To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

• Publikační typ
• časopisecké články MeSH

• MeSH
• informační systémy MeSH
• internet využití   MeSH
• počítačem řízená výuka * metody   trendy   MeSH
• programovaná výuka jako téma MeSH
• sociální sítě MeSH
• studium lékařství metody   MeSH
• výuka - hodnocení metody   MeSH
• využití lékařské informatiky MeSH
• Publikační typ
• práce podpořená grantem MeSH
• Geografické názvy
• Slovenská republika MeSH

The Chinese Government quarantined Wuhan on 23 January 2020 and thereafter the Hubei province, affecting a total of 59 million citizens, to cease the spread of the coronavirus disease in 2019 (COVID-19). The effects of this lockdown on the psychological and mental health of both the affected and unaffected Chinese are largely unknown currently. We utilized one of the largest crowdsourced databases (Sleep as Android) that consisted of 15,681 sleep records from 563 users in China to estimate the change in the sleep pattern of Chinese users during the span of 30 December 2019 to 8 March 2020 with reference to 64,378 sleep records of 1,628 users for the same calendar period of years 2011-2019. The sleep pattern in China changed drastically after 23 January 2020 when the law of quarantine and suspension of Wuhan became effective. The two major findings are: (1) Chinese people increased their sleep duration by an average of 20 min and delayed their sleep onset by an average of 30 min at weekdays, while they maintained a similar sleep duration at weekends, and (2) larger changes were found in several subgroups, including those in Wuhan (80 sleep records from 3 users), female subjects, and those aged ≤ 24 years. Overall, Chinese people slept later and longer than usual during the COVID-19 pandemic quarantine.

• MeSH
• bdění * MeSH
• Betacoronavirus metabolismus   MeSH
• chytrý telefon MeSH
• cirkadiánní rytmus fyziologie   MeSH
• COVID-19 MeSH
• crowdsourcing * MeSH
• duševní zdraví MeSH
• epidemický výskyt choroby MeSH
• karanténa psychologie   MeSH
• koronavirové infekce patofyziologie   virologie   MeSH
• lidé MeSH
• pandemie MeSH
• poruchy spánku a bdění epidemiologie   MeSH
• SARS-CoV-2 MeSH
• spánek fyziologie   MeSH
• virová pneumonie patofyziologie   virologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Čína MeSH

BACKGROUND: To stop the spread of the new coronavirus disease in 2019 (COVID-19), many countries had completely locked down. This lockdown restricted the everyday life of the affected residents and changed their mobility pattern, but its effects on sleep pattern were largely unknown. METHODS: Here, utilizing one of the largest crowdsourced database (Sleep as Android), we analyzed the sleep pattern of 25 217 users with 1 352 513 sleep records between 1 January and 29 April 2020 in the US and 16 European countries (Germany, UK, Spain, France, Italy, The Netherlands, Belgium, Hungary, Denmark, Finland, Norway, Czech, Sweden, Austria, Poland and Switzerland) with more than 100 records in all days of 2020. RESULTS: During the COVID-19 pandemic, the sleeping pattern before and after the country-level lockdown largely differed. The subjects increased their sleep duration by an average of 11.3 to 18.6 min on weekday nights, except Denmark (4.9 min) and Finland (7.1 min). In addition, subjects form all 16 European countries delayed their sleep onset from 10.7 min (Sweden) to 29.6 min (Austria). CONCLUSION: During the COVID-19 pandemic, residents in the US and 16 European countries delayed their bedtime and slept longer than usual.

• MeSH
• chytrý telefon MeSH
• COVID-19 komplikace   epidemiologie   psychologie   MeSH
• crowdsourcing * MeSH
• dospělí MeSH
• duševní zdraví * MeSH
• epidemický výskyt choroby prevence a kontrola   MeSH
• karanténa psychologie   MeSH
• kontrola infekčních nemocí MeSH
• lidé MeSH
• mladiství MeSH
• pandemie MeSH
• poruchy spánku a bdění epidemiologie   MeSH
• SARS-CoV-2 * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• spánek fyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH

Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.

• MeSH
• algoritmy * MeSH
• crowdsourcing MeSH
• elektroencefalografie MeSH
• elektrokortikografie metody   MeSH
• epilepsie parciální diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• prediktivní hodnota testů MeSH
• refrakterní epilepsie diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• spánek MeSH
• strojové učení MeSH
• studie proveditelnosti MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

• MeSH
• algoritmy MeSH
• crowdsourcing metody   MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• epilepsie patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mozek diagnostické zobrazování   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• předpověď metody   MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Crisis mapping is a legitimate component of both crisis informatics and disaster risk management. It has become an effective tool for humanitarian workers, especially after the earthquake in Haiti in 2010. Ushahidi is among the many mapping platforms on offer in the growing field of crisis mapping, and involves the application of crowdsourcing to create online and interactive maps of areas in turmoil. This paper presents the Crisis Map of the Czech Republic, which is the first such instrument to be deployed nationwide in Central Europe. It describes the methodologies used in the preparatory work phase and details some practices identified during the creation and actual employment of the map. In addition, the paper assesses its structure and technological architecture, as well as its potential possible development in the future. Lastly, it evaluates the utilisation of the Crisis Map during the floods in the Czech Republic in 2013.

• MeSH
• katastrofy * MeSH
• lidé MeSH
• plánování postupu v případě katastrof metody   MeSH
• záplavy MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

• MeSH
• celosvětové zdraví * MeSH
• lidé MeSH
• prostorová analýza MeSH
• software MeSH
• velkoměsta MeSH
• zdravotní stav * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, P.H.S. MeSH
• Geografické názvy
• velkoměsta MeSH

This paper presents the results of a consensus-driven process identifying 50 priority research questions for historical ecology obtained through crowdsourcing, literature reviews, and in-person workshopping. A deliberative approach was designed to maximize discussion and debate with defined outcomes. Two in-person workshops (in Sweden and Canada) over the course of two years and online discussions were peer facilitated to define specific key questions for historical ecology from anthropological and archaeological perspectives. The aim of this research is to showcase the variety of questions that reflect the broad scope for historical-ecological research trajectories across scientific disciplines. Historical ecology encompasses research concerned with decadal, centennial, and millennial human-environmental interactions, and the consequences that those relationships have in the formation of contemporary landscapes. Six interrelated themes arose from our consensus-building workshop model: (1) climate and environmental change and variability; (2) multi-scalar, multi-disciplinary; (3) biodiversity and community ecology; (4) resource and environmental management and governance; (5) methods and applications; and (6) communication and policy. The 50 questions represented by these themes highlight meaningful trends in historical ecology that distill the field down to three explicit findings. First, historical ecology is fundamentally an applied research program. Second, this program seeks to understand long-term human-environment interactions with a focus on avoiding, mitigating, and reversing adverse ecological effects. Third, historical ecology is part of convergent trends toward transdisciplinary research science, which erodes scientific boundaries between the cultural and natural.

• MeSH
• biodiverzita MeSH
• dějiny 20. století MeSH
• dějiny 21. století MeSH
• dějiny starověku MeSH
• dějiny středověku MeSH
• ekologie dějiny   trendy   MeSH
• ekosystém MeSH
• kulturní antropologie dějiny   trendy   MeSH
• lidé MeSH
• přírodopis trendy   MeSH
• výzkumný projekt MeSH
• Check Tag
• dějiny 20. století MeSH
• dějiny 21. století MeSH
• dějiny starověku MeSH
• dějiny středověku MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• historické články MeSH
• Geografické názvy
• Kanada MeSH
• Švédsko MeSH

BACKGROUND: In this priority-setting exercise, we sought to identify leading research priorities needed for strengthening future pandemic preparedness and response across countries. METHODS: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals. RESULTS: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences. CONCLUSIONS: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.

• MeSH
• COVID-19 * epidemiologie   prevence a kontrola   MeSH
• dítě MeSH
• konsensus MeSH
• lidé MeSH
• připravenost na pandemii * MeSH
• výzkumný projekt MeSH
• zdraví dítěte MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH