More Like this

1. Exploring FlyBase Data Using QuickSearch

   https://doi.org/10.1002/cpz1.731  

   Marygold, Steven J.; the FlyBase Consortium (April 2023, Current Protocols)

   Abstract FlyBase (www.flybase.org) is the primary online database of genetic, genomic, and functional information aboutDrosophila melanogaster. The long and rich history ofDrosophilaresearch, combined with recent surges in genomic‐scale and high‐throughput technologies, means that FlyBase now houses a huge quantity of data. Researchers need to be able to query these data rapidly and intuitively, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This article describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using the “Search FlyBase” tab of QuickSearch Basic Protocol 2: Using the “Data Class” tab of QuickSearch Basic Protocol 3: Using the “References” tab of QuickSearch Basic Protocol 4: Using the “Gene Groups” tab of QuickSearch Basic Protocol 5: Using the “Pathways” tab of QuickSearch Basic Protocol 6: Using the “GO” tab of QuickSearch Basic Protocol 7: Using the “Protein Domains” tab of QuickSearch Basic Protocol 8: Using the “Expression” tab of QuickSearch Basic Protocol 9: Using the “GAL4 etc” tab of QuickSearch Basic Protocol 10: Using the “Phenotype” tab of QuickSearch Basic Protocol 11: Using the “Human Disease” tab of QuickSearch Basic Protocol 12: Using the “Homologs” tab of QuickSearch Support Protocol 1: Managing FlyBase hit lists 

   more » « less
2. Active Learning a Convex Body in Low Dimensions

   Har-Peled, Sariel; Jones, Mitchell; Saladi, Rahul (July 2020, Leibniz international proceedings in informatics)

   Czumaj, Artur; Dawar, Anuj; Merelli, Emanuela (Ed.)

   Consider a set P ⊆ ℝ^d of n points, and a convex body C provided via a separation oracle. The task at hand is to decide for each point of P if it is in C using the fewest number of oracle queries. We show that one can solve this problem in two and three dimensions using O(⬡_P log n) queries, where ⬡_P is the largest subset of points of P in convex position. In 2D, we provide an algorithm which efficiently generates these adaptive queries. Furthermore, we show that in two dimensions one can solve this problem using O(⊚(P,C) log² n) oracle queries, where ⊚(P,C) is a lower bound on the minimum number of queries that any algorithm for this specific instance requires. Finally, we consider other variations on the problem, such as using the fewest number of queries to decide if C contains all points of P. As an application of the above, we show that the discrete geometric median of a point set P in ℝ² can be computed in O(n log² n (log n log log n + ⬡(P))) expected time. 

   more » « less
3. Continuous-Time Modeling of Zipfian Workload Locality

   https://doi.org/10.1145/3746450  

   Wang, Yiyang; Ding, Chen; Sciortino, Leo; Chen, Linlin (July 2025, ACM Transactions on Modeling and Performance Evaluation of Computing Systems)

   Traditional workload analysis uses discrete times measured by data accesses. An example is the classic independent reference model (IRM). Effective solutions have been developed to model workloads with stochastic access patterns, but they incur a high cost for Zipfian workloads, which may contain millions of items each accessed with a different frequency. This paper first presents a continuous-time model of locality for workloads with stochastic access patterns. It shows that two previous techniques by Dan and Towsley and by Denning and Schwartz can be interpreted as a single model using different discrete times. Using continuous time, it derives a closed-form solution for an item and a general solution that is a differentiable function. In addition, the paper presents an approximation technique by grouping items into partitions. When evaluated using Zipfian workloads, it shows that a workload with millions of items can be approximated using a small number of partitions, and the continuous-time model has greater accuracy and is faster to compute numerically. For the largest data size verifiable using trace generation and simulation, the new techniques reduce the time of locality analysis by 6 orders of magnitude. 

   more » « less
4. Revisiting the driving force inducing phase separation in PEG-phosphate aqueous biphasic systems

   https://doi.org/10.1039/D4FD00058G  

   Bonnassieux, Sophie; Pandya, Raj; Adan Skiba, Dhyllan; Degoulange, Damien; Petit, Dorothee; Seem, Peter; Cowburn, Russell; Gallant, Betar M; Grimaud, Alexis Jules (January 2024, Faraday Discussions)

   Liquid phase separation using aqueous biphasic systems (ABS) is widely used in industrial processes for the extraction, separation and purification of macromolecules. Using water as the single solvent, a wide... 

   more » « less
5. Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

   https://doi.org/10.3389/fphys.2024.1334396  

   Debnath, Minakshi; Chang, Joshua; Bhandari, Keshav; Nagy, Dalma J; Insperger, Tamas; Milton, John G; Ngu, Anne_H H (April 2024, Frontiers in Physiology)

   Introduction:There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods:Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results:We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%–70% of real human stick falls up to 2.35 s in advance. Conclusion:These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events. 

   more » « less