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1. Chapter Six - Heterologous expression, purification, and characterization of proteins in the lanthanome

   https://doi.org/10.1016/bs.mie.2021.02.004  

   Featherston, Emily R; Mattocks, Joseph A; Trisch, Jonathan L; Cotruvo Jr, Joseph A (April 2021, Methods in enzymology)

   Cotruvo Jr, Joseph A (Ed.)

   Recent work has revealed that certain lanthanides—in particular, the more earth-abundant, lighter lanthanides—play essential roles in pyrroloquinoline quinone (PQQ) dependent alcohol dehydrogenases from methylotrophic and non-methylotrophic bacteria. More recently, efforts of several laboratories have begun to identify the molecular players (the lanthanome) involved in selective uptake, recognition, and utilization of lanthanides within the cell. In this chapter, we present protocols for the heterologous expression in Escherichia coli, purification, and characterization of many of the currently known proteins that comprise the lanthanome of the model facultative methylotroph, Methylorubrum extorquens AM1. In addition to the methanol dehydrogenase XoxF, these proteins include the associated c-type cytochrome, XoxG, and solute binding protein, XoxJ. We also present new, streamlined protocols for purification of the highly selective lanthanide-binding protein, lanmodulin, and a solute binding protein for PQQ, PqqT. Finally, we discuss simple, spectroscopic methods for determining lanthanide- and PQQ-binding stoichiometry of proteins. We envision that these protocols will be useful to investigators identifying and characterizing novel members of the lanthanome in many organisms. 

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2. Navigating Computational Resources for the CRISPR Classroom

   https://doi.org/10.1007/978-3-031-73734-3_11  

   Andersen, Linnea; Goller, Carlos; Samsa, Leigh Ann; Sengupta, Arnab (January 2025, Springer Nature Switzerland)

   What You Will Learn in This Chapter In this chapter, instructors will develop foundational knowledge about how to select and use computational tools to teach CRISPR-Cas technologies. Broadly speaking, CRISPR-Cas is a sequence-based technology. Computational resources provide a platform for managing and interacting with these sequences. With appropriate instructional design, computational tools are a valuable complement to lessons about CRISPR-Cas technologies and are essential support tools for CRISPR-Cas experiments. With an ever-growing suite of computational tools available, in this chapter, instructors will learn to navigate the landscape of these tools to select the most appropriate tools for their classroom or laboratory needs. Instructors will learn to identify when computational resources are appropriate for use in their classroom (and when they are not appropriate), then how to select the most appropriate tools for their unique needs. Additionally, we introduce instructors to best practices in instructional design for using CRISPR-Cas computational tools in the classroom. Throughout, instructors will learn both the rationale and principle behind selection so they can evaluate tools discussed in this chapter and new ones as they become available. 

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3. PANGEA: a new gene set enrichment tool for Drosophila and common research organisms

   https://doi.org/10.1093/nar/gkad331  

   Hu, Yanhui; Comjean, Aram; Attrill, Helen; Antonazzo, Giulia; Thurmond, Jim; Chen, Weihang; Li, Fangge; Chao, Tiffany; Mohr, Stephanie E; Brown, Nicholas H; et al (May 2023, Nucleic Acids Research)

   Abstract Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an ‘omics’ study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species. 

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4. Updatable Private Set Intersection Revisited: Extended Functionalities, Deletion, and Worst-Case Complexity

   https://doi.org/10.1007/978-981-96-0938-3_7  

   Badrinarayanan, Saikrishna; Miao, Peihan; Shi, Xinyi; Tromanhauser, Max; Zeng, Ruida (December 2024, Springer Singapore)

   Private set intersection (PSI) allows two mutually distrusting parties each holding a private set of elements, to learn the intersection of their sets without revealing anything beyond the intersection. Recent work (Badrinarayanan et al., PoPETS’22) initiates the study of updatable PSI (UPSI), which allows the two parties to compute PSI on a regular basis with sets that constantly get updated, where both the computation and communication complexity only grow with the size of the small updates and not the large entire sets. However, there are several limitations of their presented protocols. First, they can only be used to compute the plain PSI functionality and do not support extended functionalities such as PSI-Cardinality and PSI-Sum. Second, they only allow parties to add new elements to their existing set and do not support arbitrary deletion of elements. Finally, their addition-only protocols either require both parties to learn the output or only achieve low complexity in an amortized sense and incur linear worst-case complexity. In this work, we address all the above limitations. In particular, we study UPSI with semi-honest security in both the addition-only and addition-deletion settings. We present new protocols for both settings that support plain PSI as well as extended functionalities including PSI-Cardinality and PSI-Sum, achieving one-sided output (which implies two-sided output). In the addition-only setting, we also present a protocol for a more general functionality Circuit-PSI that outputs secret shares of the intersection. All of our protocols have worst-case computation and communication complexity that only grow with the set updates instead of the entire sets (except for a polylogarithmic factor). We implement our new UPSI protocols and compare with the state-of-the-art protocols for PSI and extended functionalities. Our protocols compare favorably when the total set sizes are sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth. 

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5. CaImAn an open source tool for scalable calcium imaging data analysis

   https://doi.org/10.7554/eLife.38173  

   Giovannucci, Andrea; Friedrich, Johannes; Gunn, Pat; Kalfon, Jérémie; Brown, Brandon L; Koay, Sue Ann; Taxidis, Jiannis; Najafi, Farzaneh; Gauthier, Jeffrey L; Zhou, Pengcheng; et al (January 2019, eLife)

   The human brain contains billions of cells called neurons that rapidly carry information from one part of the brain to another. Progress in medical research and healthcare is hindered by the difficulty in understanding precisely which neurons are active at any given time. New brain imaging techniques and genetic tools allow researchers to track the activity of thousands of neurons in living animals over many months. However, these experiments produce large volumes of data that researchers currently have to analyze manually, which can take a long time and generate irreproducible results. There is a need to develop new computational tools to analyze such data. The new tools should be able to operate on standard computers rather than just specialist equipment as this would limit the use of the solutions to particularly well-funded research teams. Ideally, the tools should also be able to operate in real-time as several experimental and therapeutic scenarios, like the control of robotic limbs, require this. To address this need, Giovannucci et al. developed a new software package called CaImAn to analyze brain images on a large scale. Firstly, the team developed algorithms that are suitable to analyze large sets of data on laptops and other standard computing equipment. These algorithms were then adapted to operate online in real-time. To test how well the new software performs against manual analysis by human researchers, Giovannucci et al. asked several trained human annotators to identify active neurons that were round or donut-shaped in several sets of imaging data from mouse brains. Each set of data was independently analyzed by three or four researchers who then discussed any neurons they disagreed on to generate a ‘consensus annotation’. Giovannucci et al. then used CaImAn to analyze the same sets of data and compared the results to the consensus annotations. This demonstrated that CaImAn is nearly as good as human researchers at identifying active neurons in brain images. CaImAn provides a quicker method to analyze large sets of brain imaging data and is currently used by over a hundred laboratories across the world. The software is open source, meaning that it is freely-available and that users are encouraged to customize it and collaborate with other users to develop it further. 

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