Using machine learning for decoy discrimination in protein tertiary structure prediction.

In this thesis, the novelty of using machine learning to identify the low-RMSD structures in decoy discrimination in protein tertiary structure prediction is investigated. More specifically, neural networks are used to learn to recognize low-RMSD structures, using native protein structures as positive training examples, and simulated decoy structures as negative training examples. Simulated decoy structures are derived by reversing the sequences of native structures in the set of positive training examples, and threading the reversed sequences back to the native structures. Various input features, extracted from these native and simulated decoy structures, are used as inputs to the neural networks. These input features are the identities of residue pairs, the separation between the residues along the sequence, the pairwise distance and the relative solvent accessibilities of the residues. Various neural networks are created depending on the amount of input features used. The neural networks are tested against the in-house pairwise potentials of mean force method, as well as against a K-Nearest Neighbours algorithm. The second novel idea of this thesis is to use evolutionary information in the decoy discrimination process. Evolutionary information, in the form of PSI-BLAST profiles, is used as inputs to the neural networks. Results have shown that the best performing neural network is the one that uses in put information comprising of PSI-BLAST profiles of residue pairs, pairwise distance and the relative solvent accessibilities of the residues. This neural network is the best among all methods tested, including the pairwise potentials method, in discriminating the native structures. Therefore this thesis has demonstrated the feasibility of using machine learning, more specifically neural networks, in the problem of decoy discrimination. More significantly, evolutionary information in the form of PSI-BLAST profiles has been success fully used to further improve decoy discrimination, particularly in the discrimination of native structures

The statistical properties of technical trading rules

A portfolio of 200 heterogeneous technical trading rules is tested for their directional predictabilities on the DJIAI from 1988 to 1999. We also explore several nonparametric techniques designed for brain research, and detected possibly other forms of dependencies more significant than the traditional linear autocorrelation for the time series. The overall conditional mean directional predictability is 46%. 36 percent of the rules have more than 50% directional predictability, and the top 20 percent rules has a 73% directional predictability, whereas the bottom 80 percent has a directional predictability of 40%. Buy signals consistently generate higher predictability than sell signals but do not commensurate with their respective risk levels. The relationship between two sub-periods is not stable, while the difference between the conditional mean directional predictability of buy only and sell only signals is highly significance. The belief that most successful rules have a directional predictability of 25% to 50% coincides with the mode of distribution. We observe counter intuitive relationship between volatility and directional predictability. The results of directional predictability in a downtrend concur with the argument that buy-and-hold strategy is not a suitable benchmark. Attempts are made to tackle the issues of small sample bias, data snooping, size of test window, bootstrap or t-test, and homogeneity. Issues are discussed on empirical testing for their real world applications, statistical and non-statistical interpretations; also randomness test; physical or biological science approach

Colon cryptogenesis: Asymmetric budding

The process of crypt formation and the roles of Wnt and cell-cell adhesion signaling in cryptogenesis are not well described; but are important to the understanding of both normal and cancer colon crypt biology. A quantitative 3D-microscopy and image analysis technique is used to study the frequency, morphology and molecular topography associated with crypt formation. Measurements along the colon reveal the details of crypt formation and some key underlying biochemical signals regulating normal colon biology. Our measurements revealed an asymmetrical crypt budding process, contrary to the previously reported symmetrical fission of crypts. 3D immunofluorescence analyses reveals heterogeneity in the subcellular distribution of E-cadherin and β-catenin in distinct crypt populations. This heterogeneity was also found in asymmetrical budding crypts. Singular crypt formation (i.e. no multiple new crypts forming from one parent crypt) were observed in crypts isolated from the normal colon mucosa, suggestive of a singular constraint mechanism to prevent aberrant crypt production. The technique presented improves our understanding of cryptogenesis and suggests that excess colon crypt formation occurs when Wnt signaling is perturbed (e.g. by truncation of adenomatous polyposis coli, APC protein) in most colon cancers

STARA fight or flight: a two-wave time-lagged study of challenge and hindrance appraisal of STARA awareness on basic psychological needs and individual competitiveness productivity among hospitality employees

The introduction of smart technologies, artificial intelligence, robotics, and algorithms (STARA) has changed the workforce significantly, with many concerns about its impact on employees. This study elucidates how one’s appraisal of this situation would influence basic psychological needs and individual competitiveness productivity. Using a two-wave time-lagged study, data collected from 224 hospitality employees was examined using the partial least squares method structural equation modelling (PLS-SEM). Results suggested that individual appraisal towards STARA awareness has differential outcomes towards satisfying basic psychological needs. Among the three basic psychological needs, the needs for relatedness and competency were positively related to individual competitive productivity (ICP). We extend extant studies by incorporating challenge-hindrance framework and self-determination theory (SDT) in the context of the future of work involving STARA. It advances the body of knowledge in understanding a more fundamental issue of how STARA can bring out the best in employees, how STARA shapes employees’ opinions and perspectives of the work they are doing, and what they should do to work alongside STARA

Spatial Variation in Foliar Chemicals Within Radish (Raphanus sativus) Plants and Their Effects on Performance of Spodoptera litura

Foliar chemicals are variable within a plant and this may affect herbivore feeding preference. This study was carried out to quantify concentrations of primary (nitrogen, water, and total nonstructural carbohydrates) and secondary substances (sinigrin) in young and old leaves of Raphanus sativus L. and to evaluate performance and survival of a generalist herbivore Spodoptera litura F. feeding on them. Forty to 50-d-old R. sativus plants were used in both foliar chemical analysis and insect performance bioassays. Leaves located on the third to the sixth node from the base of the plant were defined as old leaves and the remaining leaves (from seventh node to the plant apex) of the plant were referred as young leaves in this study. All foliar chemicals except water differed significantly between young and old leaves. Moreover, young leaves were more nutritious but much more defended, based on sinigrin content, against S. litura than old leaves. Performance and survival of S. litura were reduced on young leaves as compared with old leaves. Male and female larval duration only differed significantly on young leaves. Female larval development time was longer than male development time on young leaves, but not on older leaves. Therefore, this study revealed that defenses in young leaves have differential effects upon male and female S. litura

The quantitative soil pit method for measuring belowground carbon and nitrogen stocks

Many important questions in ecosystem science require estimates of stocks of soil C and nutrients. Quantitative soil pits provide direct measurements of total soil mass and elemental content in depth-based samples representative of large volumes, bypassing potential errors associated with independently measuring soil bulk density, rock volume, and elemental concentrations. The method also allows relatively unbiased sampling of other belowground C and nutrient stocks, including roots, coarse organic fragments, and rocks. We present a comprehensive methodology for sampling these pools with quantitative pits and assess their accuracy, precision, effort, and sampling intensity as compared to other methods. At 14 forested sites in New Hampshire, nonsoil belowground pools (which other methods may omit, double-count, or undercount) accounted for upward of 25% of total belowground C and N stocks: coarse material accounted for 4 and 1% of C and N in the O horizon; roots were 11 and 4% of C and N in the O horizon and 10 and 3% of C and N in the B horizon; and soil adhering to rocks represented 5% of total B-horizon C and N. The top 50 cm of the C horizon contained the equivalent of 17% of B-horizon carbon and N. Sampling procedures should be carefully designed to avoid treating these important pools inconsistently. Quantitative soil pits have fewer sources of systematic error than coring methods; the main disadvantage is that because they are time-consuming and create a larger zone of disturbance, fewer observations can be made than with cores

Dimeric FcγR ectodomains detect pathogenic anti-platelet factor 4-heparin antibodies in heparin-induced thromobocytopenia

Background Heparin‐induced thrombocytopenia (HIT) is a major and potentially fatal consequence of antibodies produced against platelet factor 4 (PF4)–heparin complexes following heparin exposure. Not all anti‐PF4–heparin antibodies are pathogenic, so overdiagnosis can occur, with resulting inappropriate use of alternative anticoagulation therapies that have associated risks of bleeding. However, definitive platelet functional assays are not widely available for routine analysis. Objectives To assess the utility of dimeric recombinant soluble FcγRIIa (rsFcγRIIa) ectodomains for detecting HIT antibodies. Patients/Methods Plasma from 27 suspected HIT patients were tested for pathogenic anti‐PF4–heparin antibodies by binding of a novel dimeric FcγRIIa ectodomain probe. Plasmas were also tested by the use of PF4–heparin IgG ELISA, the HemosIL AcuStar HIT IgG‐specific assay, and a serotonin release assay (SRA). Results The dimeric rsFcγRIIa test produced no false positives and excluded four samples that were positive by IgG ELISA. In this small patient cohort, the novel assay correctly assigned 93% of the suspected HIT patients, with two of the HIT patients being scored as false negatives. The improved discrimination of the novel assay over the IgG ELISA, which scored four false positives, supports the mechanistic interpretation that binding of dimeric rsFcγRIIa detects pairs of closely spaced IgG antibodies in PF4–heparin immune complexes. Conclusions This study found the cell‐free, function‐based dimeric rsFcγRIIa assay to be convenient, simple, and potentially predictive of HIT. The assay had improved specificity over the IgG ELISA, and correlated strongly with the AcuStar HIT IgG‐specific assay, warranting further evaluation of its potential to identify HIT in larger patient cohorts

Simulation of nanodielectrics: nanoparticle and interphase effects on electric field distributions

Nanodielectrics have been regarded as a class of material system that can provide significantly improved chemical, mechanical and dielectric properties over conventional microcomposites. This is due to the presence of a high volume fraction of the interphase between nanoparticles and polymers. However, precise effects of nanodielectrics are not well understood, leading to difficulties in interpreting the dielectric behaviours of nanodielectrics. In the current work, effects of nanoparticle distributions, interparticle distances, nanoparticle sizes, interphase permittivities and interphase thicknesses on the possible electric field variations within a nanodielectric model have been simulated using Finite Element Method Magnetics (FEMM) 4.2. The results demonstrate that different nanoparticle and interphase configurations lead to different effects on the electric field intensity within the nanodielectric model. Mechanisms leading to changes in dielectric properties based on the observed electric field variations are discussed

The effect of water deficit and excess copper on proline metabolism in Nicotiana benthamiana

Fluctuation in proline content is a widespread phenomenon among plants in response to heavy metal stress. To distinguish between the participation of water deficit and copper on changes in proline metabolism, potted plants and floating leaf discs of tobacco were subjected to CuSO4 treatments. The application of copper increased the proline content in the leaves concomitantly with decreased leaf relative water content and increased abscisic acid (ABA) content in the potted plant. Excess copper increased the expression of two proline synthesis genes, pyrroline-5-carboxylate synthetase (P5CS) and ornithine aminotransferase (OAT) and suppressed proline catabolism gene, proline dehydrogenase (PDH). However, in the experiment with tobacco leaf discs floating on CuSO4 solutions, the excess copper decreased proline content and suppressed the expression of the P5CS, OAT and PDH genes. Therefore, proline accumulation in the potted tobacco plants treated with excess Cu treatment might not be the consequence of the increased copper content in tobacco leaves but rather by the accompanied decrease in water content and/or increased ABA content

Primary recovery of lipase derived from Burkholderia sp. ST8 with aqueous micellar two-phase system

The partitioning and recovery of lipase derived from Burkholderia sp. ST8 strain was explored using temperature-induced aqueous micellar two-phase system (AMTPS) composed of single nonionic surfactant. Nonionic surfactant Triton X-114 and Pluronic series (triblock copolymer) were evaluated in terms of their clouding phenomenon (cloud-point temperature) and the performance of the lipase partitioning in these AMTPSs. Pluronic L81 showed the most optimum partition efficiency for the recovery of lipase to the micellar phase of the AMTPS. Based on the AMTPS which consisted of 24 (w/w) Pluronic L81 and 0.5 (w/w) potassium chloride (KCl), the selectivity of lipase partitioned to bottom phase has been enhanced to 0.035 and the lipase was purified 7.2 fold. Furthermore, the lipase from the micellar phase was consecutively extracted to a new aqueous solution, with an aim of removing the surfactant from the purified lipase. It was attained by replacing the aqueous top phase from the primary recovery of AMTPS with a new potassium thiocyanate (KSCN) solution. The lipase was then recovered in the newly formed bottom aqueous phase which culminated in the yield of 89 and partition coefficients of 0.34 and 4.50 for lipase and surfactant, respectively. AMTPS offers a convenient and efficient method for the primary recovery of lipase with low cost, large loading capacity and the potential of linear scale up. © 2011 Elsevier Ltd. All rights reserved