Geometrodynamics of Variable-Speed-of-Light Cosmologies

This paper is dedicated to the memory of Dennis Sciama. Variable-Speed-of-Light (VSL) cosmologies are currently attracting interest as an alternative to inflation. We investigate the fundamental geometrodynamic aspects of VSL cosmologies and provide several implementations which do not explicitly break Lorentz invariance (no "hard" breaking). These "soft" implementations of Lorentz symmetry breaking provide particularly clean answers to the question "VSL with respect to what?". The class of VSL cosmologies we consider are compatible with both classical Einstein gravity and low-energy particle physics. These models solve the "kinematic" puzzles of cosmology as well as inflation does, but cannot by themselves solve the flatness problem, since in their purest form no violation of the strong energy condition occurs. We also consider a heterotic model (VSL plus inflation) which provides a number of observational implications for the low-redshift universe if chi contributes to the "dark energy" either as CDM or quintessence. These implications include modified gravitational lensing, birefringence, variation of fundamental constants and rotation of the plane of polarization of light from distant sources.Comment: 19 pages, latex 209, revtex 3.1; To appear in Physical Review D; Numerous small changes of presentation and emphasis; new section on the entropy problem; references updated; central results unaffecte

Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM

BACKGROUND: Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone. Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT. METHODS: Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images. RESULTS: The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images. CONCLUSION: The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred

The My Active and Healthy Aging (My-AHA) ICT platform to detect and prevent frailty in older adults: Randomized control trial design and protocol

[EN] Introduction Frailty increases the risk of poor health outcomes, disability, hospitalization, and death in older adults and affects 7%¿12% of the aging population. Secondary impacts of frailty on psychological health and socialization are significant negative contributors to poor outcomes for frail older adults. Method The My Active and Healthy Aging (My-AHA) consortium has developed an information and communications technology¿based platform to support active and healthy aging through early detection of prefrailty and provision of individually tailored interventions, targeting multidomain risks for frailty across physical activity, cognitive activity, diet and nutrition, sleep, and psychosocial activities. Six hundred adults aged 60 years and older will be recruited to participate in a multinational, multisite 18-month randomized controlled trial to test the efficacy of the My-AHA platform to detect prefrailty and the efficacy of individually tailored interventions to prevent development of clinical frailty in this cohort. A total of 10 centers from Italy, Germany, Austria, Spain, United Kingdom, Belgium, Sweden, Japan, South Korea, and Australia will participate in the randomized controlled trial. Results Pilot testing (Alpha Wave) of the My-AHA platform and all ancillary systems has been completed with a small group of older adults in Europe with the full randomized controlled trial scheduled to commence in 2018. Discussion The My-AHA study will expand the understanding of antecedent risk factors for clinical frailty so as to deliver targeted interventions to adults with prefrailty. Through the use of an information and communications technology platform that can connect with multiple devices within the older adult's own home, the My-AHA platform is designed to measure an individual's risk factors for frailty across multiple domains and then deliver personalized domain-specific interventions to the individual. The My-AHA platform is technology-agnostic, enabling the integration of new devices and sensor platforms as they emerge.This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 689582 and the Australian National Health and Medical Research Council (NHRMC) European Union grant scheme (1115818). M.J.S. reports personal fees from Eli Lilly (Australia) Pty Ltd and grants from Novotech Pty Ltd, outside the submitted work. All other authors report nothing to disclose.Summers, MJ.; Rainero, I.; Vercelli, AE.; Aumayr, GA.; De Rosario Martínez, H.; Mönter, M.; Kawashima, R. (2018). The My Active and Healthy Aging (My-AHA) ICT platform to detect and prevent frailty in older adults: Randomized control trial design and protocol. Alzheimer's and Dementia: Translational Research and Clinical Interventions. 4:252-262. https://doi.org/10.1016/j.trci.2018.06.004S2522624Blair, S. N. (1995). Changes in Physical Fitness and All-Cause Mortality. JAMA, 273(14), 1093. doi:10.1001/jama.1995.03520380029031Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 59(3), M255-M263. doi:10.1093/gerona/59.3.m255Gillick, M. (2001). Guest Editorial: Pinning Down Frailty. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(3), M134-M135. doi:10.1093/gerona/56.3.m134Hamerman, D. (1999). Toward an Understanding of Frailty. Annals of Internal Medicine, 130(11), 945. doi:10.7326/0003-4819-130-11-199906010-00022Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., … McBurnie, M. A. (2001). Frailty in Older Adults: Evidence for a Phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 56(3), M146-M157. doi:10.1093/gerona/56.3.m146Panza, F., Solfrizzi, V., Barulli, M. R., Santamato, A., Seripa, D., Pilotto, A., & Logroscino, G. (2015). Cognitive Frailty: A Systematic Review of Epidemiological and Neurobiological Evidence of an Age-Related Clinical Condition. Rejuvenation Research, 18(5), 389-412. doi:10.1089/rej.2014.1637Soong, J., Poots, A., Scott, S., Donald, K., Woodcock, T., Lovett, D., & Bell, D. (2015). Quantifying the prevalence of frailty in English hospitals. BMJ Open, 5(10), e008456. doi:10.1136/bmjopen-2015-008456Varadhan, R., Walston, J., Cappola, A. R., Carlson, M. C., Wand, G. S., & Fried, L. P. (2008). Higher Levels and Blunted Diurnal Variation of Cortisol in Frail Older Women. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 63(2), 190-195. doi:10.1093/gerona/63.2.190BROWN, I., RENWICK, R., & RAPHAEL, D. (1995). Frailty. International Journal of Rehabilitation Research, 18(2), 93-102. doi:10.1097/00004356-199506000-00001Buchner, D. M., & Wagner, E. H. (1992). Preventing Frail Health. Clinics in Geriatric Medicine, 8(1), 1-18. doi:10.1016/s0749-0690(18)30494-4Kojima, G., Iliffe, S., Jivraj, S., & Walters, K. (2016). Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. Journal of Epidemiology and Community Health, 70(7), 716-721. doi:10.1136/jech-2015-206717Ory, M. G., Schechtman, K. B., Miller, J. P., Hadley, E. C., Fiatarone, M. A., … Province, M. A. (1993). Frailty and Injuries in Later Life: The FICSIT Trials. Journal of the American Geriatrics Society, 41(3), 283-296. doi:10.1111/j.1532-5415.1993.tb06707.xShamliyan, T., Talley, K. M. C., Ramakrishnan, R., & Kane, R. L. (2013). Association of frailty with survival: A systematic literature review. Ageing Research Reviews, 12(2), 719-736. doi:10.1016/j.arr.2012.03.001Woodhouse, K. W., & O’Mahony, M. S. (1997). Frailty and ageing. Age and Ageing, 26(4), 245-246. doi:10.1093/ageing/26.4.245CAMPBELL, A. J., & BUCHNER, D. M. (1997). Unstable disability and the fluctuations of frailty. Age and Ageing, 26(4), 315-318. doi:10.1093/ageing/26.4.315Drey, M., Pfeifer, K., Sieber, C. C., & Bauer, J. M. (2011). The Fried Frailty Criteria as Inclusion Criteria for a Randomized Controlled Trial: Personal Experience and Literature Review. Gerontology, 57(1), 11-18. doi:10.1159/000313433Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., … Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270-279. doi:10.1016/j.jalz.2011.03.008Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild Cognitive Impairment. Archives of Neurology, 56(3), 303. doi:10.1001/archneur.56.3.303Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.-O., … Petersen, R. C. (2004). Mild cognitive impairment - beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240-246. doi:10.1111/j.1365-2796.2004.01380.xDubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., … Andrieu, S. (2016). Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia, 12(3), 292-323. doi:10.1016/j.jalz.2016.02.002Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., … Altman, D. G. (2010). CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ, 340(mar23 1), c869-c869. doi:10.1136/bmj.c869Gray, L. C., Bernabei, R., Berg, K., Finne-Soveri, H., Fries, B. E., Hirdes, J. P., … Ariño-Blasco, S. (2008). Standardizing Assessment of Elderly People in Acute Care: The interRAI Acute Care Instrument. Journal of the American Geriatrics Society, 56(3), 536-541. doi:10.1111/j.1532-5415.2007.01590.xRadloff, L. S. (1977). The CES-D Scale. Applied Psychological Measurement, 1(3), 385-401. doi:10.1177/014662167700100306Guralnik, J. M., Simonsick, E. M., Ferrucci, L., Glynn, R. J., Berkman, L. F., Blazer, D. G., … Wallace, R. B. (1994). A Short Physical Performance Battery Assessing Lower Extremity Function: Association With Self-Reported Disability and Prediction of Mortality and Nursing Home Admission. Journal of Gerontology, 49(2), M85-M94. doi:10.1093/geronj/49.2.m85Powell, L. E., & Myers, A. M. (1995). The Activities-specific Balance Confidence (ABC) Scale. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 50A(1), M28-M34. doi:10.1093/gerona/50a.1.m28Kendzierski, D., & DeCarlo, K. J. (1991). Physical Activity Enjoyment Scale: Two Validation Studies. Journal of Sport and Exercise Psychology, 13(1), 50-64. doi:10.1123/jsep.13.1.50Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). «Mini-mental state». Journal of Psychiatric Research, 12(3), 189-198. doi:10.1016/0022-3956(75)90026-6Brandt, J. (1991). The hopkins verbal learning test: Development of a new memory test with six equivalent forms. Clinical Neuropsychologist, 5(2), 125-142. doi:10.1080/13854049108403297Lubben, J. E. (1988). Assessing social networks among elderly populations. Family & Community Health, 11(3), 42-52. doi:10.1097/00003727-198811000-00008Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39(3), 472-480. doi:10.1037/0022-3514.39.3.472De Vries, O. J., Peeters, G. M. E. E., Lips, P., & Deeg, D. J. H. (2013). Does frailty predict increased risk of falls and fractures? A prospective population-based study. Osteoporosis International, 24(9), 2397-2403. doi:10.1007/s00198-013-2303-zTheou, O., Stathokostas, L., Roland, K. P., Jakobi, J. M., Patterson, C., Vandervoort, A. A., & Jones, G. R. (2011). The Effectiveness of Exercise Interventions for the Management of Frailty: A Systematic Review. Journal of Aging Research, 2011, 1-19. doi:10.4061/2011/569194Cadore, E. (2014). Strength and Endurance Training Prescription in Healthy and Frail Elderly. Aging and Disease, 5(3), 183. doi:10.14336/ad.2014.0500183Cadore, E. L., Rodríguez-Mañas, L., Sinclair, A., & Izquierdo, M. (2013). Effects of Different Exercise Interventions on Risk of Falls, Gait Ability, and Balance in Physically Frail Older Adults: A Systematic Review. Rejuvenation Research, 16(2), 105-114. doi:10.1089/rej.2012.1397Gardner, M. M. (2001). Practical implementation of an exercise-based falls prevention programme. Age and Ageing, 30(1), 77-83. doi:10.1093/ageing/30.1.77Eng, J. J. (2010). Fitness and Mobility Exercise Program for Stroke. Topics in Geriatric Rehabilitation, 26(4), 310-323. doi:10.1097/tgr.0b013e3181fee736Wadlinger, H. A., & Isaacowitz, D. M. (2008). Looking happy: The experimental manipulation of a positive visual attention bias. Emotion, 8(1), 121-126. doi:10.1037/1528-3542.8.1.121MacLeod, C. (2012). Cognitive bias modification procedures in the management of mental disorders. Current Opinion in Psychiatry, 25(2), 114-120. doi:10.1097/yco.0b013e32834fda4aMensink, R. P., & Katan, M. B. (1989). Effect of a Diet Enriched with Monounsaturated or Polyunsaturated Fatty Acids on Levels of Low-Density and High-Density Lipoprotein Cholesterol in Healthy Women and Men. New England Journal of Medicine, 321(7), 436-441. doi:10.1056/nejm19890817321070

Rumen biogeographical regions and their impact on microbial and metabolome variation

The rumen microbiome is a complex microbial network critical to the health and nutrition of its host, due to their inherent ability to convert low-quality feedstuffs into energy. In rumen microbiome studies, samples from the ventral sac are most often collected because of the ease of access and repeatability. However, anatomical musculature demarcates the rumen into five sacs (biogeographical regions), which may support distinct microbial communities. The distinction among the microbes may generate functional variation among the rumen microbiome, thus, specialized tasks within different sacs. The objective of this study was to determine the rumen liquid metabolome and epimural, planktonic, and fiber-adherent bacterial communities among each rumen biogeographical region. It was hypothesized that differences in bacterial species and metabolome would occur due to differing anatomy and physiology associated with the respective regions. To assess this variation, epithelial and content microbial-associated communities were evaluated, as well as the metabolites among various rumen biogeographical regions. A total of 17 cannulated Angus cows were utilized to examine the fiber-adherent (solid fraction), planktonic (liquid fraction), and epimural microbial communities from the cranial, dorsal, caudodorsal blind, caudoventral blind, and ventral sacs. Metagenomic DNA was extracted and sequenced from the hypervariable V4 region of the 16S rRNA gene. Reads were processed using packages ‘phyloseq’ and ‘dada2’ in R. Untargeted metabolomics were conducted on rumen liquid from each sac using UHPLC-HRMS and analyzed in MetaboAnalyst 5.0. An analysis of variance (ANOVA) revealed 13 significant differentially abundant metabolites with pairwise comparisons against the five rumen sacs (P < 0.05). Within the bacterial communities, neither alpha nor beta diversity determined significance against the rumen sacs (P > 0.05), although there was significance against the fraction types (P < 0.05). Utilizing multivariable association analysis with MaAslin2, there were significant differential abundances found in fraction type × location (P < 0.05). Knowledge of similarities among fiber-adherent microbial communities provides evidence that single sac sampling is sufficient for this fraction. However, future projects focusing on either planktonic or epimural fractions may need to consider multiple rumen sac sampling to obtain the most comprehensive analysis of the rumen. Defining these variabilities, especially among the rumen epimural microbiome, are critical to define host-microbiome interactions

Priority monism and part/whole dependence

Priority monism is the view that the cosmos is the only independent concrete object. The paper argues that, pace its proponents, Priority monism is in conflict with the dependence of any whole on any of its parts: if the cosmos does not depend on its parts, neither does any smaller composit