A method for construction of long randomized open reading frames and polypeptides

A method is presented for construction of randomized open readingframe sequences (ORFs) and gene libraries containing them. Thebuilding blocks for the ORFs were 75 bp long DNA fragments generatedby cloning sequences from a single synthetic oligonucleotidepreparation by bridge mutagenesis. The fragments had the propertythat, regardless of their orientation in the ligated product,the ORF of the construct was maintained. The heterogeneity ofthe ORFs resulted from the random ligation of 2000 differentDNA fragments. The randomized ORFs were cloned downstream fromthe lac promoter in a multicopy plasmid in Escherichia coli.To test the method, a library of 10⁶ clones was constructed.     

Correlation of omega-3 levels in serum phospholipid from 2053 human blood samples with key fatty acid ratios

Background  

This research was conducted to explore the relationships between the levels of omega-3 fatty acids in serum phospholipid and key fatty acid ratios including potential cut-offs for risk factor assessment with respect to coronary heart disease and fatal ischemic heart disease.     

Computational mereology: A study of part-of relations for multimedia indexing

How to index or retrieve multimedia objects is by no means obvious, because the computer can retrieve right multimedia material only if it reasons about its contents. We show that it is possible to write formal specifications of this reasoning process using set theory and mereology. We discuss the theoretical consequences of trying to use mereology and set theory for multimedia indexing and retrieval. We re-examine the roles of mereology and set theory in knowledge representation. We conclude that both commonsense set theories and mereologies should play the role of constraining databases of arbitrary multimedia objects, e.g. video clips. But although both should be viewed as database constraints, we argue that part-of hierarchies should be used to encode relatively permanent background knowledge, elsewhere names thereferential level, while member-of hierarchies should describe arbitrary multimedia records. We also propose a language and a set of axioms, SetNM, for natural mereologies with sets. A multimedia indexing system can then be viewed as a particular SetNM theory.     

Elevated Circulating and Placental SPINT2 Is Associated with Placental Dysfunction

Biomarkers for placental dysfunction are currently lacking. We recently identified SPINT1 as a novel biomarker; SPINT2 is a functionally related placental protease inhibitor. This study aimed to characterise SPINT2 expression in placental insufficiency. Circulating SPINT2 was assessed in three prospective cohorts, collected at the following: (1) term delivery (n = 227), (2) 36 weeks (n = 364), and (3) 24–34 weeks’ (n = 294) gestation. SPINT2 was also measured in the plasma and placentas of women with established placental disease at preterm (<34 weeks) delivery. Using first-trimester human trophoblast stem cells, SPINT2 expression was assessed in hypoxia/normoxia (1% vs. 8% O2), and following inflammatory cytokine treatment (TNFα, IL-6). Placental SPINT2 mRNA was measured in a rat model of late-gestational foetal growth restriction. At 36 weeks, circulating SPINT2 was elevated in patients who later developed preeclampsia (p = 0.028; median = 2233 pg/mL vs. controls, median = 1644 pg/mL), or delivered a small-for-gestational-age infant (p = 0.002; median = 2109 pg/mL vs. controls, median = 1614 pg/mL). SPINT2 was elevated in the placentas of patients who required delivery for preterm preeclampsia (p = 0.025). Though inflammatory cytokines had no effect, hypoxia increased SPINT2 in cytotrophoblast stem cells, and its expression was elevated in the placental labyrinth of growth-restricted rats. These findings suggest elevated SPINT2 is associated with placental insufficiency.     

Weighted‐Covariance Factor Decomposition of Varma Models Applied to Forecasting Quarterly U.S. Real GDP at Monthly Intervals

Suppose a vector autoregressive moving‐average model is estimated for m observed variables of primary interest for an application and n–m observed secondary variables to aid in the application. An application indicates the variables of primary interest but usually only broadly suggests secondary variables that may or may not be useful. Often, one has many potential secondary variables to choose from but is unsure which ones to include in or exclude from the application. The article proposes a method called weighted‐covariance factor decomposition (WCFD), comparable to Stock and Watson's method here called principle‐components factor decomposition (PCFD), for reducing the secondary variables to fewer factors to obtain a parsimonious estimated model that is more effective in an application. The WCFD method is illustrated in the article by forecasting quarterly observed U.S. real GDP at monthly intervals using monthly observed four coincident and eight leading indicators from the Conference Board ( http://www.conference‐board.org ). The results show that root mean‐squared errors of GDP forecasts of PCFD‐factor models are 0.9–11.3% higher than those of WCFD‐factor models especially as estimation‐forecasting periods pass from the pre‐2007 Great Moderation through the 2007–2009 Great Recession to the 2009–2016 Slow Recovery.     

Modulation of enzyme activity by antibody binding to an alkaline phosphatase-epitope hybrid protein

An epitope from the HTV-1 gpl20 protein V3 loop has been insertedonto the surface of bacterial alkaline phosphatase at differentpositions in the vicinity of the enzyme active site, creatinghybrid proteins that can bind to an anti-gpl20 monoclonal antibody.One of the hybrid proteins, API1, has a 13 amino acid V3 loopsequence inserted between residues 407 and 408 of alkaline phosphatase.The enzymatic activity of this protein is modulated upon antibodybinding. API1 maintains the full activity of the wild type alkalinephosphatase but in the presence of the anti-gpl20 antibody,the enzyme activity is inhibited by 40–50%. Thus, thehybrid enzyme can be used to detect the presence of antibodyin solution. The concept of signalling proteins may have a wideapplication. Two models for the mechanism of modulation, sterichindrance and allosteric regulation, are discussed.     

Fluorographene: a two-dimensional counterpart of Teflon

A stoichiometric derivative of graphene with a fluorine atom attached to each carbon is reported. Raman, optical, structural, micromechanical, and transport studies show that the material is qualitatively different from the known graphene-based nonstoichiometric derivatives. Fluorographene is a high-quality insulator (resistivity >10(12) Ω) with an optical gap of 3 eV. It inherits the mechanical strength of graphene, exhibiting a Young's modulus of 100 N m(-1) and sustaining strains of 15%. Fluorographene is inert and stable up to 400 °C even in air, similar to Teflon.     

Fluorinated graphene: Fluorographene: A Two‐Dimensional Counterpart of Teflon (Small 24/2010)

A stoichiometric derivative of graphene with a fluorine atom attached to each carbon is reported. Raman, optical, structural, micromechanical, and transport studies show that the material is qualitatively different from the known graphene‐based nonstoichiometric derivatives. Fluorographene is a high‐quality insulator (resistivity >10¹² Ω) with an optical gap of 3 eV. It inherits the mechanical strength of graphene, exhibiting a Young’s modulus of 100 N m^?1 and sustaining strains of 15%. Fluorographene is inert and stable up to 400 °C even in air, similar to Teflon.     

Estimation of vector error correction models with mixed‐frequency data

Vector autoregressive (VAR) models with error‐correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single‐frequency data. Both unstructured and structured VAR models have been estimated and used with mixed‐frequency data. However, VECMs have not been studied or used with mixed‐frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation‐maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in‐sample monthly smoothed estimates and out‐of‐sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly‐quarterly data are used as given (neither interpolated to all‐monthly data, nor aggregated to all‐quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed‐frequency data vs. with their single‐frequency temporal aggregate.