Gamma Rays from Clusters and Groups of Galaxies: Cosmic Rays versus Dark Matter

Clusters of galaxies have not yet been detected at gamma-ray frequencies; however, the recently launched Fermi Gamma-ray Space Telescope, formerly known as GLAST, could provide the first detections in the near future. Clusters are expected to emit gamma rays as a result of (1) a population of high-energy primary and re-accelerated secondary cosmic rays (CR) fueled by structure formation and merger shocks, active galactic nuclei and supernovae, and (2) particle dark matter (DM) annihilation. In this paper, we ask the question of whether the Fermi telescope will be able to discriminate between the two emission processes. We present data-driven predictions for a large X-ray flux limited sample of galaxy clusters and groups. We point out that the gamma ray signals from CR and DM can be comparable. In particular, we find that poor clusters and groups are the systems predicted to have the highest DM to CR emission at gamma-ray energies. Based on detailed Fermi simulations, we study observational handles that might enable us to distinguish the two emission mechanisms, including the gamma-ray spectra, the spatial distribution of the signal and the associated multi-wavelength emissions. We also propose optimal hardness ratios, which will help to understand the nature of the gamma-ray emission. Our study indicates that gamma rays from DM annihilation with a high particle mass can be distinguished from a CR spectrum even for fairly faint sources. Discriminating a CR spectrum from a light DM particle will be instead much more difficult, and will require long observations and/or a bright source. While the gamma-ray emission from our simulated clusters is extended, determining the spatial distribution with Fermi will be a challenging task requiring an optimal control of the backgrounds.Comment: revised to match resubmitted version, 35 pages, 16 figures: results unchanged, some discussion added and unnecessary text and figures remove

Body composition in young female eating-disorder patients with severe weight loss and controls: evidence from the four-component model and evaluation of DXA

BACKGROUND/OBJECTIVES: Whether fat-free mass (FFM) and its components are depleted in eating-disorder (ED) patients is uncertain. Dual energy X-ray absorptiometry (DXA) is widely used to assess body composition in pediatric ED patients; however, its accuracy in underweight populations remains unknown. We aimed (1) to assess body composition of young females with ED involving substantial weight loss, relative to healthy controls using the four-component (4C) model, and (2) to explore the validity of DXA body composition assessment in ED patients. SUBJECTS/METHODS: Body composition of 13 females with ED and 117 controls, aged 10-18 years, was investigated using the 4C model. Accuracy of DXA for estimation of FFM and fat mass (FM) was tested using the approach of Bland and Altman. RESULTS: Adjusting for age, height and pubertal stage, ED patients had significantly lower whole-body FM, FFM, protein mass (PM) and mineral mass (MM) compared with controls. Trunk and limb FM and limb lean soft tissue were significantly lower in ED patients. However, no significant difference in the hydration of FFM was detected. Compared with the 4C model, DXA overestimated FM by 5 +/- 36% and underestimated FFM by 1 +/- 9% in ED patients. CONCLUSION: Our study confirms that ED patients are depleted not only in FM but also in FFM, PM and MM. DXA has limitations for estimating body composition in individual young female ED patients

The individual and combined effects of obesity- and ageing-induced systemic inflammation on human skeletal muscle properties.

BACKGROUND/OBJECTIVES: The purpose of this study was to determine whether circulating pro-inflammatory cytokines, elevated with increased fat mass and ageing, were associated with muscle properties in young and older people with variable adiposity. SUBJECTS/METHODS: Seventy-five young (18-49 yrs) and 67 older (50-80 yrs) healthy, untrained men and women (BMI: 17-49 kg/m(2)) performed isometric and isokinetic plantar flexor maximum voluntary contractions (MVCs). Volume (Vm), fascicle pennation angle (FPA), and physiological cross-sectional area (PCSA) of the gastrocnemius medialis (GM) muscle were measured using ultrasonography. Voluntary muscle activation (VA) was assessed using electrical stimulation. GM specific force was calculated as GM fascicle force/PCSA. Percentage body fat (BF%), body fat mass (BFM), and lean mass (BLM) were assessed using dual-energy X-ray absorptiometry. Serum concentration of 12 cytokines was measured using multiplex luminometry. RESULTS: Despite greater Vm, FPA, and PCSA (P0.05), while IL-8 correlated with VA in older but not young adults (r⩾0.378, P⩽0.027). TNF-alpha correlated with MVC, lean mass, GM FPA and maximum force in older adults (r⩾0.458; P⩽0.048). CONCLUSIONS: The age- and adiposity-dependent relationships found here provide evidence that circulating pro-inflammatory cytokines may play different roles in muscle remodelling according to the age and adiposity of the individual.International Journal of Obesity accepted article preview online, 29 August 2016. doi:10.1038/ijo.2016.151

Volatile Organic Compound Detection and Disease Diagnostics Using DNA-Functionalized Carbon Nanotube Sensor Arrays

There is a strong desire for novel chemical sensors that can detect low concentrations of volatile organic compounds (VOCs) for early-stage disease diagnostics as well as various environmental monitoring applications. The aim of this thesis work was to address these challenges by developing an “electronic nose” (e-nose) platform based on chemical sensor arrays capable of detecting and differentiating between various VOCs of interest. Sensor arrays were fabricated in a field-effect transistor (FET) configuration with exquisitely sensitive carbon nanotubes (CNTs) as the channel material. The nanotubes were functionalized with a variety of single-stranded DNA oligomers, forming DNA-NT hybrid structures with affinity to a wide variety of VOC targets. Interactions between DNA-NTs and VOCs yielded changes in sensor conductivity that depended strongly on the base sequence of DNA. Arrays of CNT devices were functionalized with up to ten different DNA oligomers to enable electronic signature readouts of VOC binding events. DNA-NT responses were processed with pattern recognition algorithms in order to classify different VOC targets according to their chemical “fingerprints.” This technology was used to measure VOC biomarkers associated with ovarian cancer and COVID-19 from human fluid media. DNA-NT arrays measured headspaces VOCs from 58 blood plasma samples from individual people, including 15 with a late-stage malignant form of ovarian cancer, 6 with early-stage malignant cancer, 16 with a benign form of cancer, and 21 healthy age-matched controls. Statistical techniques based on machine learning were used to discriminate between the malignant, benign, and healthy groups with 90 – 95% classification accuracy. Furthermore, all six early-stage samples were correctly identified with the malignant group, indicating significant progress towards an effective screening method for ovarian cancer. Similar investigations were conducted on sweat samples procured from patients who had tested positive for COVID-19 (CoV+) and those who had tested negative (CoV-). Statistical analysis of the DNA-NT responses to the sweat headspace VOCs revealed highly differentiated clusters associated with the CoV+ and CoV- groups. A binary classifier was constructed using the response data and was estimated to have a 99% classification success rate, suggesting strong potential for utilizing DNA-NTs for COVID screening. Finally, DNA-NT arrays were assessed based on various performance characteristics desired for remote environmental monitoring applications such as pollution monitoring and explosives detection in a warzone. A series of experiments was conducted to evaluate DNA-NT sensitivity, specificity, and longevity using mixtures of 2,6-dinitrotoluene (DNT) and dimethyl methylphosphonate (DMMP) to simulate complex VOC environments. The sensors demonstrated sensitivity to parts-per-billion concentrations of DNT in a highly concentrated background of DMMP. Moreover, the shelf life of these sensors was projected on the order of months, making DNA-NTs promising candidates for a wide range of applications