Basonuclin Regulates a Subset of Ribosomal RNA Genes in HaCaT Cells

Basonuclin (Bnc1), a cell-type-specific ribosomal RNA (rRNA) gene regulator, is expressed mainly in keratinocytes of stratified epithelium and gametogenic cells of testis and ovary. Previously, basonuclin was shown in vitro to interact with rRNA gene (rDNA) promoter at three highly conserved sites. Basonuclin's high affinity binding site overlaps with the binding site of a dedicated and ubiquitous Pol I transcription regulator, UBF, suggesting that their binding might interfere with each other if they bind to the same promoter. Knocking-down basonuclin in mouse oocytes eliminated approximately one quarter of RNA polymerase I (Pol I) transcription foci, without affecting the BrU incorporation of the remaining ones, suggesting that basonuclin might regulate a subset of rDNA. Here we show, via chromatin immunoprecipitation (ChIP), that basonuclin is associated with rDNA promoters in HaCaT cells, a spontaneously established human keratinocyte line. Immunoprecipitation data suggest that basonuclin is in a complex that also contains the subunits of Pol I (RPA194, RPA116), but not UBF. Knocking-down basonuclin in HaCaT cells partially impairs the association of RPA194 to rDNA promoter, but not that of UBF. Basonuclin-deficiency also reduces the amount of 47S pre-rRNA, but this effect can be seen only after cell-proliferation related rRNA synthesis has subsided at a higher cell density. DNA sequence of basonuclin-bound rDNA promoters shows single nucleotide polymorphisms (SNPs) that differ from those associated with UBF-bound promoters, suggesting that basonuclin and UBF interact with different subsets of promoters. In conclusion, our results demonstrate basonuclin's functional association with rDNA promoters and its interaction with Pol I in vivo. Our data also suggest that basonuclin-Pol I complex transcribes a subset of rDNA

Rapid and high-resolution analysis of winemaking yeasts using MALDI-TOF MS : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Winemaking is a biologically diverse and dynamic process in which the grape sugar is converted into ethanol, CO2 and other aromatic compounds by yeasts. Saccharomyces cerevisiae is the main species used for wine production, whereas the contribution of non-Saccharomyces yeasts to the distinctiveness of wine was not acknowledged until the 1980s. The indigenous yeasts present in the vineyard mainly belong to non-Saccharomyces species, which can have an important impact on the final wine quality, especially where spontaneous fermentation practices are used. However, metabolic profiles of individual strains of both non-Saccharomyces and Saccharomyces species may differ significantly, and thus lead to different organoleptic properties that are important to increase the expression of terroir in the wine. In this sense, some of these yeast strains may be desirable to be isolated and used for further development of novel wine products. It is also important to identify spoilage yeasts that may contaminate wine with off-flavours. Both cases require the ability to identify yeast strains that contribute particular flavour profiles to the wine. Recently, an emerging proteomic approach of matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been successfully applied to identify yeast species relevant to winemaking. This technology has shown potential in the prediction of the utility of individual yeast strains in the production of different wine styles. Despite this interest, most work focuses on its capacity for clinical identification purposes, and the list of winemaking yeasts in current MALDI-TOF databases is not exhaustive. Furthermore, the predictive potential of this approach has not gone unchallenged. With this in mind, this study aims to further develop MALDI-TOF MS as a rapid and low-cost method for yeast identification and characterisation, as well as assess it as a tool to predict the suitability of individual yeast strains in the production of different wine styles. Based on 14 type strains and 19 field isolates representing 21 yeast species, the efficiency of MALDI-TOF MS for wine yeasts identification was improved by comparing the dried-droplet (DM) and pre-mixing (PM) methods, as well as two mass ranges of m/z 2,000-20,000 and 500-4,000. With this improved protocol, MALDI-TOF MS was used to identify the yeast isolates recovered from the production of Pinot Noir wines that were spontaneously fermented in vineyard versus in winery by an organic wine producer in Waipara, New Zealand. The corresponding MALDI profiles were integrated into our in-house database stored in Software BioNumerics v 7.6. Meanwhile, 26S rRNA sequencing was used in conjunction with Restriction Fragment Length Polymorphism (RFLP) to cross-check the yeast identification results. Afterwards, eight Saccharomyces strains of diverse origin were examined to investigate the influence of growth conditions on MALDI-TOF spectra and to determine the best medium for the use of MALDI-TOF MS to predict wine yeast utility for different wine styles production, including the Pinot Noir grape juice, Chardonnay grape juice, synthetic grape juice, and laboratory-grade artificial culture media (YPD broth and agar). With the pre-selected culture media, YPD agar and YPD broth, a panel of 59 commercial yeasts including 47 wine yeasts and 12 brewing yeasts were then used to validate the predictive potential of MALDI-TOF profiling for individual yeast strains application. Dimensionality reduction techniques (DRTs) of PCA, MDS and UMAP were performed to analyse the data by using BioNumerics v 7.6 and the conda-forge packages for Python. Compared to the routine DM method, PM improved the performance of MALDI-TOF MS on wine-associated yeast analysis and yielded well-defined identification results. This is the first known usage of low-mass range m/z 500-4,000 profiles in winemaking yeast characterisation; this mass range appears unsuitable for the identification at the species level, but may offer some advantages for infraspecific (i.e. strain) classification. This improved MALDI-TOF MS protocol was then successfully applied to indigenous yeast isolated from organically produced Pinot Noir wines for diversity analysis. Thirteen species belonging to eight genera (10 non-Saccharomyces and 3 Saccharomyces yeasts) were identified, with taxonomic diversity reducing as fermentation progressed. MALDI-TOF utility also confirmed the impact of differing production systems on yeast diversity and dynamics of spontaneous fermentation. Furthermore, the MALDI profiles appeared to reflect the impact of different fermentation environments and fermentation stages on individual yeast proteomics. In addition, the yeast cultivation conditions also showed a significant impact on MALDI-TOF profiles, with YPD agar being recommended for taxonomic studies, while YPD broth may offer an improved intra-subspecific differentiation by yielding more discriminatory peaks. MDS and UMAP analyses supported the potential of MALDI-TOF proteomics in predicting the utility of yeast strains in winemaking and brewing sectors, although further studies are necessary to more comprehensively investigate the possible commercial benefits

Estimating optimal treatment regimes in survival contexts using an instrumental variable

In survival contexts, substantial literature exists on estimating optimal treatment regimes, where treatments are assigned based on personal characteristics for the purpose of maximizing the survival probability. These methods assume that a set of covariates is sufficient to deconfound the treatment-outcome relationship. Nevertheless, the assumption can be limiting in observational studies or randomized trials in which noncompliance occurs. Thus, we advance a novel approach for estimating the optimal treatment regime when certain confounders are not observable and a binary instrumental variable is available. Specifically, via a binary instrumental variable, we propose two semiparametric estimators for the optimal treatment regime, one of which possesses the desirable property of double robustness, by maximizing Kaplan-Meier-like estimators within a pre-defined class of regimes. Because the Kaplan-Meier-like estimators are jagged, we incorporate kernel smoothing methods to enhance their performance. Under appropriate regularity conditions, the asymptotic properties are rigorously established. Furthermore, the finite sample performance is assessed through simulation studies. We exemplify our method using data from the National Cancer Institute's (NCI) prostate, lung, colorectal, and ovarian cancer screening trial

A Review of Software Reliability Testing Techniques

In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligent software technology has not only offered new opportunities but also posed challenges to software reliability technology. The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigated via related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper

Metaheuristic Algorithms in Artificial Intelligence with Applications to Bioinformatics, Biostatistics, Ecology and, the Manufacturing Industries

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. We apply a newly proposed nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA) and demonstrate its flexibility and out-performance relative to its competitors in a variety of optimization problems in the statistical sciences. In particular, we show the algorithm is efficient and can incorporate various cost structures or multiple user-specified nonlinear constraints. Our applications include (i) finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, (ii) estimating parameters in a commonly used Rasch model in education research, (iii) finding M-estimates for a Cox regression in a Markov renewal model and (iv) matrix completion to impute missing values in a two compartment model. In addition we discuss applications to (v) select variables optimally in an ecology problem and (vi) design a car refueling experiment for the auto industry using a logistic model with multiple interacting factors