Prof-in-a-Box: using internet-videoconferencing to assist students in the gross anatomy laboratory

BACKGROUND: The optimal learning environment for gross anatomy is the dissection laboratory. The Prof-in-a-Box (PiB) system has been developed where an anatomist using distance-learning technologies 'helps' students in a dissection laboratory at a different site. METHODS: The PiB system consists of: (1) an anatomist in his/her office with a computer and video camera; (2) a computer and 2 video cameras in the lab; (3) iChat AV software; (4) a secure server to host the PiB-student 'consultation'. The PiB system allows the students and faculty to interact via audio and video providing an environment where questions can be asked and answered and anatomical structures can be identified 'at a distance' in real-time. The PiB system was set up at a prosected cadaver and made available for student use during 'office hours'. RESULTS: 25–30% of the students used the PiB system. Anatomical structures were identified, questions answered and demonstrations given 'at a distance' using the system. Students completed an optional questionnaire about the PiB system at the end of the semester. Results of the questionnaire indicate that the students were enthusiastic about the PiB system and wanted its use to be expanded in the future. CONCLUSION: Many of the functions of a faculty member in the gross anatomy dissection laboratory can be performed 'at a distance' using the PiB system. This suggests that a geographically dispersed faculty could assist in providing instruction in the dissection labs at multiple medical schools without needing to be physically present

Seasonality of primary care utilization for respiratory diseases in Ontario: A time-series analysis

<p>Abstract</p> <p>Background</p> <p>Respiratory diseases represent a significant burden in primary care. Determining the temporal variation of the overall burden of respiratory diseases on the health care system and their potential causes are keys to understanding disease dynamics in populations and can contribute to the rational management of health care resources.</p> <p>Methods</p> <p>A retrospective, cross-sectional time series analysis was used to assess the presence and strength of seasonal and temporal patterns in primary care visits for respiratory diseases in Ontario, Canada, for a 10-year period from January 1, 1992 to December 31, 2002. Data were extracted from the Ontario Health Insurance Plan database for people who had diagnosis codes for chronic obstructive pulmonary disease, asthma, pneumonia, or upper respiratory tract infections.</p> <p>Results</p> <p>The results illustrate a clear seasonal pattern in visits to primary care physicians for all respiratory conditions, with a threefold increase in visits during the winter. Age and sex-specific rates show marked increases in visits of young children and in female adults. Multivariate time series methods quantified the interactions among primary care visits, and Granger causality criterion test showed that the respiratory syncytial virus (RSV) and influenza virus influenced asthma (p = 0.0060), COPD (p = 0.0038), pneumonia (p = 0.0001), and respiratory diseases (p = 0.0001).</p> <p>Conclusion</p> <p>Primary care visits for respiratory diseases have clear predictable seasonal patterns, driven primarily by viral circulations. Winter visits are threefold higher than summer troughs, indicating a short-term surge on primary health service demands. These findings can aid in effective allocation of resources and services based on seasonal and specific population demands.</p

Structural change and foreign direct investment : globalization and regional economic integration

This paper investigates flows of inward and outward foreign direct investment (FDI) and FDI-to-GDP ratios in a sample of 62 countries over a 30 year time span. Using several endogenous structural break procedures (allowing for one and two break points), we find that: (1) the great majority of the series have structural breaks in the last 15 years, (2) post-break FDI and FDI/GDP ratios are substantially higher than the pre-break values, and (3) most breaks seem to be related to globalization, regional economic integration, economic growth, or political instability. Static and dynamic panel-data analy- ses accounting for and/or addressing endogeneity, simultaneity, nonstationar- ity, heterogeneity and cross-sectional dependence show that FDI is negatively related to exchange rate volatility and GDP per capita, but positively related to some regional integration agreements, trade openness, GDP, and GDP growth. Most notably, the European Union is the only regional economic integration unit found to consistently have significant and positive effects on FDI.info:eu-repo/semantics/publishedVersio

Rhodium Nanoparticle Shape Dependence in the Reduction of NO by CO

The shape dependence of the catalytic reduction of nitric oxide by carbon monoxide on rhodium nanopolyhedra and nanocubes was studied from 230 to 270 degrees C. The nanocubes are found to exhibit higher turnover frequency and lower activation energy than the nanopolyhedra. These trends are compared to previous studies on Rh single crystals.Chemistry, PhysicalSCI(E)EI21ARTICLE3-4317-32213

A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation

Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition

Aβ42 Mutants with Different Aggregation Profiles Induce Distinct Pathologies in Drosophila

Aggregation of the amyloid-β-42 (Aβ42) peptide in the brain parenchyma is a pathological hallmark of Alzheimer's disease (AD), and the prevention of Aβ aggregation has been proposed as a therapeutic intervention in AD. However, recent reports indicate that Aβ can form several different prefibrillar and fibrillar aggregates and that each aggregate may confer different pathogenic effects, suggesting that manipulation of Aβ42 aggregation may not only quantitatively but also qualitatively modify brain pathology. Here, we compare the pathogenicity of human Aβ42 mutants with differing tendencies to aggregate. We examined the aggregation-prone, EOFAD-related Arctic mutation (Aβ42Arc) and an artificial mutation (Aβ42art) that is known to suppress aggregation and toxicity of Aβ42 in vitro. In the Drosophila brain, Aβ42Arc formed more oligomers and deposits than did wild type Aβ42, while Aβ42art formed fewer oligomers and deposits. The severity of locomotor dysfunction and premature death positively correlated with the aggregation tendencies of Aβ peptides. Surprisingly, however, Aβ42art caused earlier onset of memory defects than Aβ42. More remarkably, each Aβ induced qualitatively different pathologies. Aβ42Arc caused greater neuron loss than did Aβ42, while Aβ42art flies showed the strongest neurite degeneration. This pattern of degeneration coincides with the distribution of Thioflavin S-stained Aβ aggregates: Aβ42Arc formed large deposits in the cell body, Aβ42art accumulated preferentially in the neurites, while Aβ42 accumulated in both locations. Our results demonstrate that manipulation of the aggregation propensity of Aβ42 does not simply change the level of toxicity, but can also result in qualitative shifts in the pathology induced in vivo

A graphical vector autoregressive modelling approach to the analysis of electronic diary data

<p>Abstract</p> <p>Background</p> <p>In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied.</p> <p>Methods</p> <p>We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models.</p> <p>Results</p> <p>The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours.</p> <p>Conclusion</p> <p>The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.</p

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time

RUBY-1: a randomized, double-blind, placebo-controlled trial of the safety and tolerability of the novel oral factor Xa inhibitor darexaban (YM150) following acute coronary syndrome

AIMS: To establish the safety, tolerability and most promising regimen of darexaban (YM150), a novel, oral, direct factor Xa inhibitor, for prevention of ischaemic events in acute coronary syndrome (ACS). METHODS: In a 26-week, multi-centre, double-blind, randomized, parallel-group study, 1279 patients with recent high-risk non-ST-segment or ST-segment elevation ACS received one of six darexaban regimens: 5 mg b.i.d., 10 mg o.d., 15 mg b.i.d., 30 mg o.d., 30 mg b.i.d., or 60 mg o.d. or placebo, on top of dual antiplatelet treatment. Primary outcome was incidence of major or clinically relevant non-major bleeding events. The main efficacy outcome was a composite of death, stroke, myocardial infarction, systemic thromboembolism, and severe recurrent ischaemia. RESULTS: Bleeding rates were numerically higher in all darexaban arms vs. placebo (pooled HR: 2.275; 95% CI: 1.13–4.60, P = 0.022). Using placebo as reference (bleeding rate 3.1%), there was a dose–response relationship (P = 0.009) for increased bleeding with increasing darexaban dose (6.2, 6.5, and 9.3% for 10, 30, and 60 mg daily, respectively), which was statistically significant for 30 mg b.i.d. (P = 0.002). There was no decrease (indeed a numerical increase in the 30 and 60 mg dose arms) in efficacy event rates with darexaban, but the study was underpowered for efficacy. Darexaban showed good tolerability without signs of liver toxicity. CONCLUSIONS: Darexaban when added to dual antiplatelet therapy after ACS produces an expected dose-related two- to four-fold increase in bleeding, with no other safety concerns but no signal of efficacy. Establishing the potential of low-dose darexaban in preventing major cardiac events after ACS requires a large phase III trial. ClinicalTrials.gov Identifier: NCT0099429