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Learning Landscapes (LLs) are family-friendly structures that transform community spaces (e.g., parks and bus stops) into engaging activities. The community structures are designed to encourage children to explore their environment while fostering developmentally supportive interactions (DSI) and learning opportunities between caregivers and children, including children with disabilities. Playful learning provided through LLs could result in developmental gains for children from lower-income backgrounds. The purpose of this multiple methods study, conducted in the US, was to examine 10 caregivers' perceptions and play interactions with their children while at one outdoor LL. Observational data revealed high caregiver-child engagement, and interview data indicated that caregivers perceived improved positive interactions during LL activities. The LL also promoted caregivers' knowledge of child development and impacted some participants' perceptions of generalized strategy use. Taken together, environmental prompts, such as those within LLs, can promote DSI in everyday environments, offering opportunities to boost children's early development.
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http://dx.doi.org/10.1002/imhj.70034 | DOI Listing |
Adv Sci (Weinh)
September 2025
School of Artificial Intelligence, Jilin University, Changchun, 130012, China.
Single-cell multi-omics technologies are pivotal for deciphering the complexities of biological systems, with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) emerging as a particularly valuable approach. The dual-modality capability makes CITE-seq particularly advantageous for dissecting cellular heterogeneity and understanding the dynamic interplay between transcriptomic and proteomic landscapes. However, existing computational models for integrating these two modalities often struggle to capture the complex, non-linear interactions between RNA and antibody-derived tags (ADTs), and are computationally intensive.
View Article and Find Full Text PDFSci Total Environ
September 2025
Department of Environmental Sciences, Louisiana State University, Baton Rouge, LA, USA. Electronic address:
Water quality ecosystem service (ES) modeling tools help inform freshwater management across landscapes. However, the validity of such models depends on the availability of water quality data for validation and calibration, limiting their application in regions where monitoring is limited. This study presents a methodological framework that combines machine learning (ML) and spatial extrapolation to enhance ES modeling in data-scarce contexts (https://github.
View Article and Find Full Text PDFEngineered luciferases have transformed biological imaging and sensing, yet optimizing NanoLuc luciferase (NLuc) remains challenging due to the inherent stability-activity trade-off and its limited sequence homology with characterized proteins. We report a hybrid approach that synergistically integrates computational deep learning with structure-guided rational design to develop enhanced NLuc variants that improve thermostability and thereby activity at elevated temperatures. By systematically analyzing libraries of engineered variants, we established that modifications to termini and loops distal from the catalytic center, combined with preservation of allosterically coupled networks, effectively enhance thermal resilience while maintaining enzymatic function.
View Article and Find Full Text PDFEnviron Manage
September 2025
Department of Environmental Science, Rhodes University, Makhanda (Grahamstown), South Africa.
Adaptive management has long been advocated as a framework of choice for addressing the complexities and uncertainties of natural resource management. Despite its theoretical appeal, successful implementation remains elusive, with many documented barriers and limited operational examples. This paper examines Strategic Adaptive Management (SAM), a long-running adaptive management program originating from the Kruger National Park in South Africa.
View Article and Find Full Text PDFChem Sci
August 2025
Xiangya School of Pharmaceutical Sciences, Central South University Changsha 410013 Hunan P.R. China
Structure-based molecular docking, a cornerstone of computational drug design, is undergoing a paradigm shift fueled by deep learning (DL) innovations. However, the rapid proliferation of DL-driven docking methods has created uncharted challenges in translating predictions to biomedical reality. Here, we delve into the performance and prospects of traditional methods and state-of-the-art DL docking paradigms-encompassing generative diffusion models, regression-based architectures, and hybrid frameworks-across five critical dimensions: pose prediction accuracy, physical plausibility, interaction recovery, virtual screening (VS) efficacy, and generalization across diverse protein-ligand landscapes.
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