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...