Background: Asthma and atopic dermatitis are common allergic conditions that contribute to substantial health loss, economic burden, and pain across individuals of all ages worldwide. Therefore, as a component of the ...
Background: Asthma and atopic dermatitis are common allergic conditions that contribute to substantial health loss, economic burden, and pain across individuals of all ages worldwide. Therefore, as a component of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we present updated estimates of the prevalence, disability-adjusted life-years (DALYs), incidence, and deaths due to asthma and atopic dermatitis and the burden attributable to modifiable risk factors, with forecasted prevalence up to 2050. Methods: Asthma and atopic dermatitis prevalence, incidence, DALYs, and mortality, with corresponding 95% uncertainty intervals (UIs), were estimated for 204 countries and territories from 1990 to 2021. A systematic review identified data from 389 sources for asthma and 316 for atopic dermatitis, which were further pooled using the Bayesian meta-regression tool. We also described the age-standardised DALY rates of asthma attributable to four modifiable risk factors: high BMI, occupational asthmagens, smoking, and nitrogen dioxide pollution. Furthermore, as a secondary analysis, prevalence was forecasted to 2050 using the Socio-demographic Index (SDI), air pollution, and smoking as predictors for asthma and atopic dermatitis. To assess trends in the burden of asthma and atopic dermatitis before (2010–19) and during (2019–21) the COVID-19 pandemic, we compared their average annual percentage changes (AAPCs). Findings: In 2021, there were an estimated 260 million (95% UI 227–298) individuals with asthma and 129 million (124–134) individuals with atopic dermatitis worldwide. Asthma cases declined from 287 million (250–331) in 1990 to 238 million (209–272) in 2005 but increased to 260 million in 2021. Atopic dermatitis cases consistently rose from 107 million (103–112) in 1990 to 129 million (124–134) in 2021. However, age-standardised prevalence rates decreased—by 40·0% (from 5568·3 per 100 000 to 3340·1 per 100 000) for asthma and 8·3% (from 1885·4
Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (F...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a patternrecognition based label fusion framework. T...
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Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in ...
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Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing
Optical Coherence Tomography Angiography (OCTA) is a general method to visualize blood flow in biological tissue. Despite its good results in practice, the commonly used Amplitude Decorrelation OCTA (AD-OCTA) measure ...
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Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and ba...
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The EEG is a measure of voltage as a function of time. The voltage of the EEG regulates its amplitude (measured from peak to peak). EEG amplitudes in the cortex range start from 500 to 1500 μV, but the amplitude...
Intensity modulated radiation therapy technology (IMRT) is one of the main approaches in cancer treatment because it can guarantee the killing of cancer cells while optimally protecting normal tissue from complication...
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