IL‐4 induces proliferation in prostate cancer PC3 cells under nutrient‐depletion stress through the activation of the JNK‐pathway and survivin up‐regulation

Interleukin (IL)‐4 plays a critical role in the regulation of immune responses and has been detected at high levels in the tumor microenvironment of cancer patients where it correlates with the grade of malignancy. The direct effect of IL‐4 on cancer cells has been associated with increased cell survival; however, its role in cancer cell proliferation and related mechanisms is still unclear. Here it was shown that in a nutrient‐depleted environment, IL‐4 induces proliferation in prostate cancer PC3 cells. In these cells, under nutrient‐depletion stress, IL‐4 activates mitogen‐activated protein kinases (MAPKs), including Erk, p38, and JNK. Using MAP‐signaling‐specific inhibitors, it was shown that IL‐4‐induced proliferation is mediated by JNK activation. In fact, JNK‐inhibitor‐V (JNKi‐V) stunted IL‐4‐mediated cell proliferation. Furthermore, it was found that IL‐4 induces survivin up‐regulation in nutrient‐depleted cancer cells. Using survivin‐short‐hairpin‐RNAs (shRNAs), it was demonstrated that in this milieu survivin expression above a threshold limit is critical to the mechanism of IL‐4‐mediated proliferation. In addition, the significance of survivin up‐regulation in a stressed environment was assessed in prostate cancer mouse xenografts. It was found that survivin knockdown decreases tumor progression in correlation with cancer cell proliferation. Furthermore, under nutrient depletion stress, IL ‐4 could induce proliferation in cancer cells from multiple origins: MDA‐MB‐231 (breast), A253 (head and neck), and SKOV‐3 (ovarian). Overall, these findings suggest that in a tumor microenvironment under stress conditions, IL‐4 triggers a simultaneous activation of the JNK‐pathway and the up‐regulation of survivin turning on a cancer proliferation mechanism. J. Cell. Biochem. 113: 1569–1580, 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90542/1/24025_ftp.pd

Spatially varying selection between habitats drives physiological shifts and local adaptation in a broadcast spawning coral on a remote atoll in Western Australia

At the Rowley Shoals in Western Australia, the prominent reef flat becomes exposed on low tide and the stagnant water in the shallow atoll lagoons heats up, creating a natural laboratory for characterizing the mechanisms of coral resilience to climate change. To explore these mechanisms in the reef coral Acropora tenuis, we collected samples from lagoon and reef slope habitats and combined whole-genome sequencing, ITS2 metabarcoding, experimental heat stress, and transcriptomics. Despite high gene flow across the atoll, we identified clear shifts in allele frequencies between habitats at relatively small linked genomic islands. Common garden heat stress assays showed corals from the lagoon to be more resistant to bleaching, and RNA sequencing revealed marked differences in baseline levels of gene expression between habitats. Our results provide new insight into the complex mechanisms of coral resilience to climate change and highlight the potential for spatially varying selection across complex coral reef seascapes to drive pronounced ecological divergence in climate-related traits

Deep Learning-Based Dose Prediction To Improve the Plan Quality of Volumetric Modulated Arc Therapy for Gynecologic Cancers

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1% and D98% were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization

The fastest stars in the Galaxy

We report a spectroscopic search for hypervelocity white dwarfs (WDs) that are runaways from Type Ia supernovae (SNe Ia) and related thermonuclear explosions. Candidates are selected from Gaia data with high tangential velocities and blue colors. We find six new runaways, including four stars with radial velocities (RVs) $>1000\,\rm km\,s^{-1}$ and total space velocities $\gtrsim 1300\,\rm km\,s^{-1}$. These are most likely the surviving donors from double-degenerate binaries in which the other WD exploded. The other two objects have lower minimum velocities, $\gtrsim 600\,\rm km\,s^{-1}$, and may have formed through a different mechanism, such as pure deflagration of a WD in a Type Iax supernova. The four fastest stars are hotter and smaller than the previously known "D$^6$ stars," with effective temperatures ranging from $\sim$20,000 to $\sim$130,000 K and radii of $\sim 0.02-0.10\,R_{\odot}$. Three of these have carbon-dominated atmospheres, and one has a helium-dominated atmosphere. Two stars have RVs of $-1694$ and $-2285\rm \,km\,s^{-1}$ -- the fastest systemic stellar RVs ever measured. Their inferred birth velocities, $\sim 2200-2500\,\rm km\,s^{-1}$, imply that both WDs in the progenitor binary had masses $>1.0\,M_{\odot}$. The high observed velocities suggest that a dominant fraction of the observed hypervelocity WD population comes from double-degenerate binaries whose total mass significantly exceeds the Chandrasekhar limit. However, the two nearest and faintest D$^6$ stars have the lowest velocities and masses, suggesting that observational selection effects favor rarer, higher-mass stars. A significant population of fainter low-mass runaways may still await discovery. We infer a birth rate of D$^6$ stars that is consistent with the SN Ia rate. The birth rate is poorly constrained, however, because the luminosities and lifetimes of $\rm D^6$ stars are uncertain.Comment: 26 pages, 17 figures. Accepted to OJ

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality

Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci

Glaucoma, a leading cause of irreversible blindness, is a highly heritable human disease. Previous genome-wide association studies have identified over 100 loci for the most common form, primary open-angle glaucoma. Two key glaucoma-associated traits also show high heritability: intraocular pressure and optic nerve head excavation damage quantified as the vertical cup-to-disc ratio. Here, since much of glaucoma heritability remains unexplained, we conducted a large-scale multitrait genome-wide association study in participants of European ancestry combining primary open-angle glaucoma and its two associated traits (total sample size over 600,000) to substantially improve genetic discovery power (263 loci). We further increased our power by then employing a multiancestry approach, which increased the number of independent risk loci to 312, with the vast majority replicating in a large independent cohort from 23andMe, Inc. (total sample size over 2.8 million; 296 loci replicated at P < 0.05, 240 after Bonferroni correction). Leveraging multiomics datasets, we identified many potential druggable genes, including neuro-protection targets likely to act via the optic nerve, a key advance for glaucoma because all existing drugs only target intraocular pressure. We further used Mendelian randomization and genetic correlation-based approaches to identify novel links to other complex traits, including immune-related diseases such as multiple sclerosis and systemic lupus erythematosus

A rotating white dwarf shows different compositions on its opposite faces

White dwarfs, the extremely dense remnants left behind by most stars after their death, are characterised by a mass comparable to that of the Sun compressed into the size of an Earth-like planet. In the resulting strong gravity, heavy elements sink toward the centre and the upper layer of the atmosphere contains only the lightest element present, usually hydrogen or helium. Several mechanisms compete with gravitational settling to change a white dwarf's surface composition as it cools, and the fraction of white dwarfs with helium atmospheres is known to increase by a factor ~2.5 below a temperature of about 30,000 K; therefore, some white dwarfs that appear to have hydrogen-dominated atmospheres above 30,000 K are bound to transition to be helium-dominated as they cool below it. Here we report observations of ZTF J203349.8+322901.1, a transitioning white dwarf with two faces: one side of its atmosphere is dominated by hydrogen and the other one by helium. This peculiar nature is likely caused by the presence of a small magnetic field, which creates an inhomogeneity in temperature, pressure or mixing strength over the surface. ZTF J203349.8+322901.1 might be the most extreme member of a class of magnetic, transitioning white dwarfs -- together with GD 323, a white dwarf that shows similar but much more subtle variations. This new class could help shed light on the physical mechanisms behind white dwarf spectral evolution.Comment: 45 pages, 11 figure

Investigating the Viral Ecology of Global Bee Communities with High-Throughput Metagenomics

Bee viral ecology is a fascinating emerging area of research: viruses exert a range of effects on their hosts, exacerbate the impacts of other environmental stressors, and, importantly, are readily shared across multiple bee species in a community. However, our understanding of bee viral communities is limited, as it is primarily derived from studies of North American and European Apis mellifera populations. Here, we examined viruses in populations of A. mellifera and 11 other bee species from 9 countries, across 5 continents and Oceania. We developed a novel pipeline to rapidly, inexpensively, and robustly screen for bee viruses. This pipeline includes purification of encapsulated RNA/DNA viruses, sequence-independent amplification, high throughput sequencing, integrated assembly of contigs, and filtering to identify contigs specifically corresponding to viral sequences. We identified sequences corresponding to (+)ssRNA, (-)ssRNA, dsRNA, and ssDNA viruses. Overall, we found 127 contigs corresponding to novel viruses (i.e. previously not observed in bees), with 29 represented by \u3e0.1% of the reads in a given sample. These viruses and viral families were distributed across multiple regions and species. This study provides a robust pipeline for metagenomics analysis of viruses, and greatly expands our understanding of the diversity of viruses found in bee communities

A Professional Ethics for Researchers?

Historical research has shown that, at its inception, research ethics was conceived as distinct from existing discourses of professional ethics. Subsequently, this distinction has been maintained and, as a result, the discourse of research ethics appears to be an external to and independent of the practices it normatively analyses and comments upon. This chapter challenges these founding preconceptions and considers if research ethics can be understood as a professional ethics. Therefore, this chapter examines the criteria sociological research identifies as constitutive of a profession, and while one might conclude that research is obviously not formally instantiated as a profession, some of the sociological criteria have significant relevance. In this light it is argued that we might rethink the notion of research ethics in terms of a professional ethics. To do so would be to more clearly embed ethical discourse in the practice(s) of research, something that is consistent with the current turn to integrity

Osteoclast Activated FoxP3+ CD8+ T-Cells Suppress Bone Resorption in vitro

BACKGROUND: Osteoclasts are the body's sole bone resorbing cells. Cytokines produced by pro-inflammatory effector T-cells (T(EFF)) increase bone resorption by osteoclasts. Prolonged exposure to the T(EFF) produced cytokines leads to bone erosion diseases such as osteoporosis and rheumatoid arthritis. The crosstalk between T-cells and osteoclasts has been termed osteoimmunology. We have previously shown that under non-inflammatory conditions, murine osteoclasts can recruit naïve CD8 T-cells and activate these T-cells to induce CD25 and FoxP3 (Tc(REG)). The activation of CD8 T-cells by osteoclasts also induced the cytokines IL-2, IL-6, IL-10 and IFN-γ. Individually, these cytokines can activate or suppress osteoclast resorption. PRINCIPAL FINDINGS: To determine the net effect of Tc(REG) on osteoclast activity we used a number of in vitro assays. We found that Tc(REG) can potently and directly suppress bone resorption by osteoclasts. Tc(REG) could suppress osteoclast differentiation and resorption by mature osteoclasts, but did not affect their survival. Additionally, we showed that Tc(REG) suppress cytoskeletal reorganization in mature osteoclasts. Whereas induction of Tc(REG) by osteoclasts is antigen-dependent, suppression of osteoclasts by Tc(REG) does not require antigen or re-stimulation. We demonstrated that antibody blockade of IL-6, IL-10 or IFN-γ relieved suppression. The suppression did not require direct contact between the Tc(REG) and osteoclasts. SIGNIFICANCE: We have determined that osteoclast-induced Tc(REG) can suppress osteoclast activity, forming a negative feedback system. As the CD8 T-cells are activated in the absence of inflammatory signals, these observations suggest that this regulatory loop may play a role in regulating skeletal homeostasis. Our results provide the first documentation of suppression of osteoclast activity by CD8 regulatory T-cells and thus, extend the purview of osteoimmunology