Post-synthetic derivatization of graphitic carbon nitride with methanesulfonyl chloride: Synthesis, characterization and photocatalysis

Bulk graphitic carbon nitride (CN) was synthetized by heating of melamine at 550 degrees C, and the exfoliated CN (ExCN) was prepared by heating of CN at 500 degrees C. Sulfur-doped CN was synthesized by heating of thiourea (S-CN) and by a novel procedure based on the post-synthetic derivatization of CN with methanesulfonyl (CH3SO2-) chloride (Mes-CN and Mes-ExCN). The obtained nanomaterials were investigated by common characterization methods and their photocatalytic activity was tested by means of the decomposition of acetic orange 7 (AO7) under ultraviolet A (UVA) irradiation. The content of sulfur in the modified CN decreased in the sequence of Mes-ExCN > Mes-CN > S-CN. The absorption of light decreased in the opposite manner, but no influence on the band gap energies was observed. The methanesulfonyl (mesyl) groups connected to primary and secondary amine groups were confirmed by high resolution mass spectrometry (HRMS). The photocatalytic activity decreased in the sequence of Mes-ExCN > ExCN > CN approximate to Mes-CN > S-CN. The highest activity of Mes-ExCN and ExCN was explained by the highest amounts of adsorbed Acetic Orange 7 (AO7). In addition, in the case of Mes-ExCN, chloride ions incorporated in the CN lattice enhanced the photocatalytic activity as well.Web of Science102art. no. 19

Photochemical Functionalization of Helicenes

Herein, a visible-light photochemical approach for practical helicene functionalization at very mild reaction conditions is described. The photochemical reactions allow for the regiospecific and innate late-stage functionalization of helicenes and are easily executed either through the activation of C(sp(2))-Br bonds in helicenes using K2CO3 as inorganic base or direct C(sp(2))-H helicene bond functionalization under oxidative photoredox reaction conditions. Overall, using these transformations six different functional groups are introduced to the helicene scaffold through C-C and four different C-heteroatom bond-forming reactions

Bivariate phase-rectified signal averaging

Phase-Rectified Signal Averaging (PRSA) was shown to be a powerful tool for the study of quasi-periodic oscillations and nonlinear effects in non-stationary signals. Here we present a bivariate PRSA technique for the study of the inter-relationship between two simultaneous data recordings. Its performance is compared with traditional cross-correlation analysis, which, however, does not work well for non-stationary data and cannot distinguish the coupling directions in complex nonlinear situations. We show that bivariate PRSA allows the analysis of events in one signal at times where the other signal is in a certain phase or state; it is stable in the presence of noise and impassible to non-stationarities.Comment: 19 pages, 6 figures, revised version submitted to Physica

Framing the Issues: Moral Distress in Health Care

Moral distress in health care has been identified as a growing concern and a focus of research in nursing and health care for almost three decades. Researchers and theorists have argued that moral distress has both short and long-term consequences. Moral distress has implications for satisfaction, recruitment and retention of health care providers and implications for the delivery of safe and competent quality patient care. In over a decade of research on ethical practice, registered nurses and other health care practitioners have repeatedly identified moral distress as a concern and called for action. However, research and action on moral distress has been constrained by lack of conceptual clarity and theoretical confusion as to the meaning and underpinnings of moral distress. To further examine these issues and foster action on moral distress, three members of the University of Victoria/University of British Columbia (UVIC/UVIC) nursing ethics research team initiated the development and delivery of a multi-faceted and interdisciplinary symposium on Moral Distress with international experts, researchers, and practitioners. The goal of the symposium was to develop an agenda for action on moral distress in health care. We sought to develop a plan of action that would encompass recommendations for education, practice, research and policy. The papers in this special issue of HEC Forum arose from that symposium. In this first paper, we provide an introduction to moral distress; make explicit some of the challenges associated with theoretical and conceptual constructions of moral distress; and discuss the barriers to the development of research, education, and policy that could, if addressed, foster action on moral distress in health care practice. The following three papers were written by key international experts on moral distress, who explore in-depth the issues in three arenas: education, practice, research. In the fifth and last paper in the series, we highlight key insights from the symposium and the papers in the series, propose to redefine moral distress, and outline directions for an agenda for action on moral distress in health care

D-cycloserine augmentation of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders: a systematic review and meta-analysis of individual participant data

Importance: Whether and under which conditions D-cycloserine (DCS) augments the effects of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders is unclear. Objective: To clarify whether DCS is superior to placebo in augmenting the effects of cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders and to evaluate whether antidepressants interact with DCS and the effect of potential moderating variables. Data Sources: PubMed, EMBASE, and PsycINFO were searched from inception to February 10, 2016. Reference lists of previous reviews and meta-analyses and reports of randomized clinical trials were also checked. Study Selection: Studies were eligible for inclusion if they were (1) double-blind randomized clinical trials of DCS as an augmentation strategy for exposure-based cognitive behavior therapy and (2) conducted in humans diagnosed as having specific phobia, social anxiety disorder, panic disorder with or without agoraphobia, obsessive-compulsive disorder, or posttraumatic stress disorder. Data Extraction and Synthesis: Raw data were obtained from the authors and quality controlled. Data were ranked to ensure a consistent metric across studies (score range, 0-100). We used a 3-level multilevel model nesting repeated measures of outcomes within participants, who were nested within studies. Results: Individual participant data were obtained for 21 of 22 eligible trials, representing 1047 of 1073 eligible participants. When controlling for antidepressant use, participants receiving DCS showed greater improvement from pretreatment to posttreatment (mean difference, -3.62; 95% CI, -0.81 to -6.43; P = .01; d = -0.25) but not from pretreatment to midtreatment (mean difference, -1.66; 95% CI, -4.92 to 1.60; P = .32; d = -0.14) or from pretreatment to follow-up (mean difference, -2.98, 95% CI, -5.99 to 0.03; P = .05; d = -0.19). Additional analyses showed that participants assigned to DCS were associated with lower symptom severity than those assigned to placebo at posttreatment and at follow-up. Antidepressants did not moderate the effects of DCS. None of the prespecified patient-level or study-level moderators was associated with outcomes. Conclusions and Relevance: D-cycloserine is associated with a small augmentation effect on exposure-based therapy. This effect is not moderated by the concurrent use of antidepressants. Further research is needed to identify patient and/or therapy characteristics associated with DCS response.2018-05-0

Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments

Projections of discharge are key for future water resources management. These projections are subject to uncertainties, which are difficult to handle in the decision process on adaptation strategies. Uncertainties arise from different sources such as the emission scenarios, the climate models and their post-processing, the hydrological models and natural variability. Here we present a detailed and quantitative uncertainty assessment, based on recent climate scenarios for Switzerland (CH2011 data set) and covering catchments representative for mid-latitude alpine areas. This study relies on a particularly wide range of discharge projections resulting from the factorial combination of 3 emission scenarios, 10 to 20 regional climate models, 2 post-processing methods and 3 hydrological models of different complexity. This enabled us to decompose the uncertainty in the ensemble of projections using analyses of variance (ANOVA). We applied the same modeling setup to 6 catchments to assess the influence of catchment characteristics on the projected streamflow and focused on changes in the annual discharge cycle. The uncertainties captured by our setup originate mainly from the climate models and natural climate variability, but the choice of emission scenario plays a large role by the end of the century. The respective contribution of the different sources of uncertainty varied strongly among the catchments. The discharge changes were compared to the estimated natural decadal variability, which revealed that a climate change signal emerges even under the lowest emission scenario (RCP2.6) by the end of the century. Limiting emissions to RCP2.6 levels would nevertheless reduce the largest regime changes at the end of the 21st century by approximately a factor of two, in comparison to impacts projected for the high emission scenario SRES A2. We finally show that robust regime changes emerge despite the projection uncertainty. These changes are significant and are consistent across a wide range of scenarios and catchments. We propose their identification as a way to aid decision-making under uncertainty

Dopamine Agonists and their risk to induce psychotic episodes in Parkinson's disease: a case-control study

<p>Abstract</p> <p>Background</p> <p>Psychosis is rare in untreated patients with Parkinson's disease (PD) but the prevalence rises to 40% during dopaminergic treatment. So far, no systematic comparison of the psychogenic potential of different dopaminergic drugs had been performed.</p> <p>Methods</p> <p>Eighty PD patients with psychotic episodes were compared to an age-matched control group of PD patients without psychotic episodes (n = 120) in a cross-sectional retrospective study.</p> <p>Results</p> <p>We found a positive correlation between psychotic episodes and dementia, number of concomitant medication, and pergolide intake. Odds ratio calculation confirmed the association with dementia. With respect to dopaminergic treatment, pergolide showed the highest odds ratio, levodopa the lowest. An adjusted logistic regression model confirmed the strong association with psychotic episodes and pergolide and no association with levodopa (adjusted odds ratio 2.01 and 0.11, respectively).</p> <p>Conclusion</p> <p>The analysis indicates that dementia and concomitant medication are factors in PD associated with psychotic symptoms. Furthermore, different dopaminergic drugs showed markedly different associations with psychotic symptoms</p

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them