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ICOBA: A highly customizable iterative imagej macro for optimization of image co-localization batch analysis

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-17 DOI: 10.1016/j.softx.2025.102094

Tyler J. Rolland , Emily R. Hudson , Luke A. Graser , Brian R Weil

Co-localization analysis is pivotal for understanding protein interactions in biomedical research, yet existing ImageJ and FIJI plug-ins often lack automated multi-channel capabilities, impeding throughput and introducing potential user bias. We introduce ICOBA (Iterative Channel Overlay Batch Analysis), a freely available ImageJ macro designed to streamline and standardize co-localization workflows across large image datasets. As a demonstration of the workflow and to validate its performance, cardiac fibroblasts were immunostained and imaged on a Leica DMi8 microscope, with .tiff files exported for processing. Compared to traditional manual approaches, ICOBA demonstrated significantly faster single-channel and two-channel processing times without sacrificing quantitative accuracy. By leveraging ImageJ's built-in “record” functionality and a customizable macro script, ICOBA accommodates variable staining conditions and threshold parameters, ensuring both reproducibility and flexibility. These attributes make ICOBA a versatile solution for high-throughput, multi-channel co-localization analyses across diverse research fields, from routine lab applications to advanced tissue-imaging studies.

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引用次数: 0

BibexPy: Harmonizing the bibliometric symphony of Scopus and Web of Science

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-15 DOI: 10.1016/j.softx.2025.102098

Burak Can Kara , Alperen Şahin , Taşkın Dirsehan

Bibliometric analyses frequently encounter issues such as duplicate records, missing metadata, and inconsistent formats, reducing reliability and efficiency. Existing solutions offer minimal assistance for automated data integration and enrichment, requiring researchers to rely on manual efforts. This paper presents a Python-based software solution that addresses these challenges by merging datasets from Scopus and Web of Science, performing DOI-based deduplication, and enhancing metadata using APIs such as Unpaywall and Semantic Scholar. BibexPy provides analysis-ready formats that work with VosViewer and Biblioshiny, minimizing human labor and enhancing data quality. It enables advanced analysis like as co-citation mapping and trend identification, promoting innovation in bibliometric research approaches.

引用次数: 0

Robustimizer: A graphical user interface application for efficient uncertainty quantification, robust optimization, and reliability-based optimization of processes and designs

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-15 DOI: 10.1016/j.softx.2025.102077

Omid Nejadseyfi

The primary goal of this work is to provide easy-to-use and cutting-edge optimization software designed to handle uncertainties, intended for use in research and education. Robustimizer offers efficient uncertainty quantification through exact analytic formulas using specific surrogate models, such as Gaussian Processes. Moreover, it supports integration with other software packages, and automatic updating of initial design space through exploration–exploitation techniques, among other features. This software has proven its value in sustainable manufacturing, where optimizing processes to reduce environmental impact while managing uncertainties is critical. In this article, the Robustimizer graphical user interface is introduced as a domain-independent optimization tool for surrogate-model-based robust and reliability-based design or process optimization.

引用次数: 0

The octoPus: An open-source software for supporting farmers in the control of grapevine downy mildew octoPus：支持农民防治葡萄霜霉病的开源软件

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-15 DOI: 10.1016/j.softx.2025.102085

Simone Bregaglio , Eleonora Del Cavallo , Lorenzo Ascari , Eugenio Rossi

Achieving the European Green Deal objectives necessitates adopting Integrated Pest Management practices as the standard. The limited availability of open-source software for predicting crop fungal diseases hinders European farmers and extension services from tailoring crop protection strategies based on weather conditions favorable for infections. Here, we introduce the octoPus, a free digital tool featuring an ensemble of models predicting grapevine downy mildew outbreaks, enhanced by a machine learning algorithm and a large language model, aimed at providing science-based and easy-to-interpret decision support. The octoPus can be adapted to different scenarios and extended with additional models by third parties. With a focus on open access and code sharing, the octoPus promotes a transparent and informed approach to designing sustainable crop protection strategies.

引用次数: 0

Herschel vision: A hyperspectral image processing software for data preparation in machine learning pipelines 赫歇尔视觉：用于机器学习管道数据准备的高光谱图像处理软件

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-14 DOI: 10.1016/j.softx.2025.102089

Billy G. Ram, Sunil GC, Xin Sun

In recent years, publications have relied on either commercial software or custom-developed codes for the analysis of hyperspectral images. This practice has inadvertently restricted the broader adoption of hyperspectral imaging. Herschel Vision is an open-source hyperspectral image analysis application that enables the users to visualize and analyze hyperspectral images. Herschel Vision can automate white and dark reference calibration, spectral data visualization and extraction, generation of pseudo RGB images, cropping and segmentation of region of interests, saving images as three-dimensional cubes, and applying forward feed signal pre-processing corrections. Herschel Vision can handle proximally recorded hyperspectral data for curation of machine learning and deep learning training datasets.

引用次数: 0

CNNFET: Convolutional neural network feature Extraction Tools

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-13 DOI: 10.1016/j.softx.2025.102088

Huseyin Atasoy , Yakup Kutlu

Neither machines nor even human can learn something not represented well enough. Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. This makes them very popular in signal processing and especially in image processing community. Despite the proven success of these networks, training processes of them are often expensive in terms of time and required hardware capabilities. In this paper, a user-friendly standalone Windows application titled “Convolutional Neural Network Feature Extraction Tools” (CNNFET) is presented. The application consists of tools that extract features from image sets using certain layers of pre-trained CNNs, process them, perform classifications on them and export features for further processing in Matlab or the popular machine learning software Weka.

引用次数: 0

drugdevelopR: Planning of phase II/III drug development programs with optimal sample size allocation and Go/No-go decision rules in R

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-12 DOI: 10.1016/j.softx.2025.102066

Johannes Cepicka , Lukas D. Sauer , Marietta Kirchner, Meinhard Kieser, Stella Erdmann

Sample size determination is crucial in phase II/III drug development programs, impacting the likelihood of meeting program objectives. Within a utility-based framework, methods for optimal designs were developed recently, i.e., optimal go/no-go decision rules (whether to stop or to proceed to phase III) and optimal sample sizes minimizing the cost while maximizing the chances of achieving the program objective. These approaches can accommodate diverse scenarios like multiple phase III trials, arms, or endpoints. To facilitate the usability, the drugdevelopR R package and R Shiny applications were implemented. A sophisticated quality validation concept consisting of measures for archiving, versioning, bug reporting, and code documentation was developed, assuring reliable results.

引用次数: 0

WebTrace: An enhanced system for remote user interaction tracking and analysis in web applications

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-12 DOI: 10.1016/j.softx.2025.102075

Jiseung Pyun, Min Heo, Yehui Choe, Jongwook Jeong

Usability testing is crucial for enhancing the user experience of web applications, yet traditional methods remain prohibitively costly and complex. In this paper, we present WebTrace, a tool that facilitates lightweight and effective usability testing by remotely tracking user interactions, such as clicks and keyboard inputs, through a browser extension. This simplifies testing and deployment across diverse environments. WebTrace also includes features to manage usability testing and abstract repetitive data, thereby enhancing analysis efficiency. By simplifying participation and supporting thorough analysis, WebTrace reduces the overall costs of usability testing while improving both time and resource management.

引用次数: 0

SpaceRL — A reinforcement learning-based knowledge graph driver

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-12 DOI: 10.1016/j.softx.2025.102078

Miguel Bermudo, Daniel Ayala, Inma Hernández, David Ruiz, Miguel Toro

Knowledge Graphs are powerful data structures used by large IT companies and the scientific community alike. They aid in the representation of related information by means of nodes connected through links indicating types of relations. These graphs are used as the basis for several smart applications, such as question answering or product recommendation. However, they are built in an automated unsupervised way, which leads to gaps in information, usually in the form of missing links between related entities in the original data source, which have to be added later by completion techniques.

SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that implicitly connect two nodes, incidentally providing a reasoned explanation for the inferred new link. The generation of such agents is a complex task, even more so for a non-expert user.

SpaceRL is meant to overcome these limitations by providing a flexible set of tools designed with a wide variety of customization options, in order to adapt to different users’ needs, while also including a variety of state-of-the-art RL algorithms and several embedding models that can be combined to optimize the agents performance. Furthermore, SpaceRL offers different interfaces to make it available either locally (programmatically or via a GUI), or through an OpenAPI-compliant REST API.

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引用次数: 0

VisualCodeMOOC: A course platform for algorithms and data structures integrating a conversational agent for enhanced learning through dynamic visualizations

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-11 DOI: 10.1016/j.softx.2025.102072

Mingyuan Li , Duan Wang , Erick Purwanto , Thomas Selig , Qing Zhang , Hai-Ning Liang

The abstract nature of algorithms and data structures poses challenges for students, and the integration of visualization into comprehensive learning systems remains underexplored. This article presents VisualCodeMOOC, incorporating VisualCodeChat, a conversational agent that enhances algorithm and data structure learning through dynamic visualizations and personalized feedback. The platform effectively addresses these challenges, improving student engagement and comprehension. With instructions structuring, novel response-based algorithm visualization, exercise design, VisualCodeMOOC provides a cohesive and supportive learning environment that promotes active learning. Evaluation results demonstrate its usability, responsiveness, and educational value, confirming its potential as a promising tool for advancing computer science education.

引用次数: 0

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