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Long‐lasting radioluminescence scintillators have recently attracted substantial attention from both research and industrial communities, primarily due to their distinctive capabilities of converting and storing X‐ray energy. However, determination of energy‐conversion kinetics in these nanocrystals remains unexplored. Here we present a strategy to probe and unveil energy‐funneling kinetics in NaLuF4:Mn2+/Gd3+ nanocrystal sublattices through Gd3+‐driven microenvironment engineering and Mn2+‐mediated radioluminescence profiling. Our photophysical studies reveal effective control of energy‐funneling kinetics and demonstrate the tunability of electron trap depth ranging from 0.66 to 0.96 eV, with the corresponding trap density varying between 2.38×105 and 1.34×107 cm−3. This enables controlled release of captured electrons over durations spanning from seconds to 30 days. It allows tailorable emission wavelength within the range of 520–580 nm and fine‐tuning of thermally‐stimulated temperature between 313–403 K. We further utilize these scintillators to fabricate high‐density, large‐area scintillation screens that exhibit a 6‐fold improvement in X‐ray sensitivity, 22 lp/mm high‐resolution X‐ray imaging, and a 30‐day‐long optical memory. This enables high‐contrast imaging of injured mice through fast thermally‐stimulated radioluminescence readout. These findings offer new insights into the correlation of radioluminescence dynamics with energy‐funneling kinetics, thereby contributing to the advancement of high‐energy nanophotonic applications. [ABSTRACT FROM AUTHOR]

Background: C. Oleifera is among the world's largest four woody plants known for their edible oil production, yet the contribution rate of improved varieties is less than 20%. The species traditional breeding is lengthy cycle (20–30 years), occupation of land resources, high labor cost, and low accuracy and efficiency, which can be enhanced by molecular marker-assisted selection. However, the lack of high-quality molecular markers hinders the species genetic analysis and molecular breeding. Results: Through quantitative traits characterization, genetic diversity assessment, and association studies, we generated a selection population with wide genetic diversity, and identified five excellent high-yield parental combinations associated with four reliable high-yield ISSR markers. Early selection criteria were determined based on kernel fresh weight and cultivated 1-year seedling height, aided by the identification of these 4 ISSR markers. Specific assignment of selected individuals as paternal and maternal parents was made to capitalize on their unique attributes. Conclusions: Our results indicated that molecular markers-assisted breeding can effectively shorten, enhance selection accuracy and efficiency and facilitate the development of a new breeding system for C. oleifera. [ABSTRACT FROM AUTHOR]

In this experimental study, we use a data-driven machine learning framework based on genetic programing (GP) to discover model-free control laws (individuals) for suppressing self-excited thermoacoustic oscillations in a prototypical laminar combustor. This GP framework relies on an evolutionary algorithm to make decisions based on natural selection. Starting from an initial generation of individuals, we rank their performance based on a cost function that accounts for the trade-off between the state cost (thermoacoustic amplitude) and the input cost (actuator power). We then breed subsequent generations of individuals via a tournament in which the direct forwarding of elite individuals occurs alongside genetic operations such as mutation, replication, and crossover. We implement this GP control framework in both closed-loop and open-loop forms, followed by benchmarking against conventional open-loop control based on time-periodic forcing. We find that while all three control strategies can achieve similarly large reductions in thermoacoustic amplitude, GP closed-loop control consumes the least actuator power, making it the most efficient. It achieves this efficiency by learning an actuation mechanism that exploits the strong heat-release-rate amplification of the open flame at its preferred mode, even though the GP algorithm has never seen the open flame itself. This study demonstrates the feasibility of using GP to discover new and more efficient model-free individuals for suppressing self-excited thermoacoustic oscillations, providing a promising approach to data-driven feedback control of combustion devices. [ABSTRACT FROM AUTHOR]

This cohort study investigates the patterns of morphological brain changes that are reproducibly detectable with artificial intelligence (AI) in cognitively unimpaired populations and their genetic, clinical, lifestyle, and cognitive features. Key Points: Question: What patterns of morphological brain changes are reproducibly detectable in cognitively unimpaired populations, and what are their genetic, clinical, lifestyle, and cognitive features? Findings: In this multistudy harmonized cohort of 27 402 individuals aged 45 to 85 years without diagnosed cognitive impairment, 3 subgroups of structural brain measures in decade-spanning groups in a data-driven manner were found: 1 typical and 2 accelerated aging subgroups, displaying distinct associations with genetics, cognitive decline, cardiovascular risk factors, and amyloid pathology. Meaning: Three genetically distinct and longitudinally stable subgroups display brain changes reflecting differential susceptibility to Alzheimer disease and other neurodegenerative diseases, cognitive decline, and clinical progression. Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease–related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = −0.07 [0.01]; P value = 2.31 × 10−9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10−9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10−15 and rs72932727: mean [SD] B = −0.09 [0.02]; P value = 4.05 × 10−7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10−12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10−7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care. [ABSTRACT FROM AUTHOR]