Erratum:Influenza as a molecular walker (Chemical Science (2020) 11 (27–36) DOI: 10.1039/c9sc05149j)

The authors regret that incorrect details were given for ref. 70 in the original article. The correct version of ref. 70 is given below as ref. 1. The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.</p

Induced Förster resonance energy transfer by encapsulation of DNA-scaffold based probes inside a plant virus based protein cage

Insight into the assembly and disassembly of viruses can play a crucial role in developing cures for viral diseases. Specialized fluorescent probes can benefit the study of interactions within viruses, especially during cell studies. In this work, we developed a strategy based on Förster resonance energy transfer (FRET) to study the assembly of viruses without labeling the exterior of viruses. Instead, we exploit their encapsulation of nucleic cargo, using three different fluorescent ATTO dyes linked to single-stranded DNA oligomers, which are hybridised to a longer DNA strand. FRET is induced upon assembly of the cowpea chlorotic mottle virus, which forms monodisperse icosahedral particles of about 22 nm, thereby increasing the FRET efficiency by a factor of 8. Additionally, encapsulation of the dyes in virus-like particles induces a two-step FRET. When the formed constructs are disassembled, this FRET signal is fully reduced to the value before encapsulation. This reversible behavior makes the system a good probe for studying viral assembly and disassembly. It, furthermore, shows that multi-component supramolecular materials are stabilized in the confinement of a protein cage.</p

High frequency repetitive transcranial magnetic stimulation over the motor cortex: No diagnostic value for narcolepsy/cataplexy

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A grounded theory study on the influence of sleep on Parkinson’s symptoms

Contains fulltext : 167717.pdf (publisher's version ) (Open Access)BACKGROUND: Upon awaking, many Parkinson's patients experience an improved mobility, a phenomenon known as 'sleep benefit'. Despite the potential clinical relevance, no objective correlates of sleep benefit exist. The discrepancy between the patients' subjective experience of improvement in absence of objective changes is striking, and raises questions about the nature of sleep benefit. We aimed to clarify what patients reporting subjective sleep benefit, actually experience when waking up. Furthermore, we searched for factors associated with subjective sleep benefit. METHODS: Using a standardized topic list, we interviewed 14 Parkinson patients with unambiguous subjective sleep benefit, selected from a larger questionnaire-based cohort. A grounded theory approach was used to analyse the data. RESULTS: A subset of the participants described a temporary decrease in their Parkinson motor symptoms after sleep. Others did experience beneficial effects which were, however, non-specific for Parkinson's disease (e.g. feeling 'rested'). The last group misinterpreted the selection questionnaire and did not meet the definition of sleep benefit for various reasons. There were no general sleep-related factors that influenced the presence of sleep benefit. Factors mentioned to influence functioning at awakening were mostly stress related. CONCLUSIONS: The group of participants convincingly reporting sleep benefit in the selection questionnaire appeared to be very heterogeneous, with only a portion of them describing sleep benefit on motor symptoms. The group of participants actually experiencing motor sleep benefit may be much smaller than reported in the literature so far. Future studies should employ careful inclusion criteria, which could be based on our reported data

PerSleep:A Visual Analytics Approach for Performance Assessment of Sleep Staging Models

Machine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

OBJECTIVE: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate and control the RSA. These methods are also compared and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. METHODS: A simulation model is used to create a dataset of heart rate variability and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in a real-life application, regression models trained on the simulated data are used to map the estimates to the same measurement scale. RESULTS AND CONCLUSION: RSA estimates based on cross entropy, time-frequency coherence and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. SIGNIFICANCE: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing and newly proposed RSA estimates. It is freely accessible online