Social Network Characteristics and Psychological Well-Being: A Replication and Extension

This article represents a replication and extension of a previous study by Israel and her colleagues that investigated the relationship between psychological well-being and social network characteristics. The present research included both a comparable sample of white women (N=104) between the ages of 60 and 68 (as in the original study), and a more extensive adult population of men and women (N=718) between the ages of 50 and 95. The network characteristics examined are categorized along three broad dimensions: Structure—linkages in the overall network (size and density); interaction-nature of the linkages themselves (frequency, geographic dispersion, and reciprocity); and functions that networks provide (affective support and instrumental support). The results indicate a predominance of comparable findings for both the replication and extension studies. Of the eight network characteristics examined, the results of five of the regression analyses were the same across all three studies. The network characteristics of size, density, geographic dispersion, reciprocal instrumental support, and instrumental support did not make a significant contribution to the variance in psychological well-being. Of the other three network characteristics, the effect of frequency of interaction varied across the studies, and a pattern of significant results was found for affective support and reciprocal affective support. A discussion of this evidence in light of current literature and implications for practice and research is included.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67842/2/10.1177_109019818701400406.pd

Hidden Orbital Order in URu2Si2URu_{2}Si_{2}

When matter is cooled from high temperatures, collective instabilities develop amongst its constituent particles that lead to new kinds of order. An anomaly in the specific heat is a classic signature of this phenomenon. Usually the associated order is easily identified, but sometimes its nature remains elusive. The heavy fermion metal $URu_2Si_2$ is one such example, where the order responsible for the sharp specific heat anomaly at $T_0=17 K$ has remained unidentified despite more than seventeen years of effort. In $URu_{2}Si_{2}$, the coexistence of large electron-electron repulsion and antiferromagnetic fluctuations in $URu_2Si_2$ leads to an almost incompressible heavy electron fluid, where anisotropically paired quasiparticle states are energetically favored. In this paper we use these insights to develop a detailed proposal for the hidden order in $URu_2Si_2$. We show that incommensurate orbital antiferromagnetism, associated with circulating currents between the uranium ions, can account for the local fields and entropy loss observed at the $17 K$ transition; furthermore we make detailed predictions for neutron scattering measurements

Performance deficits of NK1 receptor knockout mice in the 5 choice serial reaction time task: effects of d Amphetamine, stress and time of day.

Background The neurochemical status and hyperactivity of mice lacking functional substance P-preferring NK1 receptors (NK1R-/-) resemble abnormalities in Attention Deficit Hyperactivity Disorder (ADHD). Here we tested whether NK1R-/- mice express other core features of ADHD (impulsivity and inattentiveness) and, if so, whether they are diminished by d-amphetamine, as in ADHD. Prompted by evidence that circadian rhythms are disrupted in ADHD, we also compared the performance of mice that were trained and tested in the morning or afternoon. Methods and Results The 5-Choice Serial Reaction-Time Task (5-CSRTT) was used to evaluate the cognitive performance of NK1R-/- mice and their wildtypes. After training, animals were tested using a long (LITI) and a variable (VITI) inter-trial interval: these tests were carried out with, and without, d-amphetamine pretreatment (0.3 or 1 mg/kg i.p.). NK1R-/- mice expressed greater omissions (inattentiveness), perseveration and premature responses (impulsivity) in the 5-CSRTT. In NK1R-/- mice, perseveration in the LITI was increased by injection-stress but reduced by d-amphetamine. Omissions by NK1R-/- mice in the VITI were unaffected by d-amphetamine, but premature responses were exacerbated by this psychostimulant. Omissions in the VITI were higher, overall, in the morning than the afternoon but, in the LITI, premature responses of NK1R-/- mice were higher in the afternoon than the morning. Conclusion In addition to locomotor hyperactivity, NK1R-/- mice express inattentiveness, perseveration and impulsivity in the 5-CSRTT, thereby matching core criteria for a model of ADHD. Because d-amphetamine reduced perseveration in NK1R-/- mice, this action does not require functional NK1R. However, the lack of any improvement of omissions and premature responses in NK1R-/- mice given d-amphetamine suggests that beneficial effects of this psychostimulant in other rodent models, and ADHD patients, need functional NK1R. Finally, our results reveal experimental variables (stimulus parameters, stress and time of day) that could influence translational studies

The high costs of conserving Southeast Asia\u27s lowland rainforests

Mechanisms that mitigate greenhouse-gas emissions via forest conservation have been portrayed as a cost-effective approach that can also protect biodiversity and vital ecosystem services. However, the costs of conservation - including opportunity costs - are spatially heterogeneous across the globe. The lowland rainforests of Southeast Asia represent a unique nexus of large carbon stores, imperiled biodiversity, large stores of timber, and high potential for conversion to oil-palm plantations, making this region one where understanding the costs of conservation is critical. Previous studies have underestimated the gap between conservation costs and conversion benefits in Southeast Asia. Using detailed logging records, cost data, and species-specific timber auction prices from Borneo, we show that the profitability of logging, in combination with potential profits from subsequent conversion to palm-oil production, greatly exceeds foreseeable revenues from a global carbon market and other ecosystem-service payment mechanisms. Thus, the conservation community faces a massive funding shortfall to protect the remaining lowland primary forests in Southeast Asia. © 2011 The Ecological Society of America

HIV Prevention in Care and Treatment Settings: Baseline Risk Behaviors among HIV Patients in Kenya, Namibia, and Tanzania.

HIV care and treatment settings provide an opportunity to reach people living with HIV/AIDS (PLHIV) with prevention messages and services. Population-based surveys in sub-Saharan Africa have identified HIV risk behaviors among PLHIV, yet data are limited regarding HIV risk behaviors of PLHIV in clinical care. This paper describes the baseline sociodemographic, HIV transmission risk behaviors, and clinical data of a study evaluating an HIV prevention intervention package for HIV care and treatment clinics in Africa. The study was a longitudinal group-randomized trial in 9 intervention clinics and 9 comparison clinics in Kenya, Namibia, and Tanzania (N = 3538). Baseline participants were mostly female, married, had less than a primary education, and were relatively recently diagnosed with HIV. Fifty-two percent of participants had a partner of negative or unknown status, 24% were not using condoms consistently, and 11% reported STI symptoms in the last 6 months. There were differences in demographic and HIV transmission risk variables by country, indicating the need to consider local context in designing studies and using caution when generalizing findings across African countries. Baseline data from this study indicate that participants were often engaging in HIV transmission risk behaviors, which supports the need for prevention with PLHIV (PwP). TRIAL REGISTRATION: ClinicalTrials.gov NCT01256463

Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium.

BACKGROUND: Invasive fungal diseases (IFDs) remain important causes of morbidity and mortality. The consensus definitions of the Infectious Diseases Group of the European Organization for Research and Treatment of Cancer and the Mycoses Study Group have been of immense value to researchers who conduct clinical trials of antifungals, assess diagnostic tests, and undertake epidemiologic studies. However, their utility has not extended beyond patients with cancer or recipients of stem cell or solid organ transplants. With newer diagnostic techniques available, it was clear that an update of these definitions was essential. METHODS: To achieve this, 10 working groups looked closely at imaging, laboratory diagnosis, and special populations at risk of IFD. A final version of the manuscript was agreed upon after the groups' findings were presented at a scientific symposium and after a 3-month period for public comment. There were several rounds of discussion before a final version of the manuscript was approved. RESULTS: There is no change in the classifications of "proven," "probable," and "possible" IFD, although the definition of "probable" has been expanded and the scope of the category "possible" has been diminished. The category of proven IFD can apply to any patient, regardless of whether the patient is immunocompromised. The probable and possible categories are proposed for immunocompromised patients only, except for endemic mycoses. CONCLUSIONS: These updated definitions of IFDs should prove applicable in clinical, diagnostic, and epidemiologic research of a broader range of patients at high-risk

A process pattern model for tackling and improving big data quality

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality

Overt Visual Attention as a Causal Factor of Perceptual Awareness

Our everyday conscious experience of the visual world is fundamentally shaped by the interaction of overt visual attention and object awareness. Although the principal impact of both components is undisputed, it is still unclear how they interact. Here we recorded eye-movements preceding and following conscious object recognition, collected during the free inspection of ambiguous and corresponding unambiguous stimuli. Using this paradigm, we demonstrate that fixations recorded prior to object awareness predict the later recognized object identity, and that subjects accumulate more evidence that is consistent with their later percept than for the alternative. The timing of reached awareness was verified by a reaction-time based correction method and also based on changes in pupil dilation. Control experiments, in which we manipulated the initial locus of visual attention, confirm a causal influence of overt attention on the subsequent result of object perception. The current study thus demonstrates that distinct patterns of overt attentional selection precede object awareness and thereby directly builds on recent electrophysiological findings suggesting two distinct neuronal mechanisms underlying the two phenomena. Our results emphasize the crucial importance of overt visual attention in the formation of our conscious experience of the visual world

Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry-based biomarker discovery has long been hampered by the difficulty in reconciling lists of discriminatory peaks identified by different laboratories for the same diseases studied. We describe a multi-statistical analysis procedure that combines several independent computational methods. This approach capitalizes on the strengths of each to analyze the same high-resolution mass spectral data set to discover consensus differential mass peaks that should be robust biomarkers for distinguishing between disease states.</p> <p>Results</p> <p>The proposed methodology was applied to a pilot narcolepsy study using logistic regression, hierarchical clustering, t-test, and CART. Consensus, differential mass peaks with high predictive power were identified across three of the four statistical platforms. Based on the diagnostic accuracy measures investigated, the performance of the consensus-peak model was a compromise between logistic regression and CART, which produced better models than hierarchical clustering and t-test. However, consensus peaks confer a higher level of confidence in their ability to distinguish between disease states since they do not represent peaks that are a result of biases to a particular statistical algorithm. Instead, they were selected as differential across differing data distribution assumptions, demonstrating their true discriminatory potential.</p> <p>Conclusion</p> <p>The methodology described here is applicable to any high-resolution MALDI mass spectrometry-derived data set with minimal mass drift which is essential for peak-to-peak comparison studies. Four statistical approaches with differing data distribution assumptions were applied to the same raw data set to obtain consensus peaks that were found to be statistically differential between the two groups compared. These consensus peaks demonstrated high diagnostic accuracy when used to form a predictive model as evaluated by receiver operating characteristics curve analysis. They should demonstrate a higher discriminatory ability as they are not biased to a particular algorithm. Thus, they are prime candidates for downstream identification and validation efforts.</p

Progression from new methicillin-resistant Staphylococcus aureus colonisation to infection: An observational study in a hospital cohort

10.1186/1471-2334-13-491BMC Infectious Diseases131-BIDM