Dendrobium nobile-derived polysaccharides stimulate the glycolytic pathway by activating SIRT2 to regulate insulin resistance in polycystic ovary syndrome granulosa cells.

Insulin resistance (IR) is one of the major complications of polycystic ovary syndrome (PCOS). This study aimed to investigate the effects and the molecular regulatory mechanism by which Dendrobium nobile-derived polysaccharides (DNP) improve IR in rats with letrozole and high-fat-diet induced PCOS. In vivo, DNP (200 mg/kg/d) administration not only reduced body weight, blood glucose, and insulin levels in PCOS rats, but also improve the disrupted estrous cycle. In addition, DNP treatment reduced atretic and cystic follicles and enhanced granulosa cell layer thickness, thereby restoring follicle development. In vitro, DNP treatment (100 µM) increased lactate levels and decreased pyruvate levels in insulin-treated (8 µg/mL) KGN cells. Additionally, DNP also decreased the expression of IGF1 and increased that of IGF1R, SIRT2, LDHA, PKM2 and HK2 both in vivo and in vitro. Also, SIRT2 expression was specifically inhibited by AGK2, while DNP significantly improved IR and glycolysis by reversing the effect of AGK2 treatment on lactate and pyruvate production, upregulating the expression levels of IGF1R, LDHA, HK2, and PKM2 and downregulating the expression level of IGF1. The results indicate that DNP can effectively improve IR and restore glycolytic pathway by activating SIRT2, which may provide a potential therapeutic approach for PCOS patients.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Proteomic analyses reveal plasma EFEMP1 and CXCL12 as biomarkers and determinants of neurodegeneration.

INTRODUCTION: Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes. METHODS: We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk. RESULTS: Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization. DISCUSSION: Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration. HIGHLIGHTS: Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

A novel fast robust optimization algorithm for intensity-modulated proton therapy with minimum monitor unit constraint.

BACKGROUND: Intensity-modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential. PURPOSE: This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint. METHODS AND MATERIALS: The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO-NEW) was compared with plans that only consider nominal optimization using CTV (NMO-CTV) without handling uncertainties and PTV (NMO-PTV) to handle the uncertainties, as well as with conventional robust-optimized plans (RBO-CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root-mean-square dose (RMSD)-volume histogram curves (AUC) and dose-volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computational efficiency. RESULTS: The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO-NEW showing superior computational efficiency and plan quality compared to RBO-CONV. Specifically, statistical analysis results indicated that RBO-NEW was able to reduce the computational time from 389.70 ± 207.40 $389.70\pm 207.40$ to 228.60 ± 123.67 $228.60\pm 123.67$ s ( p < 0.01 $p<0.01$ ) and reduce the mean organ-at-risk (OAR) dose from 9.38 ± 12.80 $9.38\pm 12.80$ % of the prescription dose to 9.07 ± 12.39 $9.07\pm 12.39$ % of the prescription dose ( p < 0.05 $p<0.05$ ) compared to RBO-CONV. CONCLUSION: This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality and reliability.

NeuroSynth: MRI-Derived Neuroanatomical Generative Models and Associated Dataset of 18,000 Samples.

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present NeuroSynth: a collection of generative models of normative regional volumetric features derived from structural brain imaging. NeuroSynth models are trained on real brain imaging regional volumetric measures from the iSTAGING consortium, which encompasses over 40,000 MRI scans across 13 studies, incorporating covariates such as age, sex, and race. Leveraging NeuroSynth, we produce and offer 18,000 synthetic samples spanning the adult lifespan (ages 22-90 years), alongside the model's capability to generate unlimited data. Experimental results indicate that samples generated from NeuroSynth agree with the distributions obtained from real data. Most importantly, the generated normative data significantly enhance the accuracy of downstream machine learning models on tasks such as disease classification. Data and models are available at: https://huggingface.co/spaces/rongguangw/neuro-synth.

Technical considerations and strategies for generating and optimizing humanized mouse tumor models in immuno-oncology research.

The field of cancer immunotherapy has experienced significant progress, resulting in the emergence of numerous biological drug candidates requiring in vivo efficacy testing and a better understanding of their mechanism of action (MOA). Humanized immune system (HIS) models are valuable tools in this regard. However, there is a lack of systematic guidance on HIS modeling. To address this issue, the present study aimed to establish and optimize a variety of HIS models for immune-oncology (IO) study, including genetically engineered mouse models and HIS models with human immune components reconstituted in severely immunocompromised mice. The efficacy and utility of these models were tested with several marketed or investigational IO drugs according to their MOA, followed by immunophenotypic analysis and efficacy evaluation. The results of the present study demonstrated that the HIS models responded to various IO drugs as expected and that each model had unique niches, utilities and limitations. Researchers should carefully choose the appropriate models based on the MOA and the targeted immune cell populations of the investigational drug. The present study provides valuable methodologies and actionable technical guidance on designing, generating or utilizing appropriate HIS models to address specific questions in translational IO.

High-yield hybrid breeding of Camellia oleifolia based on ISSR molecular markers.

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.

The genetic architecture of biological age in nine human organ systems.

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Probing Energy-Funneling Kinetics in Nanocrystal Sublattices for Superior X-Ray Imaging.

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.

Anlotinib Inhibits Ovarian Cancer and Enhances Cisplatinum Sensitivity via Suppressing NOTCH2 Expression and Stemness.

BACKGROUND/AIM: The prognosis of ovarian cancer (OC) patients is especially poor for patients with chemotherapy resistance. Anlotinib, a novel multi-targeted tyrosine kinase inhibitor, has shown encouraging clinical efficacy in several tumor types. The aim of the present study was to examine the inhibitory efficacy and mechanism of anlotinib on the proliferation and chemosensitivity of OC cells. MATERIALS AND METHODS: The inhibitory effects of Anlotinib on SKOV3 and OVCAR3 OC cells were examined using CCK-8 cell-viability, colony-formation, flow-cytometry, transwell-migration and sphere-formation assays. A xenograft mouse model was used for in vivo studies. RT-qPCR and western blotting were used to detect gene expression. RESULTS: Molecular targets of anlotinib were elevated in OC patient tumors. Anlotinib significantly inhibited ovarian cancer cell proliferation and migration in vitro. Anlotinib enhanced the sensitivity of ovarian cancer cells to cisplatinum both in vitro and in vivo. Anlotinib suppressed sphere formation and the stemness phenotype of OC cells by inhibiting NOTCH2 expression. CONCLUSION: Anlotinib inhibits ovarian cancer and enhances cisplatinum sensitivity, suggesting its future clinical promise.

The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

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 ß (Aß), 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 Aß 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.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

The Genetic Architecture of Biological Age in Nine Human Organ Systems.

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.

Palladium-Catalyzed Synthesis of Indanone via C-H Annulation Reaction of Aldehydes with Norbornenes.

A novel methodology for the synthesis of indanone derivates has been developed. The palladium-catalyzed annulation reaction of o-bromobenzaldehydes with norbornene derivatives is achieved through extremely concise reaction processes. The indanone skeleton was established directly via C-H activation of the aldehyde group under a mild reaction condition. This method is simple and practical, which simplified the traditional synthesis method for the rapid construction of indanone.

A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (SPArc-particle swarm).

Objective.Delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (ELs) ascending switches. This study aims to develop a new algorithm to mitigate the need for EL ascending via water equivalent thickness (WET) sector selection followed by particle swarm optimization (SPArc-particle swarm).Approach.SPArc-particle swarmdivided the full arc trajectory into the optimal sectors based on K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, optimizing the energy selection integrated with the EL delivery sequence and relationship. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer) were selected for testing purposes. Two kinds of plans were generated: SPArc_seq and SPArc-particle swarm. The plan quality and delivery efficiency were evaluated.Main results. With a similar plan quality, the delivery efficiency was significantly improved using SPArc-particle swarmcompared to SPArc_seq. More specifically, it reduces the number of ELs ascending switching compared to the SPArc_seq (from 21 to 7 in the brain, from 21 to 5 in the prostate, from 21 to 6 in the liver), leading to a 16%-26% reduction of the beam delivery time (BDT) in the SPArc treatment.Significance. A novel planning framework, SPArc-particle swarm, could significantly improve the delivery efficiency, which paves the roadmap towards routine clinical implementation.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

High-Confidentiality X-Ray Imaging Encryption Using Prolonged Imperceptible Radioluminescence Memory Scintillators.

X-ray imaging plays an increasingly crucial role in clinical radiography, industrial inspection, and military applications. However, current X-ray imaging technologies have difficulty in protecting against information leakage caused by brute force attacks via trial-and-error. Here high-confidentiality X-ray imaging encryption by fabricating ultralong radioluminescence memory films composed of lanthanide-activated nanoscintillators (NaLuF4 : Gd3+ or Ce3+ ) with imperceptible purely-ultraviolet (UV) emission is reported. Mechanistic investigations unveil that ultralong X-ray memory is attributed to the long-lived trapping of thermalized charge carriers within Frenkel defect states and subsequent slow release in the form of imperceptible radioluminescence. The encrypted X-ray imaging can be securely stored in the memory film for more than 7 days and optically decoded by perovskite nanocrystal. Importantly, this encryption strategy can protect X-ray imaging information against brute force trial-and-error attacks through the perception of lifetime change in the persistent radioluminescence. It is further demonstrated that the as-fabricated flexible memory film enables achieving of 3D X-ray imaging encryption of curved objects with a high spatial resolution of 20 lp/mm and excellent recyclability. This study provides valuable insights into the fundamental understanding of X-ray-to-UV conversion in nanocrystal lattices and opens up a new avenue toward the development of high-confidential 3D X-ray imaging encryption technologies.