The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. While numerous explanation methods have been explored, there is a need for evaluations to quantify the quality of explanation methods to determine whether and to what extent the offered explainability achieves the defined objective, and compare available explanation methods and suggest the best explanation from the comparison for a specific task. This survey paper presents a comprehensive overview of methods proposed in the current literature for the evaluation of ML explanations. We identify properties of explainability from the review of definitions of explainability. The identified properties of explainability are used as objectives that evaluation metrics should achieve. The survey found that the quantitative metrics for both model-based and example-based explanations are primarily used to evaluate the parsimony/simplicity of interpretability, while the quantitative metrics for attribution-based explanations are primarily used to evaluate the soundness of fidelity of explainability. The survey also demonstrated that subjective measures, such as trust and confidence, have been embraced as the focal point for the human-centered evaluation of explainable systems. The paper concludes that the evaluation of ML explanations is a multidisciplinary research topic. It is also not possible to define an implementation of evaluation metrics, which can be applied to all explanation methods.

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

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Est1 is a component of yeast telomerase, and est1 mutants have senescence and telomere loss phenotypes. The exact function of Est1 is not known, and it is not homologous to components of other telomerases. We previously showed that Est1 protein coimmunoprecipitates with Tlc1 (the telomerase RNA) as well as with telomerase activity. Est1 has homology to Ebs1, an uncharacterized yeast open reading frame product, including homology to a putative RNA recognition motif (RRM) of Ebs1. Deletion of EBS1 results in short telomeres. We created point mutations in a putative RRM of Est1. One mutant was unable to complement either the senescence or the telomere loss phenotype of est1 mutants. Furthermore, the mutant protein no longer coprecipitated with the Tlc1 telomerase RNA. Mutants defective in the binding of Tlc1 RNA were nevertheless capable of binding single-stranded TG-rich DNA. Our data suggest that an important role of Est1 in the telomerase complex is to bind to the Tlc1 telomerase RNA via an RRM. Since Est1 can also bind telomeric DNA, Est1 may tether telomerase to the telomere.Telomeres are the natural ends of linear chromosomes.Telomeres are maintained at a characteristic length by a balance between two forces, loss of telomeres during DNA replication and synthesis of telomeres by an enzyme called telomerase. Telomerase is a special reverse transcriptase which contains not only a reverse transcriptase catalytic subunit but also an RNA molecule which serves as the template for telomere elongation (11). In yeast, the catalytic subunit is called Est2 (7,20,25,26), and the RNA template is called Tlc1 (39).Telomerases from several organisms have been partially characterized (3,7,12,13,24,26,29,30). In general, these complexes contain components in addition to the catalytic subunit and the RNA template (10,12,24,30,36). For the yeast Saccharomyces cerevisiae, genetic screens have identified five genes (EST1, EST2, EST3, EST4/CDC13, and TLC1) (20,27,39) whose mutations lead to progressive telomere shortening and eventual loss of viability (i.e., senescence). EST2 and TLC1 encode the reverse transcriptase (25, 26) and the RNA template (39), respectively. The Cdc13 or Est4 protein can bind the single-stranded G-rich telomeric sequence both in vitro and in vivo (2,23,32). This protein apparently caps the telomere, protecting it from nucleolytic digestion. The functions of the other two genes, EST1 and EST3, are less clear. Neither of them is required for in vitro telomerase activity (5, 25), even though mutants exhibit the same senescence phenotype as TLC1 or EST2 mutants (20). There is evidence that Est1 is associated with telomerase, since Est1 coprecipitates with Tlc1 and telomerase activity (21,40). In addition, Est1 may be associated with the telomere since, like Cdc13, Est1 can bind single-stranded G-rich telomeric DNA in vitro (43). However, the affinity of Est1 for such DNA is low, much lower than the affinity of Cdc13. Unlike Cdc13, Est1 requires a free end for binding to DNA (43). We noticed a possible RNA-bind...

Long non-coding RNAs (lncRNAs) are important regulatory factors in tumor development and progression. The lncRNA CASC9.5 is located on chromosome 8 and has a total length of 1316 bp. CASC9.5 plays a tumor-promoting role in the development and progression of brain tumor and colon cancer; however, limited research has been conducted on the role of this lncRNA in lung adenocarcinoma. The present study analyzed 44 lung adenocarcinoma specimens and 2 lung cancer cell lines. It was found that CASC9.5 expression levels were significantly higher in lung cancer tissues and cells compared with normal lung tissues. In addition, the expression level of CASC9.5 was closely related to the TNM (tumor, node and metastasis) stage of lung adenocarcinomas, tumor size, tumor metastasis and tumor metabolism. Moreover, results of the in vivo and in vitro experiments all demonstrated that CASC9.5 promoted lung adenocarcinoma cell proliferation and metabolism by regulating the expression levels of cyclin D1, E-cadherin, N-cadherin and β-catenin. In summary, the present study demonstrated that high levels of CASC9.5 expression promote the proliferation, metastasis and metabolism of lung adenocarcinoma cells and might serve as a prognostic indicator. The present study provides novel findings regarding the diagnosis and treatment of lung adenocarcinoma.

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

Proposal of INTRODUCTIONZygosaccharomyces bailii is a widely distributed yeast species that is often associated with food spoilage, particularly of acidified, preserved foods containing high concentrations of fermentable sugars (Thomas & Davenport, 1985;Cole & Keenan, 1987;Makdesi & Beuchat, 1996;James & Stratford, 2011). The yeast has been proposed as a new host for several biotechnological processes (e.g. Branduardi et al., 2004) due to its ability to tolerate such environments at relatively high temperatures, which could improve the efficiency of these processes under restrictive conditions. Moreover, the high growth rate of Z. bailii and its high biomass yield make this yeast particularly attractive for heterologous protein and metabolite production (e.g. Sousa et al., 1996Sousa et al., , 1998.Despite their well-known role in food/beverage spoilage, accurate identification of Z. bailii and related yeasts to the species level using conventional taxonomic tests remains problematic. An inability to ferment and assimilate many of the carbon compounds typically used in yeast identification, as well as ambiguous tests results due to strain variability, often hampers identification (James & Stratford, 2011). Furthermore, significant intraspecific variation in internal transcribed spacer (ITS) sequences was also reported among some strains of the species (James et al., 1996), which may cause difficulties for the use of this barcode region for identifying the species (Schoch et al., 2012). We hypothesized that polyphasic analyses of the yeasts encompassed by Zygosaccharomyces bailii sensu lato may lead to a more accurate understanding of their phylogenetic relationship and taxonomic status. Here we report the molecular, physiological and morphological characterization of these yeasts, and propose two novel species near Z. bailii. METHODSYeast strains and characterization. Strains of Z. bailii sensu lato and related taxa were selected from the ATCC Mycology Collection or were provided by bioMérieux, Inc. (Table 1). Morphological observations and metabolic tests comprising the yeast standard Abbreviations: ITS, internal transcribed spacer; LSU, large subunit; SSU, small subunit.

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