (risk ratio [RR] 0.77, 0.62–0.97, 2 studies) and ischemic stroke (RR 0.48, 0.30–0.78, 2 studies), but not for the composite outcome of any stroke, myocardial infarction, or death (RR 0.89, 0.75–1.05, 2 studies). When other antiplatelet agents (ticlodipine, cilostazol, and dipyridamole) were compared with aspirin, there was no consistent reduction in stroke recurrence (RR 0.91, 0.75–1.10, 3 studies). Dual antiplatelet therapy did not confer clear benefit over monotherapy (any stroke RR 0.83, 0.68–1.00, 3 studies; ischemic stroke RR 0.80, 0.62–1.02, 3 studies; composite outcome RR 0.90, 0.80–1.02, 3 studies). Conclusions: Our results suggest that any of the single antiplatelet agents compared with placebo in the included trials is adequate for secondary stroke prevention after lacunar stroke. Dual antiplatelet therapy should not be used for long-term stroke prevention in this stroke subtype

Dense nanostructured zirconia compacts obtained by colloidal filtration of binary mixtures

As starting materials two commercial nanosized zirconias doped with 3 mol% of Y 2O 3 were used: a powder of about 100 nm (TZ3YE, Tosoh, Japan) and a colloidal suspension of about 15 nm (Mel Chemicals, UK). Colloidal stability in water was studied for both zirconias in terms of zeta potential as a function of deflocculant concentration and pH. Concentrated suspensions were prepared by dispersing the powder in the colloidal suspension to solids loadings ranging from 5 to 30 vol.% using a sonication probe to achieve dispersion. The rheological behavior was optimized in terms of solids content, deflocculant content and sonication time. Optimized suspensions with up to 25 vol.% solids showed a nearly Newtonian behavior and extremely low viscosities and maintain stable for long times (days) which is an important drawback of conventional nanoparticle suspensions. Samples obtained by slip casting in plaster moulds were used for dynamic sintering studies and dense, nanostructured specimens were obtained at temperatures of 1300-1400°C.This work has been supported by Spanish Ministry of Science and Innovation (Projects MAT2009-14144-C03-02 and MAT2009-14369-C02-01). R. Moreno thanks to Universidad Politecnica de Valencia for the concession of a grant in the frame of its Programme of Support to R + D (PAID-02-11, R-1752).Benavente Martínez, R.; Salvador Moya, MD.; Alcázar, M.; Moreno, R. (2012). Dense nanostructured zirconia compacts obtained by colloidal filtration of binary mixtures. Ceramics International. 38(3):2111-2117. https://doi.org/10.1016/j.ceramint.2011.10.051S2111211738

Enrichment of clinically relevant organisms in spontaneous preterm delivered placenta and reagent contamination across all clinical groups in a large UK pregnancy cohort.

In this study differences in the placental microbiota of term and preterm deliveries from a large UK pregnancy cohort were studied using 16S targeted amplicon sequencing. The impact of contamination from DNA extraction, PCR reagents, as well as those from delivery itself were also examined. A total of 400 placental samples from 256 singleton pregnancies were analysed and differences investigated between spontaneous preterm, non-spontaneous preterm, and term delivered placenta. DNA from recently delivered placenta was extracted, and screening for bacterial DNA was carried out using targeted sequencing of the 16S rRNA gene on the Illumina MiSeq platform. Sequenced reads were analysed for presence of contaminating operational taxonomic units (OTUs) identified via sequencing of negative extraction and PCR blank samples. Differential abundance and between sample (beta) diversity metrics were then compared. A large proportion of the reads sequenced from the extracted placental samples mapped to OTUs that were also found in negative extractions. Striking differences in the composition of samples were also observed, according to whether the placenta was delivered abdominally or vaginally, providing strong circumstantial evidence for delivery contamination as an important contributor to observed microbial profiles. When OTU and genus level abundances were compared between the groups of interest, a number of organisms were enriched in the spontaneous preterm cohort, including organisms that have been previously associated with adverse pregnancy outcomes, specifically Mycoplasma spp., and Ureaplasma spp.. However, analyses of overall community structure did not reveal convincing evidence for the existence of a reproducible 'preterm placental microbiome'. IMPORTANCE: Preterm birth is associated with both psychological and physical disabilities and is the leading cause of infant morbidity and mortality worldwide. Infection is known to be an important cause of spontaneous preterm birth, and recent research has implicated variation in the 'placental microbiome' with preterm birth risk. Consistent with previous studies, the abundance of certain clinically relevant species differed between spontaneous preterm and non-spontaneous preterm or term delivered placenta. These results support the view that a proportion of spontaneous preterm births have an intra-uterine infection component. However, an additional observation from this study was that a substantial proportion of reads sequenced were contaminating reads, rather than DNA from endogenous, clinically relevant species. This observation warrants caution in the interpretation of sequencing output from such low biomass samples as the placenta

CDK2 regulates nuclear envelope protein dynamics and telomere attachment in mouse meiotic prophase

In most organisms, telomeres attach to the nuclear envelope at the onset of meiosis to promote the crucial processes of pairing, recombination and synapsis during prophase I. This attachment of meiotic telomeres is mediated by the specific distribution of several nuclear envelope components that interact with the attachment plates of the synaptonemal complex. We have determined by immunofluorescence and electron microscopy that the ablation of the kinase CDK2 alters the nuclear envelope in mouse spermatocytes, and that the proteins SUN1, KASH5 (also known as CCDC155) and lamin C2 show an abnormal cap-like distribution facing the centrosome. Strikingly, some telomeres are not attached to the nuclear envelope but remain at the nuclear interior where they are associated with SUN1 and with nuclear-envelope-detached vesicles. We also demonstrate that mouse testis CDK2 phosphorylates SUN1 in vitro. We propose that during mammalian prophase I the kinase CDK2 is a key factor governing the structure of the nuclear envelope and the telomere-led chromosome movements essential for homolog pairin

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation