Cancers represent a challenging public health threat in Asia. This study examines the temporal patterns of incidence, mortality, disability and risk factors of 29 cancers in Asia in the last three ...decades.
The age, sex and year-wise estimates of incidence, mortality, and disability-adjusted life years (DALYs) of 29 cancers for 49 Asian countries from 1990 through 2019 were generated as a part of the Global Burden of Disease, Injuries and Risk Factors 2019 study. Besides incidence, mortality and DALYs, we also examined the cancer burden measured in terms of DALYs and deaths attributable to risk factors, which had evidence of causation with different cancers. The development status of countries was measured using the socio-demographic index. Decomposition analysis was performed to gauge the change in cancer incidence between 1990 and 2019 due to population growth, aging and age-specific incidence rates.
All cancers combined claimed an estimated 5.6 million 95% uncertainty interval, 5.1–6.0 million lives in Asia with 9.4 million 8.6–10.2 million incident cases and 144.7 million 132.7–156.5 million DALYs in 2019. The age-standardized incidence rate (ASIR) of all cancers combined in Asia was 197.6/100,000 181.0–214.4 in 2019, varying from 99.2/100,000 76.1–126.0 in Bangladesh to 330.5/100,000 298.5–365.8 in Cyprus. The age-standardized mortality rate (ASMR) was 120.6/100,000 110.1–130.7 in 2019, varying 4-folds across countries from 71.0/100,000 59.9–83.5 in Kuwait to 284.2/100,000 229.2–352.3 in Mongolia. The age-standardized DALYs rate was 2970.5/100,000 2722.6–3206.5 in 2019, varying from 1578.0/100,000 1341.2–1847.0 in Kuwait to 6574.4/100,000 5141.7–8333.0 in Mongolia. Between 1990 and 2019, deaths due to 17 of the 29 cancers either doubled or more, and 20 of the 29 cancers underwent an increase of 150% or more in terms of new cases. Tracheal, bronchus, and lung cancer (both sexes), breast cancer (among females), colon and rectum cancer (both sexes), stomach cancer (both sexes) and prostate cancer (among males) were among top-5 cancers in most Asian countries in terms of ASIR and ASMR in 2019 and cancers of liver, stomach, hodgkin lymphoma and esophageal cancer posted the most significant decreases in age-standardized rates between 1990 and 2019. Among the modifiable risk factors, smoking, alcohol use, ambient particulate matter (PM) pollution and unsafe sex remained the dominant risk factors between 1990 and 2019. Cancer DALYs due to ambient PM pollution, high body mass index and fasting plasma glucose has increased most notably between 1990 and 2019.
With growing incidence, cancer has become more significant public health threat in Asia, demanding urgent policy attention and guidance. Its heightened risk calls for increased cancer awareness, preventive measures, affordable early-stage detection, and cost-effective therapeutics in Asia. The current study can serve as a useful resource for policymakers and researchers in Asia for devising interventions for cancer management and control.
The GBD study is funded by the Bill and Melinda Gates Foundation.
In this letter, since these methods are able to process signals locally, two spatial frequency analyses including windowed Fourier transform and wavelet transform are used to reduce synthetic ...aperture radar interferometric phase noise.
In this paper, a novel hyperspectral image clustering procedure, which is based upon the Fully Constrained Least Squares (FCLS) spectral unmixing method, is proposed. The proposed clustering method ...consists of three major steps: endmember extraction, unmixing procedure and hardening process via the winner-takes-all approach. To estimate the optimal number of endmembers, instead of using the background signal subspace identification methods, the number of endmembers is varied in a predefined interval and the commonly accepted VCA (Vertex Component Analysis) algorithm is employed to extract the endmembers' spectra. At each iteration, the bandwise Root Mean Square Error (RMSE) between the reconstructed image, obtained from estimated fractions. and the original image is computed and the mean of all bandwise RMSEs is regarded as a measure to choose the optimum number of endmembers. Experiments conducted on the Indian Pines challenging dataset proved the superiority of proposed method over the K-Means and Fuzzy c-Means methods in terms of the widely used Adjusted Rand Index measure.
PS Interferometry overcomes the temporal and geometrical decorrelation limitations through selection of so-called Persistent Scatterers (PS) with coherent scattering behavior over time using a large ...stack of interferometric SAR image. In Stanford Method for Persistent Scatterers, StaMPS, the deformation can be extracted even in areas lacking the corner reflectors, but the high subsidence rates lead to significant phase unwrapping errors. Therefore, in this paper we present an improved algorithm based on StaMPS in order to estimate deformation rate in rural area undergoing high and nearly constant deformation rate. In this approach, the linear deformation is approximated using Periodogram method and is subtracted in order to eliminate the unwrapping error. The approach is applied to the ENVISAT ASAR images of Tehran Southwestern basin and the results is compared with leveling measurements demonstrating the high performance of the proposed algorithm.
The various applications have been promised by the new generation of the spacecraft SAR data (i.e. Radarsat2 and TerraSAR-X), as the classification, the decomposition, and the modelling of the ...polarimetric synthetic aperture radar (SAR) data has been improved in recent years. This work is based on the fact that in order to extract the various patterns in distinctive field of studies, all the scattering matrix components are informative source of data. Different cross products of the complex scattering matrix channels (HH, HV, VH, and VV) that are involved in the phase and amplitude information are joined together to build instructive features. In the vector space, Fisher class separability algorithm will be tested, and the features with the best class separability, large distance between classes, and small within-class variances will be selected. As we measured the classification effectiveness of the individual features, we needed to choose a subset of the informative features from the nine originally available features. Finally, we combined all the educational information contents in order to classify desired images with the best overall accuracy.
An Innovative Image Space Clustering Technique for Automatic Road Network Vectorization Mokhtarzade, M; M.J. Valadan ZoejauthorGeodesy and Geomatics, K.N Toosi University of Technology, ValiAsr Street, Mirdamad Cross, Tehran, Iran 1996715433; H. EbadiauthorGeodesy and Geomatics, K.N Toosi University of Technology, ValiAsr Street, Mirdamad Cross, Tehran, Iran 1996715433 ...
2015
Journal Article
Nowadays different countries have used remote sensing technology to get physical parameters of the objects without direct physical contact with them. Using remote sensing sensors, huge amount of data ...is available from the Earth surface which is used in different sustainable developing programs. Due to the various available operating sensors and valuable unique information acquired by each type of sensors, then there is a need to suite from different available images acquired by different sensors simultaneously which is called image fusion technique. In image fusion, different images from different sensors are used as inputs to the fusion algorithms and finally an output image is produced which has more useful information about the interesting object comparing to each of the input images. Of course better fusion algorithms result in better fusion output images. In one case, a higher resolution gray scale image is fused with a lower resolution color image to produce a high resolution color image which inherits the spatial resolution from high resolution gray scale image and spectral information from low resolution color image simultaneously in the output fused image. In this research, we have used panchromatic and multispectral bands of IKONOS satellite image of Malard region in Iran. At first small window from the panchromatic and multispectral bands are introduced to the neural networks (NNs) and after learning phase we have predicted the panchromatic band for other parts from the multispectral data. Then the predicted panchromatic band has been used in HSI fusion method and the results are compared with the original panchromatic band. Various artificial neural networks are designed and tested to find the suitable one. It is found that in the test area using the mentioned parameters in the paper and various implemented neural networks, a multilayer feed-forward with back propagation could be the best choice. Although the results are promising themselves but it needs to carry out more tests on other type of images using more complex neural networks to achieve better results.
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