Python Automation
Python Automation - Related Videos
06:27
Automated Analysis of C. elegans Fluorescence Images using SegElegans
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2025
Here we provide instructions on effectively utilizing SegElegans, a deep learning system we developed for the automated segmentation of individual worms in widefield microscopy images, for subsequent use in image analysis software such as ImageJ. We provide ways to use the system both online and offline.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
09:05
Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices
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Cited by 11 •
2017
This protocol describes how to conduct automatic image-guided patch-clamp experiments using a system recently developed for standard in vitro electrophysiology equipment.
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
10:55
Digital Microfluidics for Automated Proteomic Processing
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Cited by 30 •
2009
Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.
FAQs
A typical workflow passes information through several stages: the program reads specified inputs, applies transformations or calculations, and then sends results to a file, report, software tool, API, or test procedure. This staged structure allows each operation to follow defined logic and supports repeatable movement of measurement data, design information, or simulation results between connected engineering activities.
Automation depends on clear interfaces and specified operating conditions because the program must know how to exchange information with files, software, APIs, or equipment-related processes. When those expectations are defined, actions can occur consistently. If inputs, connections, or conditions are unclear, the workflow may not perform the intended calculation, transfer, monitoring task, or test procedure reliably.
Once a procedure is expressed as defined program logic, the same sequence can be applied across repeated tasks rather than recreated manually each time. This reduces variation in file operations, calculations, reports, simulations, or tests and makes the resulting workflow easier to reproduce. In engineering, that consistency supports more dependable comparisons and more traceable technical work.
Its adaptability comes from connecting input sources, transformations, software libraries, APIs, and output actions within one workflow. The same general structure can support measurement-data handling, analysis-tool integration, design-workflow management, simulation standardization, or process monitoring when the relevant interfaces are specified. This makes automation a foundation for linking otherwise separate technical activities rather than isolating one repetitive task.
Begin by identifying the repetitive task, its required inputs, the transformations or calculations, and the final action or output. Then define how the program will interact with files, analysis tools, APIs, simulations, design systems, or test procedures. Finally, express the sequence as program logic and apply it under the specified operating conditions so the procedure can run consistently.
Engineers may use it when measurement data must be connected systematically with analysis tools or transformed into consistent reports. The program can organize incoming information, perform defined calculations, and trigger the next analysis or reporting action. This is useful when repeated data handling consumes time and when a reproducible connection between measurements and technical decisions is important.
In simulation work, automation can standardize recurring workflow steps; in design work, it can manage connected activities; and in monitoring, it can support equipment or process observation when interfaces and operating conditions are specified. These uses reduce routine effort while creating repeatable procedures. They also provide a basis for testing and data-driven decision-making across engineering systems.