Most treatments of large scale anomalies in the microwave sky are a posteriori, with unquantified look-elsewhere effects. We contrast these with physical models of specific inhomogeneities in the early Universe which can generate these apparent anomalies. Physical models predict correlations between candidate anomalies and the corresponding signals in polarization and large scale structure, reducing the impact of cosmic variance. We compute the apparent spatial curvature associated with large-scale inhomogeneities and show that it is typically small, allowing for a self-consistent analysis. As an illustrative example we show that a single large plane wave inhomogeneity can contribute to low-l mode alignment and odd-even asymmetry in the power spectra and the best-fit model accounts for a significant part of the claimed odd-even asymmetry. We argue that this approach can be generalized to provide a more quantitative assessment of potential large scale anomalies in the Universe.
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http://dx.doi.org/10.1103/PhysRevLett.111.261301 | DOI Listing |
HGG Adv
January 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Tissue Engineering and Organ Manufacturing (TEOM) Lab, Department of Biomedical Engineering, Wuhan University TaiKang Medical School (School of Basic Medical Sciences), Wuhan, 430071, China.
Liver organoids have been increasingly adopted as a critical in vitro model to study liver development and diseases. However, the pre-vascularization of liver organoids without affecting liver parenchymal specification remains a long-lasting challenge, which is essential for their application in regenerative medicine. Here, the large-scale formation of pre-vascularized human hepatobiliary organoids (vhHBOs) is presented without affecting liver epithelial specification via a novel strategy, namely nonparenchymal cell grafting (NCG).
View Article and Find Full Text PDFBrain Topogr
January 2025
Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
Aberrant large-scale resting-state functional connectivity (rsFC) has been frequently documented in ischemic stroke. However, it remains unclear about the altered patterns of within- and across-network connectivity. The purpose of this meta-analysis was to identify the altered rsFC in patients with ischemic stroke relative to healthy controls, as well as to reveal longitudinal changes of network dysfunctions across acute, subacute, and chronic phases.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment.
View Article and Find Full Text PDFSci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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