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EEG data suggest that cognitive load may increase when contextual priors are explicitly provided, whereas self-report data suggested a decrease in cognitive load. The findings provide insight into the processing demands associated with integration of contextual priors and visual information during dynamic anticipation tasks, and have implications for the utility of priors under cognitively demanding conditions. Furthermore, our findings add to the existing literature, suggesting that continuous EEG may be a more valid measure than retrospective self-reports for in-task assessment of cognitive load. © 2020 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG. In an effort to achieve greater standardization of EEG preprocessing, and in particular, for the analysis of pediatric data, we developed the Maryland analysis of developmental EEG (MADE) pipeline as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings. MADE uses EEGLAB and functions from some EEGLAB plugins and includes additional customized features particularly useful for EEG data collected from pediatric populations. MADE processes event-related and resting state EEG from raw data files through a series of preprocessing steps and outputs processed clean data ready to be analyzed in time, frequency, or time-frequency domain. MADE provides a report file at the end of the preprocessing that describes a variety of features of the processed data to facilitate the assessment of the quality of processed data. In this article, we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues. MADE is freely available under the terms of the GNU General Public License at https//github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline. © 2020 Society for Psychophysiological Research.BACKGROUND In epidemics of highly infectious diseases, such as Ebola, severe acute respiratory syndrome (SARS), or coronavirus (COVID-19), healthcare workers (HCW) are at much greater risk of infection than the general population, due to their contact with patients' contaminated body fluids. Personal protective equipment (PPE) can reduce the risk by covering exposed body parts. It is unclear which type of PPE protects best, what is the best way to put PPE on (i.e. donning) or to remove PPE (i.e. doffing), and how to train HCWs to use PPE as instructed. OBJECTIVES To evaluate which type of full-body PPE and which method of donning or doffing PPE have the least risk of contamination or infection for HCW, and which training methods increase compliance with PPE protocols. SEARCH METHODS We searched CENTRAL, MEDLINE, Embase and CINAHL to 20 March 2020. SELECTION CRITERIA We included all controlled studies that evaluated the effect of full-body PPE used by HCW exposed to highly infectious diseases, on the risk of id for their risk of infection. Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.OBJECTIVE We aim to systematically review the clinical characteristics of Coronavirus disease 2019 (COVID-19). METHODS Seven datebases were searched to collect studies about the clinical characteristics of COVID-19 from 1 January 2020 to 28 February 2020. Then, meta-analysis was performed by using Stata12.0 software. RESULTS A total of 38 studies involving 3 062 COVID-19 patients were included. Meta-analysis showed that a higher proportion of infected patients were male (56.9%). The incidence rate of respiratory failure or ARDS was 19.5% and the fatality rate was 5.5%. Fever (80.4%), fatigue (46%), cough (63.1%) and expectoration (41.8%) were the most common clinical manifestations. Other common symptoms included muscle soreness (33%), anorexia (38.8%), chest tightness (35.7%), shortness of breath (35%), dyspnea (33.9%). Minor symptoms included nausea and vomiting (10.2%), diarrhea (12.9%), headache (15.4%), pharyngalgia(13.1%), shivering (10.9%) and abdominal pain (4.4%). Patients with asymptomatic was 11.9%. Normal leukocytes counts (69.7%), lymphopenia (56.5%), elevated C-reactive protein levels (73.6%), elevated ESR (65.6%) and oxygenation index decreased (63.6%) were observed in most patients. About 37.2% of patients with elevated D-dimer, 25.9% of patients with leukopenia, along with abnormal levels of liver function (29%) and renal function (25.5%). Other findings included leukocytosis (12.6%) and elevated procalcitonin (17.5%). Only 25.8% of patients had lesions involving single lung and 75.7% of patients had lesions involving bilateral lungs. CONCLUSIONS The most commonly experienced symptoms of COVID-19 patients were fever, fatigue, cough and expectoration. A relatively small percentage of patients were asymptomatic. Most patients showed normal leucocytes counts, lymphopenia, elevated levels of C-reactive protein and ESR. Bilateral lungs involvement was common. This article is protected by copyright. All rights reserved. This article is protected by copyright. selleck chemical All rights reserved.BACKGROUND Surgeons urgently need guidance on how to deliver surgical services safely and effectively during the COVID-19 pandemic. The aim was to identify the key domains that should be considered when developing pandemic preparedness plans for surgical services. METHODS A scoping search was conducted to identify published articles relating to management of surgical patients during pandemics. Key informant interviews were conducted with surgeons and anaesthetists with direct experience of working during infectious disease outbreaks, in order to identify key challenges and solutions to delivering effective surgical services during the COVID-19 pandemic. RESULTS Thirteen articles were identified from the scoping search, and surgeons and anaesthetists representing 11 territories were interviewed. To mount an effective response to COVID-19, a pandemic response plan for surgical services should be developed in advance. Key domains that should be included are provision of staff training (such as patient transfers, donning and doffing personal protection equipment, recognizing and managing COVID-19 infection); support for the overall hospital response to COVID-19 (reduction in non-urgent activities such as clinics, endoscopy, non-urgent elective surgery); establishment of a team-based approach for running emergency services; and recognition and management of COVID-19 infection in patients treated as an emergency and those who have had surgery.