Patterns最新文献

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers to reproduce, validate, and expand upon study findings. The analysis of open-source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc (Data Description) framework is presented-an approach to describing data models of software interfaces with machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and reusability, of research software as well as effectively promotes its impact on research.

Organoids and 3D imaging techniques are crucial for studying human tissue structure and function, but traditional 3D reconstruction methods are expensive and time consuming, relying on complete z stack confocal microscopy data. This paper introduces VONet, a deep learning-based system for 3D organoid rendering that uses a fully convolutional neural network to reconstruct entire 3D structures from a minimal number of z stack images. VONet was trained on a library of over 39,000 virtual organoids (VOs) with diverse structural features and achieved an average intersection over union of 0.82 in performance validation. Remarkably, VONet can predict the structure of deeper focal plane regions, unseen by conventional confocal microscopy. This innovative approach and VO dataset offer significant advancements in 3D bioimaging technologies.

Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed AnnoMate, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement “reviewer” dashboards for various tasks often performed in cancer genome sequencing studies.

For over 30 years, synthetic data have been heralded as a solution to make sensitive datasets accessible. However, despite much research effort, its adoption as a tool for research with sensitive data is lacking. This article argues that to make progress in this regard, the data science community should focus on improving the accessibility of existing privacy-friendly synthesis techniques.

The “stacking cell puzzle” is a data visualization project consisting of a three-dimensional puzzle made with electron microscopy data of planarian cells.

The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework, fairness-aware interpretable modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface to identify a “fairer” model from a set of high-performing models and promoting the integration of data-driven evidence and clinical expertise to enhance contextualized fairness. We demonstrate FAIM’s value in reducing intersectional biases arising from race and sex by predicting hospital admission with two real-world databases, the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) and the database collected from Singapore General Hospital Emergency Department (SGH-ED). For both datasets, FAIM models not only exhibit satisfactory discriminatory performance but also significantly mitigate biases as measured by well-established fairness metrics, outperforming commonly used bias mitigation methods. Our approach demonstrates the feasibility of improving fairness without sacrificing performance and provides a modeling mode that invites domain experts to engage, fostering a multidisciplinary effort toward tailored AI fairness.

Accurate analysis of social behaviors in animals is hindered by methodological challenges. Here, we develop a segmentation tracking and clustering system (STCS) to address two major challenges in computational neuroethology: reliable multi-animal tracking and pose estimation under complex interaction conditions and providing interpretable insights into social differences guided by genotype information. We established a comprehensive, long-term, multi-animal-tracking dataset across various experimental settings. Benchmarking STCS against state-of-the-art tracking algorithms, we demonstrated its superior efficacy in analyzing behavioral experiments and establishing a robust tracking baseline. By analyzing the behavior of mice with autism spectrum disorder (ASD) using a novel weakly supervised clustering method under both solitary and social conditions, STCS reveals potential links between social stress and motor impairments. Benefiting from its modular and web-based design, STCS allows researchers to easily integrate the latest computer vision methods, enabling comprehensive behavior analysis services over the Internet, even from a single laptop.

With the exponential rise in the prevalence of automation, trust in such technology has become more critical than ever before. Trust is confidence in a particular entity, especially in regard to the consequences they can have for the trustor, and calibrated trust is the extent to which the judgments of trust are accurate. The focus of this paper is to reevaluate the general understanding of calibrating trust in automation, update this understanding, and apply it to worker’s trust in automation in the workplace. Seminal models of trust in automation were designed for automation that was already common in workforces, where the machine’s “intelligence” (i.e., capacity for decision making, cognition, and/or understanding) was limited. Now, burgeoning automation with more human-like intelligence is intended to be more interactive with workers, serving in roles such as decision aid, assistant, or collaborative coworker. Thus, we revise “calibrated trust in automation” to include more intelligent automated systems.

Denoising diffusion probabilistic models (DDPMs) have recently been shown to accurately generate complicated data such as images, audio, or time series. Experimental and clinical neuroscience also stand to benefit from this progress, as the accurate generation of neurophysiological time series can enable or improve many neuroscientific applications. Here, we present a flexible DDPM-based method for modeling multichannel, densely sampled neurophysiological recordings. DDPMs can generate realistic synthetic data for a variety of datasets from different species and recording techniques. The generated data capture important statistics, such as frequency spectra and phase-amplitude coupling, as well as fine-grained features such as sharp wave ripples. Furthermore, data can be generated based on additional information such as experimental conditions. We demonstrate the flexibility of DDPMs in several applications, including brain-state classification and missing-data imputation. In summary, DDPMs can serve as accurate generative models of neurophysiological recordings and have broad utility in the probabilistic generation of synthetic recordings for neuroscientific applications.