Quantum Effects in Black Hole Interiors

The Weyl curvature inside a black hole formed in a generic collapse grows, classically without bound, near to the inner horizon, due to partial absorption and blueshifting of the radiative tail of the collapse. Using a spherical model, we examine how this growth is modified by quantum effects of conformally coupled massless fields.Comment: 13 pages, 1 figure (not included), RevTe

Predicting future coexistence in a North American ant community

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The rate of leukocyte telomere shortening predicts mortality from cardiovascular disease in elderly men

Telomere length (TL) has been proposed as a marker of mitotic cell age and as a general index of human organismic aging. Short absolute leukocyte telomere length has been linked to cardiovascular-related morbidity and mortality. Our aim was to test whether the rate of change in leukocyte TL is related to mortality in a healthy elderly cohort. We examined a subsample of 236 randomly selected Caucasian participants from the MacArthur Health Aging Study (aged 70 to 79 years). DNA samples from baseline and 2.5 years later were assayed for mean TL of leukocytes. Percent change in TL was calculated as a measure of TL change (TLC). Associations between TL and TLC with 12-year overall and cardiovascular mortality were assessed. Over the 2.5 year period, 46% of the study participants showed maintenance of mean bulk TL, whereas 30% showed telomere shortening, and, unexpectedly, 24% showed telomere lengthening. For women, short baseline TL was related to greater mortality from cardiovascular disease (OR = 2.3; 95% CI: 1.0 - 5.3). For men, TLC (specifically shortening), but not baseline TL, was related to greater cardiovascular mortality, OR = 3.0 (95% CI: 1.1 - 8.2). This is the first demonstration that rate of telomere length change (TLC) predicts mortality and thus may be a useful prognostic factor for longevity

Artificial Intelligence in Fetal Resting-State Functional MRI Brain Segmentation: A Comparative Analysis of 3D UNet, VNet, and HighRes-Net Models

Introduction: Fetal resting-state functional magnetic resonance imaging (rs-fMRI) is a rapidly evolving field that provides valuable insight into brain development before birth. Accurate segmentation of the fetal brain from the surrounding tissue in nonstationary 3D brain volumes poses a significant challenge in this domain. Current available tools have 0.15 accuracy. Aim: This study introduced a novel application of artificial intelligence (AI) for automated brain segmentation in fetal brain fMRI, magnetic resonance imaging (fMRI). Open datasets were employed to train AI models, assess their performance, and analyze their capabilities and limitations in addressing the specific challenges associated with fetal brain fMRI segmentation. Method: We utilized an open-source fetal functional MRI (fMRI) dataset consisting of 160 cases (reference: fetal-fMRI - OpenNeuro). An AI model for fMRI segmentation was developed using a 5-fold cross-validation methodology. Three AI models were employed: 3D UNet, VNet, and HighResNet. Optuna, an automated hyperparameter-tuning tool, was used to optimize these models. Results and Discussion: The Dice scores of the three AI models (VNet, UNet, and HighRes-net) were compared, including a comparison between manually tuned and automatically tuned models using Optuna. Our findings shed light on the performance of different AI models for fetal resting-state fMRI brain segmentation. Although the VNet model showed promise in this application, further investigation is required to fully explore the potential and limitations of each model, including the HighRes-net model. This study serves as a foundation for further extensive research into the applications of AI in fetal brain fMRI segmentation

Hematopoietic growth factor inducible neurokinin-1 (Gpnmb/Osteoactivin) is a biomarker of progressive renal injury across species

We sought to find a urinary biomarker for chronic kidney disease and tested hematopoietic growth factor inducible neurokinin-1 (HGFIN, also known as Gpnmb/Osteoactivin) as it was found to be a kidney injury biomarker in microarray studies. Here, we studied whether HGFIN is a marker of kidney disease progression. Its increase in kidney disease was confirmed by real-time PCR after 5/6 nephrectomy, in streptozotocin-induced diabetes, and in patients with chronic kidney disease. In the remnant kidney, HGFIN mRNA increased over time reflecting lesion chronicity. HGFIN was identified in the infarct portion of the remnant kidney in infiltrating hematopoietic interstitial cells, and in distal nephron tubules of the viable remnant kidney expressed de novo with increasing time. In vitro, it localized to cytoplasmic vesicles and cell membranes. Epithelial cells lining distal tubules and sloughed luminal tubule cells of patients expressed HGFIN protein. The urine HGFIN-to-creatinine ratio increased over time after 5/6 nephrectomy; increased in patients with proteinuric and polycystic kidney disease; and remained detectable in urine after prolonged freezer storage. The urine HGFIN-to-creatinine ratio compared favorably with the urine neutrophil gelatinase-associated lipocalin (NGAL)-to-creatinine ratio (both measured by commercial enzyme-linked immunosorbent assays (ELISAs)), and correlated strongly with proteinuria, but weakly with estimated glomerular filtration rate and serum creatinine. Thus, HGFIN may be a biomarker of progressive kidney disease