Nondestructive Depth Profiling of the Protective Coating on a Turbine Blade

Turbine blades in an aircraft engine are typically made of a nickel-based alloy, and covered with a protective coating to increase oxidation and hot corrosion resistance. The coating is on the order of 50–100 μm thick. There is currently no nondestructive method available to verify that the blade coating thickness is within specifications, or that the proper interfacial boundary has been set up between the coating and base alloy.</p

Earliest Holocene south Greenland ice sheet retreat within its late Holocene extent

Early Holocene summer warmth drove dramatic Greenland ice sheet (GIS) retreat. Subsequent insolation-driven cooling caused GIS margin readvance to late Holocene maxima, from which ice margins are now retreating. We use 10Be surface exposure ages from four locations between 69.4°N and 61.2°N to date when in the early Holocene south to west GIS margins retreated to within these late Holocene maximum extents. We find that this occurred at 11.1 ± 0.2 ka to 10.6 ± 0.5 ka in south Greenland, significantly earlier than previous estimates, and 6.8 ± 0.1 ka to 7.9 ± 0.1 ka in southwest to west Greenland, consistent with existing 10Be ages. At least in south Greenland, these 10Be ages likely provide a minimum constraint for when on a multicentury timescale summer temperatures after the last deglaciation warmed above late Holocene temperatures in the early Holocene. Current south Greenland ice margin retreat suggests that south Greenland may have now warmed to or above earliest Holocene summer temperatures

Hindsight Learning for MDPs with Exogenous Inputs

Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods.Comment: 53 pages, 6 figure

Adenosine-mono-phosphate-activated protein kinase-independent effects of metformin in T cells

The anti-diabetic drug metformin regulates T-cell responses to immune activation and is proposed to function by regulating the energy-stress-sensing adenosine-monophosphate-activated protein kinase (AMPK). However, the molecular details of how metformin controls T cell immune responses have not been studied nor is there any direct evidence that metformin acts on T cells via AMPK. Here, we report that metformin regulates cell growth and proliferation of antigen-activated T cells by modulating the metabolic reprogramming that is required for effector T cell differentiation. Metformin thus inhibits the mammalian target of rapamycin complex I signalling pathway and prevents the expression of the transcription factors c-Myc and hypoxia-inducible factor 1 alpha. However, the inhibitory effects of metformin on T cells did not depend on the expression of AMPK in T cells. Accordingly, experiments with metformin inform about the importance of metabolic reprogramming for T cell immune responses but do not inform about the importance of AMPK

Selecting patients for randomized trials: a systematic approach based on risk group

BACKGROUND: A key aspect of randomized trial design is the choice of risk group. Some trials include patients from the entire at-risk population, others accrue only patients deemed to be at increased risk. We present a simple statistical approach for choosing between these approaches. The method is easily adapted to determine which of several competing definitions of high risk is optimal. METHOD: We treat eligibility criteria for a trial, such as a smoking history, as a prediction rule associated with a certain sensitivity (the number of patients who have the event and who are classified as high risk divided by the total number patients who have an event) and specificity (the number of patients who do not have an event and who do not meet criteria for high risk divided by the total number of patients who do not have an event). We then derive simple formulae to determine the proportion of patients receiving intervention, and the proportion who experience an event, where either all patients or only those at high risk are treated. We assume that the relative risk associated with intervention is the same over all choices of risk group. The proportion of events and interventions are combined using a net benefit approach and net benefit compared between strategies. RESULTS: We applied our method to design a trial of adjuvant therapy after prostatectomy. We were able to demonstrate that treating a high risk group was superior to treating all patients; choose the optimal definition of high risk; test the robustness of our results by sensitivity analysis. Our results had a ready clinical interpretation that could immediately aid trial design. CONCLUSION: The choice of risk group in randomized trials is usually based on rather informal methods. Our simple method demonstrates that this decision can be informed by simple statistical analyses

The cytotoxic T cell proteome and its shaping by the kinase mTOR

High-resolution mass spectrometry maps the cytotoxic T lymphocyte (CTL) proteome and the impact of mammalian target of rapamycin complex 1 (mTORC1) on CTLs. The CTL proteome was dominated by metabolic regulators and granzymes and mTORC1 selectively repressed and promoted expression of subset of CTL proteins (~10%). These included key CTL effector molecules, signaling proteins and a subset of metabolic enzymes. Proteomic data highlighted the potential for mTORC1 negative control of phosphatidylinositol (3,4,5)-trisphosphate (PtdIns(3,4,5)P(3)) production in CTL. mTORC1 was shown to repress PtdIns(3,4,5)P(3) production and to determine the mTORC2 requirement for activation of the kinase Akt. Unbiased proteomic analysis thus provides a comprehensive understanding of CTL identity and mTORC1 control of CTL function

Death anxiety in patients with metastatic non-small cell lung cancer with and without brain metastases

Context: Death anxiety is common in patients with metastatic cancer, but its relationship to brain metastases and cognitive decline is unknown. Early identification of death anxiety and its determinants allows proactive interventions to be offered to those in need. Objectives: To identify psychological, physical, and disease-related (including brain metastases and cognitive impairment) factors associated with death anxiety in metastatic non-small cell lung cancer (mNSCLC) patients. Methods: A cross-sectional pilot study with mNSCLC outpatients completing standardized neuropsychological tests and validated questionnaires measuring death anxiety, cognitive concerns, illness intrusiveness, depression, demoralization, self-esteem, and common cancer symptoms. We constructed a composite for objective cognitive function (mean neuropsychological tests z-scores). Results: Study measures were completed by 78 patients (50% females; median age 62 years [range 37–82]). Median time since mNSCLC diagnosis was 11 months (range 0–89); 53% had brain metastases. At least moderate death anxiety was reported by 43% (n = 33). Objective cognitive impairment was present in 41% (n = 32) and perceived cognitive impairment in 27% (n = 21). Death anxiety, objective, and perceived cognitive impairment did not significantly differ between patients with and without brain metastases. In univariate analysis, death anxiety was associated with demoralization, depression, self-esteem, illness intrusiveness, common physical cancer symptoms, and perceived cognitive impairment. In multivariate analysis, demoralization (P < 0.001) and illness intrusiveness (P = 0.001) were associated with death anxiety. Conclusion: Death anxiety and brain metastases are common in patients with mNSCLC but not necessarily linked. The association of death anxiety with both demoralization and illness intrusiveness highlights the importance of integrated psychological and symptom management. Further research is needed on the psychological impact of brain metastases

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal