Knowledge Discovery of Selection Rules for Acupuncture Points in Respiratory Diseases Therapy Based on Partial-Ordered Structure Diagrams

2013 ◽  
Author(s):  
Lina Hou ◽  
Zhongpeng Zhang ◽  
Jialin Song ◽  
Haibing Zhao ◽  
Wenxue Hong
Keyword(s):  
Knowledge Discovery ◽  
Respiratory Diseases ◽  
Acupuncture Points ◽  
Ordered Structure ◽  
Selection Rules

scholarly journals Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining

2020 ◽  
Vol 17 (10) ◽  
pp. 3718
Author(s):  
Lucas Schroeder ◽  
Mauricio Roberto Veronez ◽  
Eniuce Menezes de Souza ◽  
Diego Brum ◽  
Luiz Gonzaga ◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Respiratory Diseases ◽  
Spatial Association ◽  
Rank Correlation ◽  
Statistical Dependence ◽  
Temporal Association ◽  
Great Success ◽  
Success Rates ◽  
Drought Period

The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.

Symptom-herb knowledge discovery based on attribute partial ordered structure diagrams

2019 ◽  
Author(s):  
Hui Meng ◽  
Wenxue Hong ◽  
Cunguo Yu ◽  
Weili Ding ◽  
Jialin Song ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Ordered Structure

Planar defects in ordered (Ga,In)P

1988 ◽  
Vol 46 ◽  
pp. 902-903
Author(s):  
S. McKernan ◽  
C. B. Carter ◽  
D. Bour ◽  
J. R. Shealy
Keyword(s):  
Electron Diffraction ◽  
Vapor Phase ◽  
Growth Direction ◽  
High Density ◽  
Ordered Structure ◽  
Planar Defects ◽  
Diffraction Patterns ◽  
Ternary Semiconductors ◽  
Anion Species ◽  
Metallic Vapor

The growth of ternary III-V semiconductors by organo-metallic vapor phase epitaxy (OMVPE) is widely practiced. It has been generally assumed that the resulting structure is the same as that of the corresponding binary semiconductors, but with the two different cation or anion species randomly distributed on their appropriate sublattice sites. Recently several different ternary semiconductors including AlxGa1-xAs, Gaxln-1-xAs and Gaxln1-xP1-6 have been observed in ordered states. A common feature of these ordered compounds is that they contain a relatively high density of defects. This is evident in electron diffraction patterns from these materials where streaks, which are typically parallel to the growth direction, are associated with the extra reflections arising from the ordering. However, where the (Ga,ln)P epilayer is reasonably well ordered the streaking is extremely faint, and the intensity of the ordered spot at 1/2(111) is much greater than that at 1/2(111). In these cases it is possible to image relatively clearly many of the defects found in the ordered structure.

Characterisation of planar crystal defects in Y2Fe17Cx (0.6 ≤ x ≤ 1.6) magnetic material by High Resolution Electron Microscopy

1990 ◽  
Vol 48 (4) ◽  
pp. 774-775
Author(s):  
W. Coene ◽  
F. Hakkens ◽  
T.H. Jacobs ◽  
K.H.J. Buschow
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Permanent Magnets ◽  
Annealing Treatment ◽  
High Resolution Electron Microscopy ◽  
Crystal Defects ◽  
Ordered Structure ◽  
X Ray ◽  
Planar Crystal ◽  
Resolution Electron

Intermetallic compounds of the type RE2Fe17Cx (RE= rare earth element) are promising candidates for permanent magnets. In case of Y2Fe17Cx, the Curie temperature increases from 325 K for x =0 to 550 K for x = 1.6 . X ray and electron diffraction reveal a carbon - induced structural transformation in Y2Fe17Cx from the hexagonal Th2Ni17 - type (x < 0.6 ) to the rhombohedral Th2Zn17 - type ( x ≥ 0.6). Planar crystal defects introduce local sheets of different magnetic anisotropy as compared with the ordered structure, and therefore may have an important impact on the coercivivity mechanism .High resolution electron microscopy ( HREM ) on a Philips CM30 / Super Twin has been used to characterize planar crystal defects in rhombohedral Y2Fe17Cx ( x ≥ 0.6 ). The basal plane stacking sequences are imaged in the [100] - orientation, showing an ABC or ACB sequence of Y - atoms and Fe2 - dumbbells, for both coaxial twin variants, respectively . Compounds resulting from a 3 - week annealing treatment at high temperature ( Ta = 1000 - 1100°C ) contain a high density of planar defects.

Gut microbiome a promising target for management of respiratory diseases

2020 ◽  
Vol 477 (14) ◽  
pp. 2679-2696
Author(s):  
Riddhi Trivedi ◽  
Kalyani Barve
Keyword(s):  
Respiratory Diseases ◽  
New Technology ◽  
Bacterial Flora ◽  
Systemic Circulation ◽  
The Body ◽  
Lung Pathology ◽  
Promising Target ◽  
Current Therapies ◽  
Intestinal Microbial Flora ◽  
On Chip

The intestinal microbial flora has risen to be one of the important etiological factors in the development of diseases like colorectal cancer, obesity, diabetes, inflammatory bowel disease, anxiety and Parkinson's. The emergence of the association between bacterial flora and lungs led to the discovery of the gut–lung axis. Dysbiosis of several species of colonic bacteria such as Firmicutes and Bacteroidetes and transfer of these bacteria from gut to lungs via lymphatic and systemic circulation are associated with several respiratory diseases such as lung cancer, asthma, tuberculosis, cystic fibrosis, etc. Current therapies for dysbiosis include use of probiotics, prebiotics and synbiotics to restore the balance between various species of beneficial bacteria. Various approaches like nanotechnology and microencapsulation have been explored to increase the permeability and viability of probiotics in the body. The need of the day is comprehensive study of mechanisms behind dysbiosis, translocation of microbiota from gut to lung through various channels and new technology for evaluating treatment to correct this dysbiosis which in turn can be used to manage various respiratory diseases. Microfluidics and organ on chip model are emerging technologies that can satisfy these needs. This review gives an overview of colonic commensals in lung pathology and novel systems that help in alleviating symptoms of lung diseases. We have also hypothesized new models to help in understanding bacterial pathways involved in the gut–lung axis as well as act as a futuristic approach in finding treatment of respiratory diseases caused by dysbiosis.

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