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PubMed2025

DiCARN-DNase: enhancing cell-to-cell Hi-C resolution using dilated cascading ResNet with self-attention and DNase-seq chromatin accessibility data.

MOTIVATION: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution (HR) Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict HR Hi-C data from low-resolution counterparts. Early Convolutional Neural Network (CNN)-based models improved resolution but struggled with issues like blurring and capturing fine details. In contrast, Generative Adversarial Network (GAN)-based methods encountered difficulties in maintaining diversity and generalization. Additionally, most existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance HR data in another cell type. RESULTS: In this work, we propose Dilated Cascading Residual Network (DiCARN) to overcome these challenges and improve Hi-C data resolution. DiCARN leverages dilated convolutions and cascading residuals to capture a broader context while preserving fine-grained genomic interactions. Additionally, we incorporate DNase-seq data into our model, providing a robust framework that demonstrates superior generalizability across cell lines in HR Hi-C data reconstruction. AVAILABILITY AND IMPLEMENTATION: DiCARN is publicly available at https://github.com/OluwadareLab/DiCARN.

Chromatin↗
PubMedDate not supplied

Informatics in radiology (infoRAD): benefits of content-based visual data access in radiology.

The field of medicine is often cited as an area for which content-based visual retrieval holds considerable promise. To date, very few visual image retrieval systems have been used in clinical practice; the first applications of image retrieval systems in medicine are currently being developed to complement conventional text-based searches. An image retrieval system was developed and integrated into a radiology teaching file system, and the performance of the retrieval system was evaluated, with use of query topics that represent the teaching database well, against a standard of reference generated by a radiologist. The results of this evaluation indicate that content-based image retrieval has the potential to become an important technology for the field of radiology, not only in research, but in teaching and diagnostics as well. However, acceptance of this technology in the clinical domain will require identification and implementation of clinical applications that use content-based access mechanisms, necessitating close cooperation between medical practitioners and medical computer scientists. Nevertheless, content-based image retrieval has the potential to become an important technology for radiology practice.

Humans↗
PubMed1998

The Brazilian cohort of pulp and paper workers: the logistic of a cancer mortality study.

The International Agency for Research on Cancer (IARC) proposed this international historical cohort study trying to solve the controversy about the increased risk of cancer in the workers of the Pulp and Paper Industry. One of the most important aspects presented by this study in Brazil was the strategies used to overcome the methodological challenges, such as: data access, data accuracy, data availability, multiple data sources, and the large follow-up period. Through multiple strategies it was possible to build a Brazilian cohort of 3,622 workers, to follow them with a 93 percent success rate and to identify in 99 percent of the cases the cause of death. This paper, has evaluated the data access, data accuracy and the effectiveness of the strategies used and the different sources of data.

Brazil↗
PubMed2001

The palm as a real-time wide-area data-access device.

Handheld wireless technologies offer great promise in helping to improve healthcare. However, it is not clear whether off-the-shelf wireless networking will work as well within medical centers as this technology works outside of the medical center. Therefore, we evaluated the coverage of wide-area wireless technology within two representative academic medical centers. The study determined the rate of connectivity by testing both the Palm VII and the Minstrel V modem in a set of locations typically frequented by house staff in their daily activities. Within one hospital, connectivity was 59% for OmniSky service, and 78% for Palm.net. The second hospital's connectivity was over 93% with both devices. Differences in connectivity were likely due to the number of rooms visited with externally exposed walls, the suburban versus urban location of the academic medical center, and the relative location of service transponders. When examined by the Johns Hopkins Clinical Devices Laboratory, both devices were found to operate without interfering with other hospital equipment.

Academic Medical Centers↗