2019-nCoV

Some basic information about 2019nCoV

From Paper:
Chao lin et.al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020. DOI:https://doi.org/10.1016/S0140-6736(20)30183-52020

Time(时间):

Jan 2, 2020: 截至1月2号，2020

Samples(样本):

41 admitted hospital patients-Confirmed 2019-nCoV infection: 41人已经确认感染2019-nCoV用于本实验

men (30 [73%] of 41): 主要感染者是男性（占比73%=30/41)
The median age was 49·0 years (IQR 41·0–58·0)： 患者年龄分布 41-58岁，中间值：49岁

History diseases(13 [32%])：患者病史 13人， 占比32%

* diabetes (eight [20%]）： 糖尿病（8人，占比20%）
* hypertension (six [15%])：高血压（6人，占比15%）
* cardiovascular disease (six [15%])：心血管病（6人， 占比15%）

27 (66%) of 41 patients had been exposed to Huanan seafood market: 接触过武汉海鲜市场(27人, 占比66%)

Common symptoms（症状）

Initial symptoms (初始发病症状):

* fever (40 [98%] of 41 patients): 发热（占比98%！！！！！！！！）
* cough (31 [76%])： 咳嗽（占比76%！！！！！！）
* myalgia or fatigue (18 [44%])： 全身乏力 （占比44%！！！）
* sputum production （11 [28%] of 39): 痰液 （占比28%！！）
* headache (three [8%] of 38): 头疼 (占比8%！)
* haemoptysis (two [5%] of 39) :咯血 (占比5%！)
* diarrhoea (one [3%] of 38)：腹泻 （占比3%！）
 

Middle symptoms (中间发病症状):

22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0])：
22人（占比55%）从初始发病到出现呼吸困难需要8天左右的时间（[IQR 5·0–13·0]）

26 (63%) of 41 patients had lymphopenia：
26人（占比63% 出现淋巴细胞的减少

All 41 patients had pneumonia with abnormal findings on chest CT：
所有病人经过CT扫描都发现有肺炎的产生

Last symptoms (最后发病症状):

acute respiratory distress syndrome (12 [29%])：最后出现急性呼吸困难症状 (占比12%)
RNAaemia (six [15%])： 最后RNA败血症（占比15%）
acute cardiac injury (five [12%])： 最后出现急性心脏损伤（占比12%）
secondary infection (four [10%]))： 最后出现二次感染（占比10%）

13 (32%) patients were admitted to an ICU: 13人最后进入急诊病房（占比32%）
ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα.
这些进入ICU的病人普遍血浆水平升高（生物标志物)：IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα.
six (15%) died： 6人死亡（占比15%）

More details see Paper:

Chao lin et.al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020. DOI:https://doi.org/10.1016/S0140-6736(20)30183-52020

Diffusion MRI data analysis with DSI Studio

I joined a group for studying DTI data analysis with DSI studio. The link below is some notes for this course.


https://sinodanish.github.io/DSI-Studio-Workshop/


(Tips: A.Lee is my English Name)

How to insert mathmatics or equation into Markdown

MathJax plugin

MathJax plugin is a very easy and powerful tool for implementing HTML. Add MathJax CDN via below syntax:

<script type="text/javascript"
   src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>

[//]: <> (Markdown annotation style: the link for callinf online MathJax function, you can also download it and use it offline)

Now, it can works. 

1. formula between lines

$$ a+b=c $$

2. formula within lines

$$ evidence\_{i}=\sum \_{j}W\_{ij}x\{j}+b\_{i} $$ （1.1）            

\\ (W\_i \\) and \\ (i \\) （1.2）

The difference of 1.1 and 1.2. Testing it.

Appendix: Online equation convert tex tool - [WebEqution](https://webdemo.myscript.com/#/demo/equation)

贵州之行-InChinese

起因

很意外一个月之前接到当年同一个寝室的大学室友的邀请，来贵州当地参加他的婚礼，说实话，婚礼正好在五一的节假日期间，自己也想出去放松放松，所以就很果断的买了机票，从北京飞往贵州，开始自己的五一假期，也是自己的第一次来贵州。

北京 - 贵州

早上6：00起床，赶上飞机，大概飞了三个多小时的飞机，中午12点多到了贵阳的龙洞堡机场，然后卖大巴到金阳，大概一个小时多的时间，一路还是有很多美丽的风景，因为这边主要是已石山为主的地貌。在贵州，石山是其独有的天然奇景，有些石山美丽的令人窒息，自然赋予的地貌特征是最美丽的，自然的，纯粹的，单纯的，正如贵州本地乡村的百姓一样，很质朴。然后需要转乘客车又大概四个小时到赫章。我的同学在哪儿接我。赫章是一个县城，好像是有雄厚的历史积淀的，又称夜郎古城。可能我们每个人都比较熟悉“夜郎自大”这个成语。此古典就源于此地。更多关于夜郎的中文资料请查看百度百科。

石山一角

祝福

在当地的农村，身临其境的体会了当地乡亲的质朴与简单，这个婚礼很温馨，也很感动。自己也想了很多，说实话，真的很感动。自己看到新郎的父母大人们，今年都已贵庚六十多岁，白发苍苍，满脸都是生活带给他们的时间印记。但我可以感受到，那一天那一刻，他是有多么的高兴。感恩生命中有你，相伴一生。其实结婚对于一个人来说，真是最重要的。也许只有那一刻，你才能明白吧。踏入婚礼的那一刻，责任变了，角色变了，一切都变了。但这是我们每个人都十分愿意接受的改变吧。爱，很简单。

道歉

在回城路上，由于多次听说贵州这边的私家车（最好别坐，一般他们会拉着你到处转，如果你不是本地人的话。），出租车也是如此，有的打表，有的不打表。所以整体来说，贵州这边还是在交通上面比较乱的。今天晚上坐出租车，由于自己的太谨慎，误会了一个出租车司机，但还是想说个“对不起”。 但这两天入住的酒店景色都还不错。

2019 China Neuroscience Related Conference or Summer School Information Short List

南京 “脑-智” 国际研讨会 Nanjing International Symposium on Brain-Mind-Intelligence

Time:
Conference: 6 22-6 23, 2019
Skill Training: 6 24-6 25, 2019
Address: Nanjing

IBRO-ICPBR Summer School of Primate Neurobiology

Time:
Lectures: July 8th to July 14th, 2019
Internships: July 1st to August 31st, 2019
Address: Institute of Neuroscience, Chinese Academy of Sciences 320 Yue Yang Road, Shanghai 200031, China
The online application will be closed on **April 30th, 2019 (11:59 p.m. GMT+8)**


2019年《脑的真相》国际暑期学校 2019 SJTU Brain Facts International Summer School

时间：2019年7月1日-7月19日
地点：上海交通大学及上海周边地区等
教学内容和实践活动
Course A：2周    Course B：1周

开展海内外名师课程及讲座、学生报告研讨会、实验室工作坊、参观访问校外研究机构及企业、中国文化采风等活动。学生中期总结研讨会后，部分学员可选择进入相应课题组实验室开展科研见习，并最终递交一份实验报告，教学部分和科研见习将分别颁发结业证书。

报名方式及截止时间:

通过在线报名系统报名http://apply.sjtu.edu.cn，报名截至2019年5月31日。


2019第一届中国计算与认知神经科学会议

Time: 2019年6月14日-16日
Address: Chengdu

会议注册与缴费:

     现场注册：1500 元（5月15日后及现场)

    提前注册（5月15日及之前）：注册费1200元；学生（凭学生证或研究生证）600 元


The 4th International Conference on Basic and Clinical Multimodal Imaging (BaCI)

Time: September 10 to September 14, 2019
Address: Chengdu, China
**Status: Submitted Abstract, Waiting**

The Short Introduction of The Neurome Project

The Short Introduction of The Neurome Project
I am keen to visualize the brain connectome by HPC.  Here is a classical example for brain connectome research by Dr. Larry W. Swanson. The short introduction of the neurome project, link here. 

Adapted from neurome project

Adapted from neurome project

Adapted from neurome project

Visualization Brain

Visualization neuroscience data

1.visualization neuroscience

I had experience for molecular research, in studying this area period, I am very painful because I can't watch or notice what happened in my every step of the experiment, I really want to know to make my every step under my control and visualization what happened in real-time. But the fact is we don't know whether each step is right and until the last moment machine helps us visualization result, if we failed, actually, we normally waster more time and energy, but we don't get the good or expected experiment result.

In the neuroscience and neuroimaging, MRI has good spatial resolution and fMRI can make us visualization brain activity in real-time, so I think that visualization brain signal whatever for academic research and present your result both are very important. Combined with High-Performance-Computer(HPC) can help us visualization and simulation the neuron signal. We should grasp this opportunity to build a larger platform to visualization single cell to single neuron, neuron circuit, whole neuron activity, reconstruction neuron map etc based on machine learning, HPC etc technology. 

2.3D visualization fMRI data 

In this section, I will introduce some techniques that can help us 3D visualization fMRI data. All software and information from the literature "Creating 3D visualizations of MRI data: A brief guide". If you are interested in visualization fMRI data, you can read the raw paper.

2.1 Standard fMRI analysis and 3D visualization packages

1. SPM: http://www.fil.ion.ucl.ac.uk/spm/
2. AFNI: http://afni.nimh.nih.gov/afni/, with SUMA
3. FreeSurfer https://surfer.nmr.mgh.harvard.edu
4. MRIcroGL: http://www.mccauslandcenter.sc.edu/mricrogl/ 
5. 3D Slicer: http://www.slicer.org
6. Mango: http://ric.uthscsa.edu/mango/ 
7. ITK-SNAP: http://www.itksnap.org
8. ParaView: http://www.paraview.org
9. Statistical Map Database-NeuroSynth: http://www.neu rosynth.org

Above software, most based Matlab, Linux system, but some can both work under Windows and Linux/Mac version.

The pipeline can be shown as follows:

3D visualization fMRI pipeline( Christopher R. Madsen, 2015 )

2.1.1 Visualizing cluster maps in a glass brain

1st: Obtaining the anatomical ‘glass brain’ image

Standard preprocessing-GLM-Statistics inference- Activity map(fMRI)

Template(Anatomical)

2st: Obtaining the thresholded cluster image

Function: imcalc;  nii_threshreslicecluster

spm_imcalc_ui('statmap.nii' ,'statmapH.nii' ,'i1>12')

nii_threshreslicecluster ('statmapH.nii' ,'statmapH.nii',.5,400);

3st: Convert to VTK

4st: Render in 3D

2.1.2 Visualizing anatomical ROIs

1st: Obtain ROI volume

2st: Convert to VTK

3st: Render in 3D

2. Future 

My interesting on the machine learning, HPC, neural data visualization, computational neuroscience. In the next day, I will continue to focus on this research area. 

It's time to go to eat dinner. Have a good weekend.

3. Appendix information

Images of the anatomical ROIs used in Procedure 

Comparative neuroanatomy of whole-brain and hippocampal brain volumes

 3D renderings of MRI data