/ DoerLBH / Random

Random

2000-01-01 posted in []

here is a test Xinxin is lovely DNA computing? Seelig Nature Multiplex CRISPR computing? GMC Nature fitness evolution, ucsf, kortemme lab David report first Felix polymer into protein and also chromatin what a day rDNA still worth pursuing but have to say more molecular and structural exploration would be much more interesting~ should not use random chosen states, but use the probablities calculated based on all thress sates chromatin looping? information into a review type thing or it could be nothing how to connect my dynamics rigid-body to DNA or protein looping http://www.sciencemag.org/site/feature/data/prizes/ge/2010/fullwood.xhtml another day of entering github journal… MathBio PhD or SynBio or SysBio or CompBio??? Or Google Genomics? FZ talk? Not gonna stop Need to solidify some programs to apply weekend plan decide programs, send refer help contact FY, sned email to several prf visa intel internship sep AZ, CO, CA .. HPC as a fundamental capacity HPC needs innovation: Efficiency, Reliability, Paralleism Programmable Solutions Group FPGA IPG (Intel Processor Graphics) CPU Cores Markov Chain in other axis??? like in environment Can we infer the entire enviorment based on a point In a chessboard, how many grids do we need to mark down entire chess board. stochastic gene expression on JP paper, we can do stochastic modeling thx jai & reply qi for stochastic process of iR is there any better quantificaiton iPSCs basals to think about in a simutaneous form network transsion 7~8 hq, 8-11 422, 11-12 1em, 0-4 373, 4-5 1em, 5-7 531 bayesian in 3D, 7-8 1em, 8-10 mitapp… , catch up in ML, CV, AI, Stoch filter projects outline labs outline school ranks end resort ML AI projects and potential collaborations book flights, compare schools what do I wish to focus make plan for courses next adv physics, app math, sys bio plan time what makes a good biologidt today I am learning Python. A new language that I used to think it is easy and not worthwhile. It turned out to be a huge mistake. Now I find it a very interesting programming tool that can enable me to do many things in data science and a geeky lifestyle. I am very looking forward to it.! review bicohem back to bk lecture PTM Working with proteins detecting protein by UV absorbance spectrophotometer copper-based assays dye-based assays protein purification salting out dialysis centrifuge chromatography with UV detector and fraction collector FPLC / HPLC gel filtration ion exchange chromatography affinity chromatography recombinant tags for affinity chromatography e.g. GST, MBP, His, Strep Rasmussen te al Nature 2011 Nobel protein electrophoresis (SDS, Native, IEF) gel matrix SDS PAGE silver staining 100 times more than blue staining IsoElectric Focusing (IEF) protein identifacation 2D PAGE = IEF + SDS PAGE Western Blotting one protein detection method sandwich chamber amino acid analysis hydrolyzed sample? N-terminal sequencing Edman’s Method protein cleavage chemical cleavage enzymatic cleavage protein 3D structure determination X-ray crystallography homology modeling SAXS SANS NMR spectroscopy EM https://d11.baidupcs.com/file/63ede6346996a8e1ead9bd435c567ffb\?bkt=p3-00008dd1247c3724f81d7bba83883f10f0b8\&xcode=599367b6305f3ffe1b5eaa8cf80e39d6f80f1674f0ee75741682cb8519c2059f\&fid=2802628340-250528-724911083285829\&time=1509180020\&sign=FDTAXGERLQBHSK-DCb740ccc5511e5e8fedcff06b081203-yohKRYCv2GhmpWduAWjjP%2Fllbx0%3D\&to=d11\&size=6953474048\&sta_dx=6953474048\&sta_cs=31699\&sta_ft=iso\&sta_ct=7\&sta_mt=0\&fm2=MH,Yangquan,Anywhere,,new_york,any\&newver=1\&newfm=1\&secfm=1\&flow_ver=3\&pkey=00008dd1247c3724f81d7bba83883f10f0b8\&sl=79364174\&expires=8h\&rt=pr\&r=790002327\&mlogid=6973958507398000983\&vuk=2802628340\&vbdid=1424611918\&fin=R2016b_glnxa64_dvd1.iso\&rtype=1\&iv=0\&dp-logid=6973958507398000983\&dp-callid=0.1.1\&hps=1\&tsl=100\&csl=100\&csign=tpjPT045aQmUyt%2FnuZsuzqjdcy8%3D\&so=0\&ut=6\&uter=4\&serv=0\&uc=713335145\&ic=1552888790\&ti=6c86a94138ec9ff8c88b804018b6e44e76c2c9ca120f0293\&by=themis oef fractal learning selective history Biology of psychiatric diseases Evolution, plenotypic evolution, protein evolution Microbiome dynamics, ecology metabolism of cancer (related to oxygen)

Naive Bayesian Intergration Approach

NETBAG

cortico-striatal-thalamic loops


cortico-striatal-thalamic loops

action seleciton habit formation selection/perception of important information behavior control adaptation in activity

Austism

deep layers 5/6 cortical neurons

MRI studies reveal underconnectivity

Di Martino et al The Autism Brain Imaging Data Exchange, Mol. Psych 2014 Deshpande G et al. Identification. Frontier Human Neuroscience 2013

limbic system role in emotional intelligence emotion and motivation phan kl et al., functional neuroanatomy of emtion, neuroimage 2002 Roesch MR, Olson CR. Neuronal activity related to reward value and motivation in primate frontal cortex., sciecne 2014

Brain Expression Data (Allen Mouse Brain Atlas) Lein et al. Genome-wide atlas of gene expression in the adult mouse brain. nature 2007 mouse ISH expression data 600 defined structures from standardized brain 20,000 genes in ~300,000 voxel

2015 version very well voxel brain connectivity data

Oh et al. Nature 2014. A mesoscale connectome of the mouse brain. brain connectivity data (allen mouse brain connectivity atlas)

60k maps in coordiate systems

bias with normal tissue

TRENDS in Neuroscience Goal-directed output Nacc neuron DA-neuoron terminal


amygdala - emotional component cortex prefrontoal - thinking and judging

nucleus occuleus

how brain process information

SICK dataset SNLI dataset Flickr30K

denotation graph modeling denotational probabilities multiple premise entailment denotation of a sentence s is the set of possible worlds in which s is true - denotational semantics

define a small set of transformation rules, e.g. drop modifier, drop pps

hierarchical phrases

denotational vs. distributional similarities

distributional similarities topically related but not necessarily in the same scene

semantic textual similarity

Semeval 2012 MSR Chen & Dolan Modle DKPro similarity

textual entailment

conditional probability - PMI

Modeling Denotational Prbabilities

semantic embedding spaces order embedding (vendrov et al, 2015) binary

an embedding space for denoational probabilities

overlay

LSTM (Bowman et al., 2015) eh=a8ojeh