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Injection of CO2 for geologic carbon sequestration into deep sedimentary formations involves fluid pressure increases that engage hydromechanical processes that can cause seismicity by activation of existing faults. In this work, we use a coupled multiphase fluid flow and geomechanical simulator to model spatiotemporal fluid pressure and stress changes in order to study the poroelastic effect of CO2 injection on faults in crystalline basement rock below the injection zone. The seismicity rate along features interpreted to be basement faults is modeled using Dieterich's rate-and-state earthquake nucleation model. The methodology is applied to microseismicity detected during CO2 injection into the Mount Simon formation during the Illinois Basin—Decatur Project. The modeling accurately captures an observed reduction in seismicity rate when the injection in the second well was into a slightly shallower zone above the base of the Mount Simon formation. Moreover, the modeling shows that it is important to consider poroelastic stress changes, in addition to fluid pressure changes for accurately modeling of the observed seismicity rate.

Background: Persons with advanced cancer and their relatives experience physical, emotional, and psychosocial consequences of the illness. Most of the time, they must deal with these themselves. While peer self-management support programs may be helpful, there is little evidence on their value for this population. We present the research protocol of our SMART study that will evaluate the effectiveness of the “Living with Cancer” peer self-management support program, aimed at improving self-management behaviors, self-efficacy, and health-related quality of life of persons with advanced cancer and their relatives. Methods: We will conduct a non-randomized stepped wedge study in the Netherlands. We will include 130 persons with advanced cancer and 32 relatives. Participants can choose to either start the program within 4 weeks after inclusion or after eight to 10 weeks. The “Living with Cancer” is a peer self-management support program, based on the Chronic Disease Self-Management Program. It consists of six 1,5 hours video-conferencing group meetings with eight to 12 participants, preceded by two or three preparatory audio clips with supportive text per session. The program has the following core components: the learning of self-management skills (action-planning, problem-solving, effective communication, and decision-making), discussing relevant themes (e.g. dealing with pain and fatigue, living with uncertainty, and future planning), and sharing experiences, knowledge, and best practices. The primary outcome for both persons with advanced cancer and relatives is self-management behavior assessed by the subscale “constructive attitudes and approaches” of the Health Education Impact Questionnaire. Secondary outcomes are other self-management behaviors, self-efficacy, health-related quality of life, symptoms, depression and anxiety, and loneliness. Participants complete an online questionnaire at baseline, and after eight and 16 weeks. After each session, they compl

Detection of microbial components such as lipopolysaccharide (LPS) by Toll-like receptor 4 (TLR4) on macrophages induces a robust pro-inflammatory response that is dependent on metabolic reprogramming. These innate metabolic changes have been compared to aerobic glycolysis in tumour cells. However, the mechanisms by which TLR4 activation leads to mitochondrial and glycolytic reprogramming are unknown. Here we show that TLR4 activation induces a signalling cascade recruiting TRAF6 and TBK-1, while TBK-1 phosphorylates STAT3 on S727. Using a genetically engineered mouse model incapable of undergoing STAT3 Ser727 phosphorylation, we show ex vivo and in vivo that STAT3 Ser727 phosphorylation is critical for LPS-induced glycolytic reprogramming, production of the central immune response metabolite succinate and inflammatory cytokine production in a model of LPS-induced inflammation. Our study identifies non-canonical STAT3 activation as the crucial signalling intermediary for TLR4-induced glycolysis, macrophage metabolic reprogramming and inflammation.

The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs. Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.

This paper introduces and discusses a method to rigorously classify and prioritize UCTs using Bayesian inference and admissible regions. A detailed derivation and discussion of the methodology is given, followed by a generalized de nition of prioritization parameters. Several example prioritization parameters including time left to detect,' zero-e ort miss,' and e ective albedo-area' are motivated and given. A number of illustrative applications with optical UCTs are examined to demonstrate information that can be extracted from each observation. Finally, the information extracted from each UCT is then compared and approaches to observation prioritization discussed., In the Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference, 9-12 Sep 2014, Maui, HI.

The problem of estimating attitude for actively maneuvering or passively rotating Space Objects (SOs) with unknown mass properties / external torques and uncertain shape models is addressed. To account for agile SO maneuvers, angular rates are simply assumed to be random inputs (e.g., process noise), and model uncertainty is accounted for in a bias state with dynamics derived using rst principles. Bayesian estimation approaches are used to estimate the resulting severely non-Gaussian and multi-modal state distributions. Simulated results are given, conclusions regarding performance are made, and future work is outlined., Presented at the 13th annual Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference on September 11-14, 2012 Maui, Hawaii. Government or Federal Purpose Rights License. The original document contains color images.

In general, the task of identifying the shape, function, and status of objects from nonimaging temporally-resolved spectral data is impossible. This is due to the limited degrees of freedom in the data as compared to the degrees of freedom that define shape, function, and status. However, by modeling objects as combinations of simple shapes with simple reflective and spectral characteristics, the problem though daunting is expected to become tractable due to the reduction of degrees of freedom in object space. One anticipated result from this modeling process is that the properties of null and near-null object features can be characterized; that is, an object can be characterized as a set of observable features summed with null or near-null features that perturb the measurement only slightly or not at all. The initial development of classes presented in this paper is a first step towards an inversion procedure to map spectral data into classes.

Atmospheric density modeling has long been one of the greatest uncertainties in the dynamics of low Earth satellite orbits. Accurate density calculations are required to provide meaningful estimates of the atmospheric drag perturbing satellite motion. These effects increase with lower altitude orbits and also with higher effective area and lower mass satellites. Both of these conditions are usually met by the new class of small satellites being developed around the world as low-cost ventures into space. Early atmospheric models consisted of simple exponential functions with scale height factors that allowed the user to estimate a constant density at a given altitude. From these approaches and estimated density conditions, standard atmospheres were developed that were meant to represent the average conditions at any given point in time for a particular altitude. However, it was soon obvious that the atmosphere was far too dynamic to be adequately represented by static approaches. The 1990s brought a new approach to improving atmospheric density modeling. Dynamic Calibration of the Atmosphere (DCA) involves estimating density corrections to a given atmospheric density model based upon the observed motion of satellites. The goal of atmospheric density dynamics research is to understand and characterize the dynamic nature of the extreme upper atmosphere and develop improved orbital prediction algorithms based upon this new understanding. Many indicators suggest that the extreme upper atmosphere is far more dynamic than what is accounted for in atmospheric density models used in orbit propagation. This paper explores the impact these dynamics have on the motion of satellites. Simulations show that unmodeled atmospheric density dynamics can greatly impact the orbit determination process and add kilometers of error to orbit predictions. (10 figures, 8 refs.), Presented at the AMOS Technical Conference held in Maui, Hawaii, on 16-20 Sep 2002. Sponsored by the Air Force Office of Scientific Research (AFOSR).

Previous work with laboratory and field observations and computer simulations suggests that classes of spacecraft can be distinguished on the basis of their color or spectral properties. Empirical results also indicate that accounting for systematic effects reduces much of the observed scatter and thus increases separability of the classes. We seek to gain an understanding of the basic properties of satellite features that determine the spectral characteristics of individual satellites: surface materials and scattering properties, combination of materials, shape, orientation, illumination-viewing geometries, etc. Initial efforts have focused on systematic effects of solar phase angle on object brightness and color data.