Associations between Selected Dietary Factors, Selected Obesity-Related Metabolic Markers (Leptin, C-peptide, and High-sensitivity C-reactive Protein), and Lung Cancer: A Matched Case-Control Study Nested in the Prospective PLCO Trial

The purpose of the study was to evaluate the associations between selected dietary factors, body mass index (BMI), selected obesity-related metabolic markers, and lung cancer risk as well as histological types in ever-smokers (former and current-smokers). Characteristics of interest included BMI at age 50, fruits and vegetables daily frequency, supplemental beta-carotene intake, C-peptide (CP), high-sensitivity C-reactive protein (hsCRP), and leptin concentrations. Data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial were analyzed. Linear regression models were used to describe the associations between quantitative variables. The relationships between variables of interest and lung cancer were studied by logistic regression modelling. Multivariable fractional polynomial (MFP) models were utilized to address non-linearity in these associations. Higher fruits and vegetables daily frequency and supplemental beta-carotene intake were associated with a lower risk of lung cancer in ever-smokers. Metabolic markers, C-peptide and hsCRP, were positively associated with lung cancer risk. Inverse relationships were observed between BMI and leptin with lung cancer risk. The relationships between selected dietary factors, BMI, selected metabolic markers, and lung cancer risk were more prominent in non-small cell lung cancer (NSCLC) in comparison with those in small cell lung cancer (SCLC)

Forest fertilization does not cause any long-term effects on tree growth or vegetation composition

Forest fertilization is considered one of the most effective management options to improve forest productivity. In Fennoscandia, nitrogen (N) fertilization has been practiced for over 50 years and is normally added in the form of granules of NH4NO3 at least 15 years prior to the final harvest. In addition to the intended effect of increasing tree productivity, N addition will induce changes in other parts of the forest ecosystem. Such effects include changes in the community composition of plants, insects, and soil fungi. Although many of these effects appear short-lived there are concerns that forest fertilization will induce long-term changes in soil nutrients and associated plant productivity. This study aims to investigate the long-term residual effects of commercial forest fertilization on tree growth and vegetation composition. The experiment was conducted in two regions of Sweden: the northeastern area of Uppsala and the Skinnskatteberg municipalities. It involves 36 forest stands, with half previously fertilized and the other half were unfertilized during the previous forest rotation. It is worth mentioning that these stands were prior to this study clearcut (5-14 years ago). The two forets types were used to evaluate effects of past fertilization. Most data were collected in August 2022, including tree heights, collection of trees cross-sections, vegetation records, and soil samples collection. However, the analysis of the soil data and stem radial growth measurements were conducted at the beginning of 2023. Although tree density, soil N and C/N ratio could affect tree stem radial growth and C/N ratio could influence tree height and density, past fertilization did not affect the radial and vertical growth of trees. Further, there were no differences between fertilized and unfertilized stands in terms of soil N, C/N ratio or on ground vegetation composition. These results suggest that the long-term effects of N fertilization are small or absent, with no major long-term effects on soil N content or vegetation distribution and productivity. The results highlight the resilience and sustainability of forests and provides useful insights for the rational use of fertilization and conservation of forest ecosystems. The forest manager should focus on planning and nutrient management to better balance forest development and promote more sustainable forestry practices

Age-Associated Loss of Lamin-B Leads to Systemic Inflammation and Gut Hyperplasia

SummaryAging of immune organs, termed as immunosenescence, is suspected to promote systemic inflammation and age-associated disease. The cause of immunosenescence and how it promotes disease, however, has remained unclear. We report that the Drosophila fat body, a major immune organ, undergoes immunosenescence and mounts strong systemic inflammation that leads to deregulation of immune deficiency (IMD) signaling in the midgut of old animals. Inflamed old fat bodies secrete circulating peptidoglycan recognition proteins that repress IMD activity in the midgut, thereby promoting gut hyperplasia. Further, fat body immunosenecence is caused by age-associated lamin-B reduction specifically in fat body cells, which then contributes to heterochromatin loss and derepression of genes involved in immune responses. As lamin-associated heterochromatin domains are enriched for genes involved in immune response in both Drosophila and mammalian cells, our findings may provide insights into the cause and consequence of immunosenescence during mammalian aging.PaperFlic

Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation

Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable tensor learning model -- Low-Tubal-Rank Smoothing Tensor Completion (LSTC-Tubal) -- based on the existing framework of Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic data that is characterized by multidimensional structure of location$\times$ time of day $\times$ day. In particular, the proposed LSTC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. We compare LSTC-Tubal with state-of-the-art baseline models, and find that LSTC-Tubal can achieve competitive accuracy with a significantly lower computational cost. In addition, the LSTC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting