Applying a cumulative deficit model of frailty to dementia: progress and future challenges

The article by Song and colleagues presents findings from the Canadian Study of Health and Aging showing that the accumulation of health deficits, defined dichotomously and unqualified by severity or domain, predicted late-life dementia independent of chronological age. We identify strengths of this model, and also areas for future research. Importantly, this article broadens the perspective of research into measuring risk of dementia from focusing on specific neuropathological markers of dementia subtypes, to mechanisms underlying more general bodily vitality and health, as well as dysfunctions in repair. This work places late-life dementia in a new context, influenced more broadly by health maintenance, and less by specific neurological disease. While useful at a global level, the lack of specificity of this approach may ultimately limit its application to individual patients because without linking risk to etiology, assessment does not indicate an intervention. Ultimately, the article has value for stimulating debate about approaches to risk identification and risk reduction, suggesting that the current focus on cardiometabolic risk factors may be too limited.KJA is funded by NHMRC Research Fellowship # 1002560. RAD is supported in part by a Canada Research Chair (Tier 1). The research is supported by the Dementia Collaborative Research Centres (to KJA) and the National Institutes of Health (National Institute on Aging, R01 AG008235, to RAD)

Alzheimer’s Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD

Application of diagnostic techniques to an experimental aircraft fuel rig

An important issue for Aerospace and Defence Systems providers is how to reduce the risks associated with installing a new Fault Detection Tool (FDT) on a system. It is highly desirable that some degree of assessment, selection and validation is carried out before the FDT is integrated with the system. This paper describes the initial phases of a project to investigate the processes behind the assessment and validation using an Experimental Aircraft Fuel Rig (referred to as the Advanced Diagnostic Test-bed ADT). This paper also presents results from preliminary verification and validation work that has been used on a mathematical model of the ADT, and also some results from some initial diagnostic technique assessment that has been performed using real experimental data from the ADT and simulated data from mathematical models

Reactive Oxygen Intermediates Mediate a Systemic Signal Network in the Establishment of Plant Immunity

AbstractRecognition of an avirulent pathogen stimulates an oxidative burst generating O2− and H2O2, and these reactive oxygen intermediates (ROIs) cue the induction of defense genes and cell death in the development of a restricted lesion. This localized hypersensitive response (HR) is accompanied by the development of systemic acquired resistance to virulent pathogens. Here we show that inoculation of Arabidopsis leaves with avirulent Pseudomonas syringae induces secondary oxidative bursts in discrete cells in distant tissues, leading to low-frequency systemic micro-HRs. The primary oxidative burst induces these systemic responses, and both the primary burst and the secondary microbursts are required for systemic immunity. Hence, ROIs mediate a reiterative signal network underlying systemic as well as local resistance responses

Low-cost syngas shifting for remote gasifiers: Combination of CO2 adsorption and catalyst addition in a novel and simplified packed structure

This paper presents the technical validation of a novel, low-complexity alternative based on the inclusion of a patented (IEPI-MU-2016-185) packed bed for improving the performance of remote, small-scale gasification facilities. This study was carried out in an updraft, atmospheric-pressure gasifier, outfitted with a syngas reflux line, air and oxygen feed, and an upper packed-bed coupled to the gasification unit to improve the syngas quality by catalytic treatment and CO2 adsorption. The experimental facility is located in the rural community San Pedro del Laurel, Ecuador. Gasification experiments, with and without packed material in the upper chamber, were performed to assess its effect on the syngas quality. The assessment revealed that the packed material increases the carbon monoxide (CO) content in the syngas outlet stream while carbon dioxide (CO2) was reduced. This option appears to be a suitable and low-complexity alternative for enhancing the content of energy vectors of syngas in gasification at atmospheric pressure since CO/CO2 ratios of 5.18 and 3.27 were achieved against reported values of 2.46 and 0.94 for operations which did not include the addition of packed material. It is concluded that the upper packed-bed is an active element able to modify syngas characteristics since CO2 content was reduce

White Dwarfs in Globular Clusters: HST Observations of M4

Using WFPC2 on the Hubble Space Telescope, we have isolated a sample of 258 white dwarfs (WDs) in the Galactic globular cluster M4. Fields at three radial distances from the cluster center were observed and sizeable WD populations were found in all three. The location of these WDs in the color-magnitude diagram, their mean mass of 0.51($\pm 0.03$)M$_{\odot}$, and their luminosity function confirm basic tenets of stellar evolution theory and support the results from current WD cooling theory. The WDs are used to extend the cluster main-sequence mass function upward to stars that have already completed their nuclear evolution. The WD/red dwarf binary frequency in M4 is investigated and found to be at most a few percent of all the main-sequence stars. The most ancient WDs found are about 9 Gyr old, a level which is set solely by the photometric limits of our data. Even though this is less than the age of M4, we discuss how these cooling WDs can eventually be used to check the turnoff ages of globular clusters and hence constrain the age of the Universe.Comment: 46 pages, latex, no figures included, figures available at ftp://ftp.astro.ubc.ca/pub/richer/wdfig.uu size 2.7Mb. To be published in the Astrophysical Journa

Associations between differential aging and lifestyle, environment, current, and future health conditions : Findings from Canadian Longitudinal Study on Aging

Acknowledgement This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA dataset (Comprehensive Cohort), under Application Number 1906013. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland.Peer reviewedPostprin

Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation

BackgroundPersons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.MethodParticipants were 48 well-characterized PD patients (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.ResultsAn excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.ConclusionOur data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains

Linking Physical Activity to Breast Cancer via Sex Steroid Hormones, Part 2:The Effect of Sex Steroid Hormones on Breast Cancer Risk

We undertook a systematic review and appraised the evidence for an effect of circulating sex steroid hormones and sex hormone–binding globulin (SHBG) on breast cancer risk in pre- and postmenopausal women. Systematic searches identified prospective studies relevant to this review. Meta-analyses estimated breast cancer risk for women with the highest compared with the lowest level of sex hormones, and the DRMETA Stata package was used to graphically represent the shape of these associations. The ROBINS-E tool assessed risk of bias, and the GRADE system appraised the strength of evidence. In premenopausal women, there was little evidence that estrogens, progesterone, or SHBG were associated with breast cancer risk, whereas androgens showed a positive association. In postmenopausal women, higher estrogens and androgens were associated with an increase in breast cancer risk, whereas higher SHBG was inversely associated with risk. The strength of the evidence quality ranged from low to high for each hormone. Dose–response relationships between sex steroid hormone concentrations and breast cancer risk were most notable for post-menopausal women. These data support the plausibility of a role for sex steroid hormones in mediating the causal relationship between physical activity and the risk of breast cancer. See related reviews by Lynch et al., p. 11 and Swain et al., p. 1

Automated Speckle Interferometry of Known Binaries

Astronomers have been measuring the separations and position angles between the two components of binary stars since William Herschel began his observations in 1781. In 1970, Anton Labeyrie pioneered a method, speckle interferometry, that overcomes the usual resolution limits induced by atmospheric turbulence by taking hundreds or thousands of short exposures and reducing them in Fourier space. Our 2022 automation of speckle interferometry allowed us to use a fully robotic 1.0-meter PlaneWave Instruments telescope, located at the El Sauce Observatory in the Atacama Desert of Chile, to obtain observations of many known binaries with established orbits. The long-term objective of these observations is to establish the precision, accuracy, and limitations of this telescope's automated speckle interferometry measurements. This paper provides an early overview of the Known Binaries Project and provide example results on a small-separation (0.27") binary, WDS 12274-2843 B 228