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Showing papers by "University of Twente published in 2021"


Journal ArticleDOI
TL;DR: The advances that have enabled OoCs to demonstrate physiological relevance, and the challenges and opportunities that need to be tackled to tap the full potential of OoC utility for translational research are discussed.
Abstract: Organs-on-chips (OoCs), also known as microphysiological systems or ‘tissue chips’ (the terms are synonymous), have attracted substantial interest in recent years owing to their potential to be informative at multiple stages of the drug discovery and development process. These innovative devices could provide insights into normal human organ function and disease pathophysiology, as well as more accurately predict the safety and efficacy of investigational drugs in humans. Therefore, they are likely to become useful additions to traditional preclinical cell culture methods and in vivo animal studies in the near term, and in some cases replacements for them in the longer term. In the past decade, the OoC field has seen dramatic advances in the sophistication of biology and engineering, in the demonstration of physiological relevance and in the range of applications. These advances have also revealed new challenges and opportunities, and expertise from multiple biomedical and engineering fields will be needed to fully realize the promise of OoCs for fundamental and translational applications. This Review provides a snapshot of this fast-evolving technology, discusses current applications and caveats for their implementation, and offers suggestions for directions in the next decade. Organs-on-chips (OoCs) could be useful at various stages of drug discovery and development, providing insight regarding human organ physiology in both normal and disease contexts, as well as accurately predicting developmental drug safety and efficacy. This Review discusses the advances that have enabled OoCs to demonstrate physiological relevance, and the challenges and opportunities that need to be tackled to tap the full potential of OoC utility for translational research.

384 citations



Journal ArticleDOI
21 Jul 2021
TL;DR: In this paper, the authors conducted a systematic literature review and meta-analysis to assess the range of future global food security projections to 2050 and found that the total global food demand was expected to increase by 35% to 56% between 2010 and 2050, while population at risk of hunger is expected to change by −91% to +8%.
Abstract: Quantified global scenarios and projections are used to assess long-term future global food security under a range of socio-economic and climate change scenarios. Here, we conducted a systematic literature review and meta-analysis to assess the range of future global food security projections to 2050. We reviewed 57 global food security projection and quantitative scenario studies that have been published in the past two decades and discussed the methods, underlying drivers, indicators and projections. Across five representative scenarios that span divergent but plausible socio-economic futures, the total global food demand is expected to increase by 35% to 56% between 2010 and 2050, while population at risk of hunger is expected to change by −91% to +8% over the same period. If climate change is taken into account, the ranges change slightly (+30% to +62% for total food demand and −91% to +30% for population at risk of hunger) but with no statistical differences overall. The results of our review can be used to benchmark new global food security projections and quantitative scenario studies and inform policy analysis and the public debate on the future of food.

297 citations


Journal ArticleDOI
TL;DR: In the early 2000s, a subcommittee of the American Clinical Neurophysiology Society (ACNS) set out to standardize terminology of periodic and rhythmic EEG patterns in the critically ill to aid in future research involving such patterns as mentioned in this paper.
Abstract: In the early 2000s, a subcommittee of the American Clinical Neurophysiology Society (ACNS) set out to “standardize terminology of periodic and rhythmic EEG patterns in the critically ill to aid in future research involving such patterns.” The initial proposed terminology was published in 2005.1 This was presented at many meetings on several continents, subjected to multiple rounds of testing of interrater reliability, underwent many revisions, and was then published as an ACNS guideline in 2013.2 Interrater agreement of the 2012 version (published in early 2013) was very good, with almost perfect agreement for seizures, main terms 1 and 2, the +S modifier, sharpness, absolute amplitude, frequency, and number of phases.3 Agreement was substantial for the +F and +R modifiers (66% and 67%) but was only moderate for triphasic morphology (58%) and fair for evolution (21%, likely at least partly because of the short EEG samples provided).3 The authors concluded that interrater agreement for most terms in the ACNS critical care EEG terminology was high and that these terms were suitable for multicenter research on the clinical significance of these critical care EEG patterns. With the help of infrastructure funding from the American Epilepsy Society and administrative and website support from the ACNS, a database that incorporated the ACNS terminology was developed for clinical and research purposes, tested during routine clinical care in multiple centers,4 and made available at no cost on the ACNS website (https://www.acns.org/research/critical-care-eeg-monitoring-research-consortium-ccemrc/ccemrc-public-database). This greatly enhanced the ability to complete multicenter investigations. After the establishment of the standardized terminology and free access to a database incorporating these terms, there have been many investigations into the clinical significance of rhythmic and periodic patterns (RPPs) in critically ill patients. Patterns such as lateralized rhythmic delta activity (LRDA) were found to be highly associated with acute seizures,5,6 equivalent to the association found with lateralized periodic discharges (LPDs) in one study.5 The association of all the main patterns in the nomenclature with seizures was defined in a multicenter cohort of almost 5,000 patients, with seizure rates highest for LPDs, intermediate for LRDA and generalized periodic discharges (GPDs), and lowest for generalized rhythmic delta activity (GRDA).6 This and other studies have shown that several of the modifiers within the nomenclature do indeed have clinically relevant meaning. For example, studies have shown that higher frequency (especially >1.5 Hz), higher prevalence, longer duration, and having a “plus” modifier are all associated with a higher chance of acute seizures.6,7 On the other hand, whether a pattern was spontaneous or “stimulus-induced” did not seem to have a significant effect on its association with seizures.6 In other investigations, the “triphasic morphology” modifier was investigated blindly with multiple expert reviewers, calling into question its relationship with metabolic encephalopathy and its lack of a relationship with seizures.8,9 For patients with refractory status epilepticus treated with anesthetic-induced coma, the presence of “highly epileptiform” bursts suggested that an attempted wean off of anesthetics at that time was much more likely to lead to seizure recurrence than if the bursts were not highly epileptiform.10 Even long-term outcome seemed to be associated with some modifiers, with a higher risk of later epilepsy found if LPDs were more prevalent, had longer duration, or had a “plus” modifier.7

289 citations


Journal ArticleDOI
TL;DR: In this article, the authors present a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans, and analyze whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition.
Abstract: Cities across the globe recognise their role in climate mitigation and are acting to reduce carbon emissions. Knowing whether cities set ambitious climate and energy targets is critical for determining their contribution towards the global 1.5 °C target, partly because it helps to identify areas where further action is necessary. This paper presents a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans. The sample encompasses over 25% of the EU population and includes cities of all sizes across all Member States, plus the UK. The study analyses whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition. Results reveal that 78% of the cities have a GHG emissions reduction target. However, with an average target of 47%, European cities are not on track to reach the Paris Agreement: they need to roughly double their ambitions and efforts. Some cities are ambitious, e.g. 25% of our sample (81) aim to reach carbon neutrality, with the earliest target date being 2020.90% of these cities are members of the Climate Alliance and 75% of the Covenant of Mayors. City size is the strongest predictor for carbon neutrality, whilst climate network(s) membership, combining adaptation and mitigation into a single strategy, and local motivation also play a role. The methods, data, results and analysis of this study can serve as a reference and baseline for tracking climate mitigation ambitions across European and global cities.

227 citations


Journal ArticleDOI
TL;DR: In this article, the authors focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions.
Abstract: Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

224 citations


Journal ArticleDOI
TL;DR: The findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL.
Abstract: Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a burden throughout the population. To provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform and was promoted by thirty-five research organizations from Europe, North Africa, Western Asia and the Americas. Questions were presented in a differential format with questions related to responses “before” and “during” the confinement period. 1047 replies (54% women) from Western Asia (36%), North Africa (40%), Europe (21%) and other continents (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (+10% to +16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (+71.2%) inactivity, (ii) poor sleep quality (+12.8%), (iii) unhealthy diet behaviours (+10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (+15%) of technology during the confinement period. These findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL).

218 citations


Journal ArticleDOI
TL;DR: In this paper, a physically-based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models were used to answer the question to what extent the risk of these fires was exacerbated by anthropogenic climate change.
Abstract: . Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September–February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

173 citations


Journal ArticleDOI
TL;DR: In this paper, the authors focus on process planning including build orientation, slicing, and path planning, as well as the definition of process parameter selection from a single track to multi-track and multilayer, and finally geometric features from a thinwall to lattice structures with several case studies.

173 citations


Journal ArticleDOI
TL;DR: An overview of state-of-the-art 3D models for studying tumor-stroma interactions, with a focus on understanding why the TME is a key target in cancer therapy.
Abstract: The complex microenvironment in which malignant tumor cells grow is crucial for cancer progression. The physical and biochemical characteristics of this niche are involved in controlling cancer cell differentiation, proliferation, invasion, and metastasis. It is therefore essential to understand how cancer cells interact and communicate with their surrounding tissue – the so-called tumor stroma – and how this interplay regulates disease progression. To mimic the tumor microenvironment (TME), 3D in vitro models are widely used because they can incorporate different patient-derived tissues/cells and allow longitudinal readouts, thus permitting deeper understanding of cell interactions. These models are therefore excellent tools to bridge the gap between oversimplified 2D systems and unrepresentative animal models. We present an overview of state-of-the-art 3D models for studying tumor–stroma interactions, with a focus on understanding why the TME is a key target in cancer therapy.

167 citations


Journal ArticleDOI
23 Apr 2021
TL;DR: In this article, the authors present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks.
Abstract: Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks. A significant innovation is recognizing that risks can arise both from potential impacts due to climate change and from responses to climate change. This approach encourages thinking that traverses sectoral and regional boundaries and links physical and socio-economic drivers of risk. Advancing climate change risk assessment in these ways is essential for more informed decision making that reduces negative climate change impacts. © 2021 The Authors

Journal ArticleDOI
TL;DR: A comprehensive review of the microstructure and mechanical properties of nickel-based superalloys, manufactured using the two principle PBF techniques: Laser Powder Bed Fusion (LPBF) and Electron Beam Melting (EBM) is presented in this article.
Abstract: Powder Bed Fusion (PBF) techniques constitute a family of Additive Manufacturing (AM) processes, which are characterised by high design flexibility and no tooling requirement. This makes PBF techniques attractive to many modern manufacturing sectors (e.g. aerospace, defence, energy and automotive) where some materials, such as Nickel-based superalloys, cannot be easily processed using conventional subtractive techniques. Nickel-based superalloys are crucial materials in modern engineering and underpin the performance of many advanced mechanical systems. Their physical properties (high mechanical integrity at high temperature) make them difficult to process via traditional techniques. Consequently, manufacture of nickel-based superalloys using PBF platforms has attracted significant attention. To permit a wider application, a deep understanding of their mechanical behaviour and relation to process needs to be achieved. The motivation for this paper is to provide a comprehensive review of the mechanical properties of PBF nickel-based superalloys and how process parameters affect these, and to aid practitioners in identifying the shortcomings and the opportunities in this field. Therefore, this paper aims to review research contributions regarding the microstructure and mechanical properties of nickel-based superalloys, manufactured using the two principle PBF techniques: Laser Powder Bed Fusion (LPBF) and Electron Beam Melting (EBM). The ‘target’ microstructures are introduced alongside the characteristics of those produced by PBF process, followed by an overview of the most used building processes, as well as build quality inspection techniques. A comprehensive evaluation of the mechanical properties, including tensile strength, hardness, shear strength, fatigue resistance, creep resistance and fracture toughness of PBF nickel-based superalloys are analysed. This work concludes with summary tables for data published on these properties serving as a quick reference to scholars. Characteristic process factors influencing functional performance are also discussed and compared throughout for the purpose of identifying research opportunities and directing the research community toward the end goal of achieving part integrity that extends beyond static components only.

Journal ArticleDOI
TL;DR: This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses.

Journal ArticleDOI
TL;DR: In this article, a large-scale dataset of object detection in aerial images (DOTA) is presented, which contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images.
Abstract: In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.

DOI
01 Nov 2021
TL;DR: This article reported new twenty-first-century projections using ensembles of latest-generation crop and climate models, which suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble.
Abstract: Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated. Climate change affects agricultural productivity. New systematic global agricultural yield projections of the major crops were conducted using ensembles of the latest generation of crop and climate models. Substantial shifts in global crop productivity due to climate change will occur within the next 20 years—several decades sooner than previous projections—highlighting the need for targeted food system adaptation and risk management in the coming decades.

Journal ArticleDOI
TL;DR: Direct numerical simulations of a typical respiratory aerosol in a turbulent jet of the respiratory event within a Lagrangian-Eulerian approach with 5000 droplets are employed to better and fundamentally understand the environmental ambient conditions under which airborne transmission of the coronavirus is likely to occur, in order to be able to control and adapt them.
Abstract: To quantify the fate of respiratory droplets under different ambient relative humidities, direct numerical simulations of a typical respiratory event are performed. We found that, because small droplets (with initial diameter of $10\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$) are swept by turbulent eddies in the expelled humid puff, their lifetime gets extended by a factor of more than 30 times as compared to what is suggested by the classical picture by Wells, for 50% relative humidity. With increasing ambient relative humidity the extension of the lifetimes of the small droplets further increases and goes up to around 150 times for 90% relative humidity, implying more than 2 m advection range of the respiratory droplets within 1 sec. Employing Lagrangian statistics, we demonstrate that the turbulent humid respiratory puff engulfs the small droplets, leading to many orders of magnitude increase in their lifetimes, implying that they can be transported much further during the respiratory events than the large ones. Our findings provide the starting points for larger parameter studies and may be instructive for developing strategies on optimizing ventilation and indoor humidity control. Such strategies are key in mitigating the COVID-19 pandemic in the present autumn and upcoming winter.

Journal ArticleDOI
TL;DR: In this article, the authors present a systematic and comprehensive global stocktake of implemented human adaptation to climate change and identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses.
Abstract: Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses. Determining progress in adaptation to climate change is challenging, yet critical as climate change impacts increase. A stocktake of the scientific literature on implemented adaptation now shows that adaptation is mostly fragmented and incremental, with evidence lacking for its impact on reducing risk.

Posted ContentDOI
TL;DR: The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures.
Abstract: . In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements ( Dorigo et al. , 2011 b , a ) . The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal ( https://ismn.earth/en/ , last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Journal ArticleDOI
TL;DR: The COVID-19 home confinement led to impaired sleep quality, as evidenced by the increase in the global PSQI score and the number of hours of daily-sitting increased by ~2 hours/days during home confinement.
Abstract: Symptoms of psychological distress and disorder have been widely reported in people under quarantine during the COVID-19 pandemic;in addition to severe disruption of peoples’ daily activity and sleep patterns This study investigates the association between physical-activity levels and sleep patterns in quarantined individuals An international Google online survey was launched in April 6th, 2020 for 12-weeks Forty-one research organizations from Europe, North-Africa, Western-Asia, and the Americas promoted the survey through their networks to the general society, which was made available in 14 languages The survey was presented in a differential format with questions related to responses “before” and “during” the confinement period Participants responded to the Pittsburgh Sleep Quality Index (PSQI) questionnaire and the short form of the International Physical Activity Questionnaire 5056 replies (59 4% female), from Europe (46 4%), Western-Asia (25 4%), America (14 8%) and North-Africa (13 3%) were analysed The COVID-19 home confinement led to impaired sleep quality, as evidenced by the increase in the global PSQI score (4 37 ± 2 71 before home confinement vs 5 32 ± 3 23 during home confinement) (p <0 001) The frequency of individuals experiencing a good sleep decreased from 61% (n = 3063) before home confinement to 48% (n = 2405) during home confinement with highly active individuals experienced better sleep quality (p <0 001) in both conditions Time spent engaged in all physical-activity and the metabolic equivalent of task in each physical-activity category (i e , vigorous, moderate, walking) decreased significantly during COVID-19 home confinement (p <0 001) The number of hours of daily-sitting increased by ~2 hours/days during home confinement (p <0 001) COVID-19 home confinement resulted in significantly negative alterations in sleep patterns and physical-activity levels To maintain health during home confinement, physical-activity promotion and sleep hygiene education and support are strongly warranted © 2021 Institute of Sport All rights reserved


Journal ArticleDOI
TL;DR: In 2018, the 25th year of development of radar altimetry was celebrated and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences as discussed by the authors.

Journal ArticleDOI
TL;DR: It is concluded that this growth is not fully compensated by efficiency gains of data center technological innovations and the future impact of the data centers’ electricity consumption is vulnerable to behavioral usage trends.


Journal ArticleDOI
TL;DR: It is shown that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.
Abstract: Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ( $$c$$ -index $$\sim \,0.63$$ ), and in the case of XGB even better ( $$c$$ -index $$\sim 0.73$$ ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.


Journal ArticleDOI
TL;DR: In this article, the effect of surface texturing as dimples on the wear evolution of total hip arthroplasty was investigated by developing finite element analysis from the prediction model without dimples and with bottom profile dimples of flat, drill, and ball types.
Abstract: Wear and wear-induced debris is a significant factor in causing failure in implants. Reducing contact pressure by using a textured surface between the femoral head and acetabular cup is crucial to improving the implant’s life. This study presented the effect of surface texturing as dimples on the wear evolution of total hip arthroplasty. It was implemented by developing finite element analysis from the prediction model without dimples and with bottom profile dimples of flat, drill, and ball types. Simulations were carried out by performing 3D physiological loading of the hip joint under normal walking conditions. A geometry update was initiated based on the patient’s daily routine activities. Our results showed that the addition of dimples reduced contact pressure and wear. The bottom profile dimples of the ball type had the best ability to reduce wear relative to the other types, reducing cumulative linear wear by 24.3% and cumulative volumetric wear by 31% compared to no dimples. The findings demonstrated that surface texturing with appropriate dimple bottom geometry on a bearing surface is able to extend the lifetime of hip implants.

Journal ArticleDOI
TL;DR: In this paper, the progress, advantages, and shortcomings of emerging bio-fabrication techniques are highlighted in a review of biomaterials for tissue engineering applications, and the novel applications of biofabricated natural hydrogels in cardiac, neural, and bone tissue engineering are discussed.

Journal ArticleDOI
Toshi A. Furukawa1, Aya M Suganuma1, Edoardo G Ostinelli2, Gerhard Andersson3, Gerhard Andersson4, Christopher G. Beevers5, Jason Shumake5, Thomas Berger6, Florien W. Boele7, Claudia Buntrock8, Per Carlbring9, Isabella Choi10, Helen Christensen11, Andrew Mackinnon11, Jennifer Dahne12, Marcus J.H. Huibers13, David Daniel Ebert14, Louise Farrer15, Nicholas R. Forand16, Daniel R. Strunk17, Iony D. Ezawa17, Erik Forsell4, Viktor Kaldo4, Viktor Kaldo18, Anna C. M. Geraedts, Simon Gilbody19, Elizabeth Littlewood19, Sally Brabyn19, Heather D. Hadjistavropoulos20, Luke H. Schneider21, Robert Johansson9, Robin Maria Francisca Kenter22, Marie Kivi23, Cecilia Björkelund23, Annet Kleiboer13, Heleen Riper13, Jan Philipp Klein24, Johanna Schröder25, Björn Meyer, Steffen Moritz25, Lara Bücker25, Ove Lintvedt, Peter Johansson3, Johan Lundgren3, Jeannette Milgrom26, Alan W. Gemmill26, David C. Mohr27, Jesus Montero-Marin2, Javier García-Campayo28, Stephanie Nobis, Anna Carlotta Zarski8, Kathleen O’Moore11, Alishia D. Williams11, Jill M. Newby11, Sarah Perini29, Rachel Phillips30, Justine Schneider31, Wendy Theresia Maria Pots32, Nicole E. Pugh, Derek Richards33, Isabelle M. Rosso34, Scott L. Rauch34, Lisa Sheeber35, Jessica Smith30, Viola Spek, Victor J M Pop36, Burçin Ünlü, Kim M P van Bastelaar37, Sanne van Luenen38, Nadia Garnefski38, Vivian Kraaij38, Kristofer Vernmark3, Lisanne Warmerdam, Annemieke van Straten13, Pavle Zagorscak39, Christine Knaevelsrud39, Manuel Heinrich39, Clara Miguel13, Andrea Cipriani2, Andrea Cipriani40, Orestis Efthimiou2, Orestis Efthimiou6, Eirini Karyotaki41, Pim Cuijpers13 
TL;DR: In this paper, a systematic review and individual participant data component network meta-analysis (cNMA) of Internet cognitive behavioural therapy (iCBT) trials for depression was conducted, which revealed potentially helpful, less helpful or harmful components and delivery formats for iCBT packages.

Journal ArticleDOI
TL;DR: The current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations are analyzed and the general guidances on creating benchmark datasets in efficient manners are presented.
Abstract: The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0 ), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors explored the variations in the size of the COVID-19 confirmed case clusters across the urban district Huangzhou in the city of Huanggang, and investigated the hypothetic relationships between built environment attributes and clusters of COVID19 cases with the structural equation model.