英文摘要的最基本原則

文章日期：2013-05-24 09:36

這其實應該是英文作文的原則，例如
1.事實用現在式，研究方法跟結果因為已經做完，所以用過去式或完成式。
2.主詞不是人的，應用被動式。
3.數字少於10者，請用one, two, three, 不要用阿拉伯數字1,2,3.
4.數字在字首，則也用英文而非阿拉伯數字，例如 One hundred questionnaires .....,

Email 禮貌

禮貌--有關 email

文章日期：2013-05-21 16:21

email禮貌或許個人各有見地，但至少

收到他人信件，當下儘速回復，即使只寫四個字都可以，但不要杳無音訊。

Dear ***

  Received with thanks.

Cheers,

***

應該注意卻老是未注意

這看似抱怨文，其實是語重心長啊~~~

文章日期：2013-05-21 16:05

教書教到第五年，開始會要把這些感想跟事情記下來，

第一件事是
有一些，講過many many many times，
卻總是看到學生用著無辜的眼神看著我
1. 好似從來沒聽過，或者  2. 說知道，可是忘記了
3. 更無辜的會說，老師，我不會~~~
我想，妳們都是二十二歲以上的年輕人了。

運用遊客心態打造生態旅遊環境

Understanding heterogeneous preference of tourists for big game species: 運用遊客心態打造生態旅遊環境

文章日期：2013-05-19 08:10

     隨著生態旅遊越來越興盛，也開始有"客製化"生態旅遊的想法，最早有很多研究旅遊動機，例如潛水的動機是因為特有種生物、生物多樣性、生物豐富度、天氣、地質或者與友同遊的心態而參與潛水活動。
     也有些以PCA集群分析法分析國家公園的旅客特質，看是喜歡動物生態、植物生態、生物多樣性或者是離群索居的遊客設計不同套裝行程。
    此篇則是以choice experiment approach的方式探討到南非國家公園的旅客想法，包括會因為經濟能力(有錢人或非)以及經驗而有不同需求。這些結果都可以做為國家公園管理處管理保育生態系的重點方向。
    我們是不是也能用此去分析釣客的心態及種類，去規畫友善的釣魚環境呢？
    一樣是研究，如何把成果講得很有價值也是一門學問。

Gene expression profiling of breast cancer survivability---用21個基因推測乳癌，夠有效率!

文章日期：2013-05-17 12:11

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

Hsiu-Ling Chou¹, Chung-Tay Yao², Sui-Lun Su³, Chia-Yi Lee³, Kuang-Yu Hu⁴, Harn-Jing Terng⁵, Yun-Wen Shih³, Yu-Tien Chang³, Yu-Fen Lu³, Chi-Wen Chang⁶, Mark L Wahlqvist⁷, Thomas Wetter⁸ and Chi-Ming Chu^3*

BMC Bioinformatics 2013, 14:100 doi:10.1186/1471-2105-14-100

Abstract

Background

Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.

Results

The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence…

Conclusions

The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

Keywords:

Breast cancer; Microarray; Artificial neural network; Logistic regression; Decision tree