A New Method for the Determination of Water Content in Extra Dry Gases

The water content in extra dry gases can be determined by the condensation of water vapor on the cooled walls of the container. After separation, vapor pressure is measured at room temperature by the Pirani vacuummeter. In favorable conditions water content as low as 1 μg per liter of gas at NTP can be detected

Lipid Peroxidation and Depressed Mood in Community-Dwelling Older Men and Women

It has been hypothesized that cellular damage caused by oxidative stress is associated with late-life depression but\ud epidemiological evidence is limited. In the present study we evaluated the association between urinary 8-iso-prostaglandin\ud F2a (8-iso-PGF2a), a biomarker of lipid peroxidation, and depressed mood in a large sample of community-dwelling older\ud adults. Participants were selected from the Health, Aging and Body Composition study, a community-based longitudinal\ud study of older persons (aged 70–79 years). The present analyses was based on a subsample of 1027 men and 948 women\ud free of mobility disability. Urinary concentration of 8-iso-PGF2a was measured by radioimmunoassay methods and adjusted\ud for urinary creatinine. Depressed mood was defined as a score greater than 5 on the 15-item Geriatric Depression Scale and/\ud or use of antidepressant medications. Depressed mood was present in 3.0% of men and 5.5% of women. Depressed men\ud presented higher urinary concentrations of 8-iso-PGF2a than non-depressed men even after adjustment for multiple\ud sociodemographic, lifestyle and health factors (p=0.03, Cohen’s d = 0.30). This association was not present in women\ud (depressed status-by-sex interaction p = 0.04). Our study showed that oxidative damage may be linked to depression in\ud older men from a large sample of the general population. Further studies are needed to explore whether the modulation of\ud oxidative stress may break down the link between late-life depression and its deleterious health consequences

Unicorn, hare, or tortoise? Using machine learning to predict working memory training performance

People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which specific individual differences, and in what combination, contribute to inter-individual variations in training trajectories. In the current study, 568 undergraduates completed one of several N-back intervention variants over the course of two weeks. Participants\u27 training trajectories were clustered into three distinct training patterns (high performers, intermediate performers, and low performers). We applied machine-learning algorithms to train a binary tree model to predict individuals\u27 training patterns relying on several individual difference variables that have been identified as relevant in previous literature. These individual difference variables included pre-existing cognitive abilities, personality characteristics, motivational factors, video game experience, health status, bilingualism, and socioeconomic status. We found that our classification model showed good predictive power in distinguishing between high performers and relatively lower performers. Furthermore, we found that openness and pre-existing WM capacity to be the two most important factors in distinguishing between high and low performers. However, among low performers, openness and video game background were the most significant predictors of their learning persistence. In conclusion, it is possible to predict individual training performance using participant characteristics before training, which could inform the development of personalized interventions

The effect of family size on estimates of the frequency of hereditary non-polyposis colorectal cancer.

Diagnosis of hereditary non-polyposis colorectal cancer (HNPCC) is currently based on phenotypical analysis of an expanded pedigree. Diagnostic guidelines ('Amsterdam criteria') proposed by the International Collaborative Group on HNPCC are often too stringent for use with small families. There is also the possibility of false-positive diagnosis in large pedigrees that may contain chance clusters of tumours. This study was conducted to determine the effect of family size on the probability of diagnosing HNPCC according to the Amsterdam criteria. A total of 1052 patients with colorectal cancer were classified as HNPCC or non-HNPCC according to the Amsterdam criteria. Associations between this diagnosis and the size of the first-degree pedigree were evaluated in logistic regression and linear discriminant analyses. Logistic regression showed a significant association for family size with the Amsterdam-criteria-based HNPCC diagnosis. Linear discriminant analysis showed that HNPCC diagnosis was most likely to occur when first-degree pedigrees contained more than seven relatives. Failure to consider family size in phenotypic diagnosis of HNPCC can lead to both under- and overestimation of the frequency of this disease. Small pedigrees must be expanded to reliably exclude HNPCC. Positive diagnoses based on assessment of eight or more first-degree relatives should be supported by other clinical features

Change in Physical Performance Over Time in Older Women: The Women's Health and Aging Study

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1,1,1-Trifluoro-4-(thio­phen-2-yl)-4-[(2-{[4,4,4-trifluoro-3-oxo-1-(thio­phen-2-yl)but-1-en-1-yl]amino}­eth­yl)amino]­but-3-en-2-one

The asymmetric unit of the diamine compound, C18H14F3N2O2S2, consists of two mol­ecules; the C=C double bond has a Z configuration in the C4H3S—C=C—C(=O)—C segment. The –NH—CH2—CH2—NH chain adopts a twisted U-shape. The amino group is an intra­molecular hydrogen-bond donor to the carbonyl group; the intra­molecular hydrogen bond generates a six-membered ring. In both mol­ecules, the thienyl rings are disordered over two positions; the occupancies of the major components are 0.817 (4) and 0.778 (4) in one mol­ecule and 0.960 (4) and 0.665 (4) in the other. One of the trifluoro­methyl groups is disordered over two positions with the major component having 0.637 (8) occupancy

The Impact of Behavioral Intervention on Obesity Mediated Declines in Mobility Function: Implications for Longevity

A primary focus of longevity research is to identify prognostic risk factors that can be mediated by early treatment efforts. To date, much of this work has focused on understanding the biological processes that may contribute to aging process and age-related disease conditions. Although such processes are undoubtedly important, no current biological intervention aimed at increasing health and lifespan exists. Interestingly, a close relationship between mobility performance and the aging process has been documented in older adults. For example, recent studies have identified functional status, as assessed by walking speed, as a strong predictor of major health outcomes, including mortality, in older adults. This paper aims to describe the relationship between the comorbidities related to decreased health and lifespan and mobility function in obese, older adults. Concurrently, lifestyle interventions, including diet and exercise, are described as a means to improve mobility function and thereby limit the functional limitations associated with increased mortality

Distributional Latent Variable Models with an Application in Active Cognitive Testing

Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near-ubiquitous approach is to repeat many observations for each test, resulting in a distribution over the outcomes from each test given to each subject. In this paper, we explore the usage of latent variable modeling to enable learning across many correlated variables simultaneously. We extend latent variable models (LVMs) to the setting where observed data for each subject are a series of observations from many different distributions, rather than simple vectors to be reconstructed. By embedding test battery results for individuals in a latent space that is trained jointly across a population, we are able to leverage correlations both between tests for a single participant and between multiple participants. We then propose an active learning framework that leverages this model to conduct more efficient cognitive test batteries. We validate our approach by demonstrating with real-time data acquisition that it performs comparably to conventional methods in making item-level predictions with fewer test items.Comment: 9 pages, 6 figure