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1. TEfinder: A Bioinformatics Pipeline for Detecting New Transposable Element Insertion Events in Next-Generation Sequencing Data

   https://doi.org/10.3390/genes12020224  

   Sohrab, Vista; López-Díaz, Cristina; Di Pietro, Antonio; Ma, Li-Jun; Ayhan, Dilay Hazal (February 2021, Genes)

   null (Ed.)

   Transposable elements (TEs) are mobile elements capable of introducing genetic changes rapidly. Their importance has been documented in many biological processes, such as introducing genetic instability, altering patterns of gene expression, and accelerating genome evolution. Increasing appreciation of TEs has resulted in a growing number of bioinformatics software to identify insertion events. However, the application of existing tools is limited by either narrow-focused design of the package, too many dependencies on other tools, or prior knowledge required as input files that may not be readily available to all users. Here, we reported a simple pipeline, TEfinder, developed for the detection of new TE insertions with minimal software and input file dependencies. The external software requirements are BEDTools, SAMtools, and Picard. Necessary input files include the reference genome sequence in FASTA format, an alignment file from paired-end reads, existing TEs in GTF format, and a text file of TE names. We tested TEfinder among several evolving populations of Fusarium oxysporum generated through a short-term adaptation study. Our results demonstrate that this easy-to-use tool can effectively detect new TE insertion events, making it accessible and practical for TE analysis. 

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2. Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage

   https://doi.org/10.1145/3526113.3545704  

   Brackenbury, Will; Chard, Kyle; Elmore, Aaron; Ur, Blase (October 2022, Proceedings of the 35th ACM Symposium on User Interface Software and Technology (UIST))

   Personal cloud storage systems increasingly offer recommendations to help users retrieve or manage files of interest. For example, Google Drive's Quick Access predicts and surfaces files likely to be accessed. However, when multiple, related recommendations are made, interfaces typically present recommended files and any accompanying explanations individually, burdening users. To improve the usability of ML-driven personal information management systems, we propose a new method for summarizing related file-management recommendations. We generate succinct summaries of groups of related files being recommended. Summaries reference the files' shared characteristics. Through a within-subjects online study in which participants received recommendations for groups of files in their own Google Drive, we compare our summaries to baselines like visualizing a decision tree model or simply listing the files in a group. Compared to the baselines, participants expressed greater understanding and confidence in accepting recommendations when shown our novel recommendation summaries. 

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3. geodl: An R package for geospatial deep learning semantic segmentation using torch and terra

   https://doi.org/10.1371/journal.pone.0315127  

   Maxwell, Aaron E; Farhadpour, Sarah; Das, Srinjoy; Yang, Yalin (December 2024, PLOS ONE)

   Sun, Xiaoyong (Ed.)

   Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development. 

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4. FireMarshal: Making HW/SW Co-Design Reproducible and Reliable

   https://doi.org/10.1109/ISPASS51385.2021.00052  

   Pemberton, Nathan; Amid, Alon (March 2021, 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS))

   null (Ed.)

   Reproducibility in the sciences is critical to reliable inquiry, but is often easier said than done. In the computer architecture community, research may require modifying systems from low-level circuits to operating systems and highlevel applications. All of these moving parts make reproducible experiments on full-stack systems challenging to design. Furthermore, the computing ecosystem evolves quickly, leading to rapidly obsolete artifacts. This is especially true in the realm of software where applications are often updated on a monthly, or even daily, cadence. In this paper we introduce FireMarshal, a software workload management tool for RISC-V based full-stack hardware development and research. FireMarshal automates workload generation (constructing boot binaries and filesystem images), development (with functional simulation), and evaluation (with cycle-exact RTL simulation). It also ensures, to the extent possible, that the exact same software runs deterministically across all phases of development, providing confidence in correctness and accuracy while minimizing time spent on slow and expensive RTL-level simulation. To ease workload specification, FireMarshal provides sane defaults for common components like firmware and operating systems, freeing users to focus only on project-specific components. Beyond reproducibility, Fire- Marshal enables continued development of workloads through the use of inheritance, where new workloads can be derived from established and continually updated base workloads. Users communicate their designs through the use of simple JSON configuration files that can be easily version controlled, reused, and shared. In this paper, we describe the design of FireMarshal along with the associated software management methodology for architectural research and development. 

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5. Moving from Composable to Programmable

   https://doi.org/10.1109/IPDPSW55747.2022.00209  

   Chen, Zhongyi; Renambot, Luc; Long, Lance; Brown, Maxine; Johnson, Andrew E. (May 2022, 36th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW))

   In today’s Big Data era, data scientists require modern workflows to quickly analyze large-scale datasets using complex codes to maintain the rate of scientific progress. These scientists often rely on available campus resources or off-the-shelf computational systems for their applications. Unified infrastructure or over-provisioned servers can quickly become bottlenecks for specific tasks, wasting time and resources. Composable infrastructure helps solve these problems by providing users with new ways to increase resource utilization. Composable infrastructure disaggregates a computer’s components – CPU, GPU (accelerators), storage and networking – into fluid pools of resources, but typically relies upon infrastructure engineers to architect individual machines. Infrastructure is either managed with specialized command-line utilities, user interfaces or specification files. These management models are cumbersome and difficult to incorporate into data-science workflows. We developed a high-level software API, Composastructure, which, when integrated into modern workflows, can be used by infrastructure engineers as well as data scientists to reorganize composable resources on demand. Composastructure enables infrastructures to be programmable, secure, persistent and reproducible. Our API composes machines, frees resources, supports multi-rack operations, and includes a Python module for Jupyter Notebooks. 

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