High-fat feeding reprograms maternal energy metabolism and induces long-term postpartum obesity in mice.

BackgroundExcessive gestational weight gain (EGWG) closely associates with postpartum obesity. However, the causal role of EGWG in postpartum obesity has not been experimentally verified. The objective of this study was to determine whether and how EGWG causes long-term postpartum obesity.MethodsC57BL/6 mice were fed with high-fat diet during gestation (HFFDG) or control chow, then their body composition and energy metabolism were monitored after delivery.ResultsWe found that HFFDG significantly increased gestational weight gain. After delivery, adiposity of HFFDG-treated mice (Preg-HF) quickly recovered to the levels of controls. However, 3 months after parturition, Preg-HF mice started to gain significantly more body fat even with regular chow. The increase of body fat of Preg-HF mice was progressive with aging and by 9 months after delivery had increased 2-fold above the levels of controls. The expansion of white adipose tissue (WAT) of Preg-HF mice was manifested by hyperplasia in visceral fat and hypertrophy in subcutaneous fat. Preg-HF mice developed low energy expenditure and UCP1 expression in interscapular brown adipose tissue (iBAT) in later life. Although blood estrogen concentrations were similar between Preg-HF and control mice, a significant decrease in estrogen receptor α (ERα) expression and hypermethylation of the ERα promoter was detected in the fat of Preg-HF mice 9 months after delivery. Interestingly, hypermethylation of ERα promoter and low ERα expression were only detected in adipocyte progenitor cells in both iBAT and WAT of Preg-HF mice at the end of gestation.ConclusionsThese results demonstrate that HFFDG causes long-term postpartum obesity independent of early postpartum fat retention. This study also suggests that HFFDG adversely programs long-term postpartum energy metabolism by epigenetically reducing estrogen signaling in both BAT and WAT

Generalized genetic association study with samples of related individuals

Genetic association study is an essential step to discover genetic factors that are associated with a complex trait of interest. In this paper we present a novel generalized quasi-likelihood score (GQLS) test that is suitable for a study with either a quantitative trait or a binary trait. We use a logistic regression model to link the phenotypic value of the trait to the distribution of allelic frequencies. In our model, the allele frequencies are treated as a response and the trait is treated as a covariate that allows us to leave the distribution of the trait values unspecified. Simulation studies indicate that our method is generally more powerful in comparison with the family-based association test (FBAT) and controls the type I error at the desired levels. We apply our method to analyze data on Holstein cattle for an estimated breeding value phenotype, and to analyze data from the Collaborative Study of the Genetics of Alcoholism for alcohol dependence. The results show a good portion of significant SNPs and regions consistent with previous reports in the literature, and also reveal new significant SNPs and regions that are associated with the complex trait of interest.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS465 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Characterization of methane hydrate host sediments using synchrotron-computed microtomography (CMT)

This paper is not subject to U.S. copyright. The definitive version was published in Journal of Petroleum Science and Engineering 56 (2007): 136-145, doi:10.1016/j.petrol.2006.03.029.The hydrate–sediment interaction is an important aspect of gas hydrate studies that needs further examination. We describe here the applicability of the computed microtomography (CMT) technique that utilizes an intense X-ray synchrotron source to characterize sediment samples, two at various depths from the Blake Ridge area (a well-known hydrate-prone region) and one from Georges Bank, that once contained methane trapped as hydrates. Detailed results of the tomographic analysis performed on the deepest sample (667 m) from Blake Ridge are presented as 2-D and 3-D images which show several mineral constituents, the internal grain/pore microstructure, and, following segmentation into pore and grain space, a visualization of the connecting pathways through the pore-space of the sediment. Various parameters obtained from the analysis of the CMT data are presented for all three sediment samples. The micro-scale porosity values showed decreasing trend with increasing depth for all three samples that is consistent with the previously reported bulk porosity data. The 3-D morphology, pore-space pathways, porosity, and permeability values are also reported for all three samples. The application of CMT is now being expanded to the laboratory-formed samples of hydrate in sediments as well as field samples of methane hydrate bearing sediments.Research was supported in part by the US Department of Energy Contract No. DE-AC02-98CH10886 (KWJ and HF). Additional support was provided through the Laboratory Directed Research and Development (LDRD) program at Brookhaven National Laboratory to DM

Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits.

The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell-derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes

Copper Accumulation and the Effect of Chelation Treatment on Cerebral Amyloid Angiopathy Compared to Parenchymal Amyloid Plaques

Accumulation of fibrillar amyloid β-protein (Aβ) in parenchymal plaques and in blood vessels of the brain, the latter condition known as cerebral amyloid angiopathy (CAA), are hallmark pathologies of Alzheimer\u27s disease (AD) and related disorders. Cerebral amyloid deposits have been reported to accumulate various metals, most notably copper and zinc. Here we show that, in human AD, copper is preferentially accumulated in amyloid-containing brain blood vessels compared to parenchymal amyloid plaques. In light of this observation, we evaluated the effects of reducing copper levels in Tg2576 mice, a transgenic model of AD amyloid pathologies. The copper chelator, tetrathiomolybdate (TTM), was administered to twelve month old Tg2576 mice for a period of five months. Copper chelation treatment significantly reduced both CAA and parenchymal plaque load in Tg2576 mice. Further, copper chelation reduced parenchymal plaque copper content but had no effect on CAA copper levels in this model. These findings indicate that copper is associated with both CAA deposits and parenchymal amyloid plaques in humans, but less in Tg2576 mice. TTM only reduces copper levels in plaques in Tg2576 mice. Reducing copper levels in the brain may beneficially lower amyloid pathologies associated with AD

Furanodiene alters mitochondrial function in doxorubicin-resistant MCF-7 human breast cancer cells in an AMPK-dependent manner

Furanodiene is a bioactive sesquiterpene isolated from the spice-producing Curcuma wenyujin plant (Y. H. Chen and C. Ling) (C. wenyujin), which is a commonly prescribed herb used in clinical cancer therapy by modern practitioners of traditional Chinese medicine. Previously, we have shown that furanodiene inhibits breast cancer cell growth both in vitro and in vivo, however, the mechanism for this effect is not yet known. In this study, therefore, we asked (1) whether cultured breast cancer cells made resistant to the chemotherapeutic agent doxorubicin (DOX) via serial selection protocols are susceptible to furanodiene\u27s anticancer effect, and (2) whether AMP-activated protein kinase (AMPK), which is a regulator of cellular energy homeostasis in eukaryotic cells, participates in this effect. We show here (1) that doxorubicin-resistant MCF-7 (MCF-7/DOXR) cells treated with furanodiene exhibit altered mitochondrial function and reduced levels of ATP, resulting in apoptotic cell death, and (2) that AMPK is central to this effect. In these cells, furanodiene (as opposed to doxorubicin) noticeably affects the phosphorylation of AMPK and AMPK pathway intermediates, ACLY and GSK-3β, suggesting that furanodiene reduces mitochondrial function and cellular ATP levels by way of AMPK activation. Finally, we find that the cell permeable agent and AMPK inhibitor compound C (CC), abolishes furanodiene-induced anticancer activity in these MCF-7/DOXR cells, with regard to cell growth inhibition and AMPK activation; in contrast, AICAR (5-aminoimidazole-4-carboxamide-1-β-4-ribofuranoside, acadesine), an AMPK activator, augments furanodiene-induced anticancer activity. Furthermore, specific knockdown of AMPK in MCF-7/DOXR cells protects these cells from furanodiene-induced cell death. Taken together, these findings suggest that AMPK and its pathway intermediates are promising therapeutic targets for treating chemoresistant breast cancer, and that furanodiene may be an important chemical agent incorporated in next-generation chemotherapy protocols

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report

Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a benchmark for future studies to reference. Methods and Materials: The Generative Pre-trained Transformer (GPT)-4 application programming interface (API) was implemented as a Digital Imaging and Communications in Medicine (DICOM) storage server, which upon receiving a structure set DICOM file, prompts GPT-4 to re-label the structure names of both target volumes and normal tissues according to the AAPM TG-263. Three disease sites, prostate, head and neck, and thorax were selected for evaluation. For each disease site category, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50) and 50 patients were randomly selected for evaluation. Structure names that were considered were those that were most likely to be relevant for studies utilizing structure contours for many patients. Results: The overall re-labeling accuracy of both target volumes and normal tissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and 96.9% respectively. Re-labeling of target volumes was less accurate on average except for prostate - 100%, 93.1%, and 91.1% respectively. Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.Comment: 20 pages, 5 figures, 1 tabl

Development and assessment of scoring functions for protein identification using PMF data

PMF is one of the major methods for protein identification using the MS technology. It is faster and cheaper than MS/MS. Although PMF does not differentiate trypsin-digested peptides of identical mass, which makes it less informative than MS/MS, current computational methods for PMF have the potential to improve its detection accuracy by better use of the information content in PMF spectra. We developed a number of new probability-based scoring functions for PMF protein identification based on the MOWSE algorithm. We considered a detailed distribution of matching masses in a protein database and peak intensity, as well as the likelihood of peptide matches to be close to each other in a protein sequence. Our computational methods are assessed and compared with other methods using PMF data of 52 gel spots of known protein standards. The comparison shows that our new scoring schemes have higher or comparable accuracies for protein identification in comparison to the existing methods. Our software is freely available upon request. The scoring functions can be easily incorporated into other proteomics software packages

Observation of a prethermal discrete time crystal

The conventional framework for defining and understanding phases of matter requires thermodynamic equilibrium. Extensions to non-equilibrium systems have led to surprising insights into the nature of many-body thermalization and the discovery of novel phases of matter, often catalyzed by driving the system periodically. The inherent heating from such Floquet drives can be tempered by including strong disorder in the system, but this can also mask the generality of non-equilibrium phases. In this work, we utilize a trapped-ion quantum simulator to observe signatures of a non-equilibrium driven phase without disorder: the prethermal discrete time crystal (PDTC). Here, many-body heating is suppressed not by disorder-induced many-body localization, but instead via high-frequency driving, leading to an expansive time window where non-equilibrium phases can emerge. We observe a number of key features that distinguish the PDTC from its many-body-localized disordered counterpart, such as the drive-frequency control of its lifetime and the dependence of time-crystalline order on the energy density of the initial state. Floquet prethermalization is thus presented as a general strategy for creating, stabilizing and studying intrinsically out-of-equilibrium phases of matter.Comment: 9 + 10 pages, 3 + 6 figure

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure