Emphysema and lung cancer in population-based CT screening

Population screening for emphysema and lung cancer using CTScreening for lung cancer using CT is an effective strategy to reduce lung cancer mortality, but also leads to suboptimal referrals in retrospect. It is important to arrive at an optimal definition of a group of people who may be eligible for this screening. This thesis looked at the role of emphysema in identifying this group, both in the general Chinese and Dutch populations. First, we looked at the prevalence of emphysema and the epidemiological and radiological characteristics of emphysema based on CT. Then, the association of emphysema with lung cancer and lung nodules was examined. The main findings of this thesis show that Chinese never-smokers have a higher prevalence of CT-detected emphysema than the Dutch general population. Based on analysis of studies with a total of more than 107,000 participants, it appears that individuals with emphysema detected on CT have an increased risk of lung nodules and lung cancer. This risk increases with the severity of the emphysema. This applies to both emphysema diagnosed by visual and quantitative assessment on CT. In population screening with CT, the emphysema determination can be considered a predictor for lung cancer. This knowledge can be used to improve inclusion criteria and optimal frequency for lung cancer screening. In addition, uniform approaches for CT-based emphysema determination should be adopted to reliably determine the true burden of emphysema in different population groups and based on different CT systems

Fast Min-Sum Algorithms for Decoding of LDPC over GF(q)

In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier in ISIT 05 only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200

Geodesic Distance Function Learning via Heat Flow on Vector Fields

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if the original manifold is not Euclidean. Note that the distance function on a manifold can always be well-defined. In this paper, we propose to learn the distance function directly on the manifold without embedding. We first provide a theoretical characterization of the distance function by its gradient field. Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself. Specifically, we set the gradient field of a local distance function as an initial vector field. Then we transport it to the whole manifold via heat flow on vector fields. Finally, the geodesic distance function can be obtained by requiring its gradient field to be close to the normalized vector field. Experimental results on both synthetic and real data demonstrate the effectiveness of our proposed algorithm

Outstanding supercapacitive properties of Mn-doped TiO2 micro/nanostructure porous film prepared by anodization method.

Mn-doped TiO2 micro/nanostructure porous film was prepared by anodizing a Ti-Mn alloy. The film annealed at 300 °C yields the highest areal capacitance of 1451.3 mF/cm(2) at a current density of 3 mA/cm(2) when used as a high-performance supercapacitor electrode. Areal capacitance retention is 63.7% when the current density increases from 3 to 20 mA/cm(2), and the capacitance retention is 88.1% after 5,000 cycles. The superior areal capacitance of the porous film is derived from the brush-like metal substrate, which could greatly increase the contact area, improve the charge transport ability at the oxide layer/metal substrate interface, and thereby significantly enhance the electrochemical activities toward high performance energy storage. Additionally, the effects of manganese content and specific surface area of the porous film on the supercapacitive performance were also investigated in this work

Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials