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Eukaryotic cells share a basic scheme of internal organization featuring membrane-based organelles. The use of fluorescent proteins (FPs) greatly facilitated live-cell imaging of organelle dynamics and protein trafficking. One major limitation of this approach is that the fusion of an FP to a target protein can and often does compromise the function of the target protein and alter its subcellular localization. The optimization process to obtain a desirable fusion construct can be time-consuming or even unsuccessful. In this work, we set out to provide a validated set of FP-based markers for major organelles in the budding yeast (Saccharomyces cerevisiae). Out of over 160 plasmids constructed, we present a final set of 42 plasmids, the recommendations for which are backed up by meticulous evaluations. The tool set includes three colors (green, red, and blue) and covers the endoplasmic reticulum (ER), nucleus, Golgi apparatus, endosomes, vacuoles, mitochondria, peroxisomes, and lipid droplets. The fidelity of the markers was established by systematic cross-comparison and quantification. Functional assays were performed to examine the impact of marker expression on the secretory pathway, endocytic pathway, and metabolic activities of mitochondria and peroxisomes. Concomitantly, our work constitutes a reassessment of organelle identities in this model organism. Our data support the recognition that “late Golgi” and “early endosomes,” two seemingly distinct terms, denote the same compartment in yeast. Conversely, all other organelles can be visually separated from each other at the resolution of conventional light microscopy, and quantification results justify their classification as distinct entities. IMPORTANCE Cells contain elaborate internal structures. For eukaryotic cells, like those in our bodies, the internal space is compartmentalized into membrane-bound organelles, each tasked with specialized functions. Oftentimes, one needs to visualize organelles to understand a complex cellular process. Here, we provide a validated set of fluorescent protein-based markers for major organelles in budding yeast. Yeast is a commonly used model when investigating basic mechanisms shared among eukaryotes. Fluorescent proteins are produced by cells themselves, avoiding the need for expensive chemical dyes. Through extensive cross-comparison, we make sure that each of our markers labels and only labels the intended organelle. We also carefully examined if the presence of our markers has any negative impact on the functionality of the cells and found none. Our work also helps answer a related question: are the structures we see really what we think they are?

In this study, we propose a simple and novel data structure using hyper-links, H-struct, and a new mining algorithm, H-mine, which takes advantage of this data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has a very limited and precisely predictable main memory cost and runs very quickly in memory-based settings. Moreover, it can be scaled up to very large databases using database partitioning. When the data set becomes dense, (conditional) FP-trees can be constructed dynamically as part of the mining process. Our study shows that H-mine has an excellent performance for various kinds of data, outperforms currently available algorithms in different settings, and is highly scalable to mining large databases. This study also proposes a new data mining methodology, space-preserving mining, which may have a major impact on the future development of efficient and scalable data mining methods.

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In this paper, we present a useful data modeling methodology in data warehousing which integrates three existing approaches normally used in isolation: goal-driven, data-driven and userdriven. It comprises of four stages. Goal-driven stage produces subjects and KPIs(Key Performance Indicators) of main business fields. Data-driven stage produces subject oriented enterprise data schema. User-driven stage yields analytical requirements represented by measures and dimensions of each subject. Combination stage combines the triple-driven results. By tripledriven, we can get a more complete, more structured and more layered data model of a data warehouse. We illustrate each stage step by step using examples in our case study.