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MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.
J Chem Inf Model
January 2025
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.
View Article and Find Full Text PDFSelf-care interventions for legal and safe abortions: lessons learned from a woman-centered approach to sexual and reproductive healthcare in Uruguay.
Lancet Reg Health Am
February 2025
Department of Sexual and Reproductive Health and Research, Including the UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland.
Problem: In the 1990s, almost 40% of maternal deaths in Uruguay were caused by unsafe abortions.
Approach: A harm reduction model implemented in Uruguay, which addressed the risks associated with unsafe abortion practices by promoting and supporting the self-management of medical abortions by women in their homes, encouraged women's autonomy.
Local Setting: Since 2005, an accelerated decrease in maternal mortality has been recorded in Uruguay, coinciding with the implementation of two major actions: a harm reduction approach with active promotion of self-care through self-management of medical abortions; and in 2012, a change in legislation, which made abortion legal within sexual and reproductive health facilities when requested by women up to 12 weeks of pregnancy or later for specific indications.
LEHP-DETR: A model with backbone improved and hybrid encoding innovated for flax capsule detection.
iScience
January 2025
Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730000, China.
Flax, as a functional crop with rich essential fatty acids and nutrients, is important in nutrition and industrial applications. However, the current process of flax seed detection relies mainly on manual operation, which is not only inefficient but also prone to error. The development of computer vision and deep learning techniques offers a new way to solve this problem.
View Article and Find Full Text PDFSocial learning preserves both useful and useless theories by canalizing learners' exploration.
Proc Biol Sci
January 2025
Human Behaviour and Cultural Evolution Group, Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK.
In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials.
View Article and Find Full Text PDFHyperspectral images (HSI) have been extensively applied in a multitude of domains, due to their combined spatial and spectral characteristics along with a wealth of spectral bands. The ingenious combination of spatial and spectral information in HSI classification has remained a central research area for an extended period. In the classification process, it is essential to choose an expanded neighborhood window for learning.
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