Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. Eric Lease Morgan May 27, 2019 Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 50 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 11251 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 63 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 28 January 10 Fig 9 international 9 International 6 SARS 5 figure 4 fig 3 dna 3 Figure 3 ACE2 2 cell 2 Supp 2 RNA 2 RBD 2 Aβ40 1 𝐷27 1 𝐷23 1 −)-GR24 1 table 1 synuclein 1 stat1 1 rbd-62 1 protein 1 prescription 1 preprint 1 pnpla2 1 membrane 1 lipoprotein 1 li+ 1 lc3 1 integration 1 il-6 1 gene 1 flagellum 1 egfr 1 editing 1 doi 1 cluster 1 cellulase 1 biorxiv 1 arpe-19 1 amino 1 aflpmo16 1 acid 1 Wärmländer 1 Wang 1 Vpx 1 Vprmus 1 Vitiligo 1 ULK1 Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 2536 preprint 2191 cell 1724 protein 1690 author 1617 version 1576 review 1551 holder 1550 peer 1549 copyright 1548 funder 1539 preprintthis 1158 license 1152 perpetuity 1152 % 911 datum 909 structure 820 analysis 782 model 782 fig 762 figure 725 gene 723 site 716 sequence 706 licenseavailable 616 study 580 time 573 acid 572 complex 547 activity 546 p 544 sample 541 c 531 membrane 526 a. 475 value 468 domain 464 concentration 462 b 457 peptide 446 licensemade 440 type 437 enzyme 429 dna 427 result 412 right 398 interaction 396 reuse 396 permission 389 h 387 residue Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 2032 al 1546 January 1507 et 1314 J. 1219 C 1211 . 1193 M. 936 S. 805 ND 735 NC 720 International 709 ⋅ 649 C. 622 D. 603 S 600 A. 590 J 588 R. 557 Figure 535 K. 519 M 512 L. 494 E. 493 H. 460 P. 445 mM 435 B. 420 A 413 Fig 412 IL-27 394 G. 389 T. 379 pH 367 − 367 Biol 354 SARS 315 Y. 309 B 302 W. 300 N. 300 N 289 CoV-2 288 m 279 F. 279 D 276 E 276 Cell 272 L 272 F 261 RNA Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 1780 we 1635 it 159 they 135 i 61 them 55 us 22 itself 21 ya 20 he 19 one 16 https://doi.org/10.1101/2020.04.24.059154 10 u 10 me 7 em 6 you 5 themselves 3 il-27ra 2 zncl2 2 s 2 mtorc1 2 matchdrugwithdisease 2 ico 2 https://doi.org/10.1101/2021.01.08.425918 2 https://doi.org/10.1101/2021.01.08.423993 1 𝑓 1 −)-gr24 1 † 1 ​biochim 1 ~1:50 1 us- 1 snm1b 1 oxysterols 1 ourself 1 our 1 ng 1 na 1 mrnas 1 mp 1 mine 1 lit-170 1 ia 1 https://doi.org/10.1101/2021.01.06.425536 1 https://doi.org/10.1101/2021.01.06.425392 1 https://doi.org/10.1101/2021.01.05.425440 1 hrs 1 getfirstdrugprescription 1 dsi-118 1 cul4b 1 atg9a 1 arid1a Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 15063 be 2207 have 2125 use 1549 post 1548 certify 1227 display 1171 biorxiv 1158 grant 1019 show 880 bind 837 make 550 allow 497 contain 455 perform 442 base 437 indicate 399 reserve 375 identify 368 do 349 follow 311 suggest 309 find 307 include 296 determine 288 describe 270 incubate 270 increase 269 compare 265 represent 259 observe 259 induce 245 require 242 form 236 reveal 235 reduce 234 express 233 obtain 227 result 214 generate 212 measure 211 provide 208 calculate 204 add 203 associate 202 report 187 involve 183 correspond 179 fold 167 select 167 analyze Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 2318 not 753 - 606 high 575 available 521 also 478 different 448 international 390 human 361 more 358 other 346 molecular 330 then 327 well 318 low 313 single 304 only 295 structural 283 first 273 specific 266 non 265 however 260 same 258 such 257 respectively 243 further 242 previously 204 dependent 203 biological 199 similar 197 new 195 multiple 187 free 186 most 180 as 178 relative 178 disulfide 177 large 168 thus 167 small 165 here 162 active 159 final 158 cryo 149 significant 146 complex 145 additional 144 anti 143 amino 140 independent 140 functional Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 68 high 54 most 42 least 32 low 22 good 16 large 12 close 9 late 8 Most 7 strong 7 small 6 E3 5 AfLPMO16 4 big 3 steep 3 slow 3 short 3 near 3 long 3 fast 2 great 1 tiny 1 theysugg 1 spac 1 pgbd 1 few 1 editosome 1 c(.05 1 SH_RcsFss 1 SH3BGRL3 1 His6-tagged Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 132 most 53 least 7 well 3 highest 3 e3 2 aflpmo16 1 tightest 1 thinnest 1 pstat1/3 Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 3203 doi.org 1153 creativecommons.org 21 github.com 17 f1000.com 14 www.ncbi.nlm.nih.gov 7 dx.doi.org 5 www.lipidmaps.org 5 orcid.org 3 raw.githubusercontent.com 3 gnps.ucsd.edu 3 cprdcw.cprd.com 2 xcmsonline.scripps.edu 2 www.who.int 2 www.jstatsoft.org 2 www.foastat.org 2 www.ebi.ac.uk 2 www.cahanlab.org 2 immpres.co.uk 2 gtr.ukri.org 2 gitlab.tudelft.nl 1 www2.mrc-578 1 www.uniprot.org 1 www.sigmaaldrich.com 1 www.pymol.org.2002 1 www.omicsnet.ca 1 www.ms2lda.org 1 www.microsoft.com 1 www.metaboanalyst.ca 1 www.idtdna.com 1 www.graphpad.com 1 www.data.4tu.nl 1 www.collaborativedrug.com 1 www.cbs.dtu.dk 1 www.cazy.org 1 www.cancer.gov 1 www.aspgd.org 1 www.r- 1 www 1 virological.org 1 vcfannotator.sourceforge.net 1 trna.bioinf.uni-leipzig.de 1 smart.servier.com 1 sciex.com 1 rawgraphs.io 1 pubmed.ncbi.nlm.nih.gov 1 proteomics2.ucsd.edu 1 portals.broadinstitute.org 1 pdmr.cancer.gov 1 palmoilis.mpob.gov.my 1 n2t.net Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 694 http://creativecommons.org/licenses/by-nc-nd/4.0/ 335 http://creativecommons.org/licenses/by/4.0/ 101 http://creativecommons.org/licenses/by-nd/4.0/ 79 http://doi.org/10.1101/2021.01.05.425432doi: 79 http://doi.org/10.1101/2021.01.05.425432 74 http://doi.org/10.1101/2021.01.08.425379doi: 74 http://doi.org/10.1101/2021.01.08.425379 61 http://doi.org/10.1101/2021.01.07.425723doi: 61 http://doi.org/10.1101/2021.01.07.425723 60 http://doi.org/10.1101/2020.07.08.188672doi: 60 http://doi.org/10.1101/2020.07.08.188672 59 http://doi.org/10.1101/2020.12.31.424971doi: 59 http://doi.org/10.1101/2020.12.31.424971 55 http://doi.org/10.1101/2020.03.27.012757doi: 55 http://doi.org/10.1101/2020.03.27.012757 49 http://doi.org/10.1101/2021.01.04.425348doi: 49 http://doi.org/10.1101/2021.01.04.425348 46 http://doi.org/10.1101/2021.01.08.425897doi: 46 http://doi.org/10.1101/2021.01.08.425897 45 http://doi.org/10.1101/2020.12.31.424931doi: 45 http://doi.org/10.1101/2020.12.31.424931 44 http://doi.org/10.1101/2021.01.02.425093doi: 44 http://doi.org/10.1101/2021.01.02.425093 42 http://doi.org/10.1101/2021.01.04.425218doi: 42 http://doi.org/10.1101/2021.01.04.425218 41 http://doi.org/10.1101/2021.01.08.425864doi: 41 http://doi.org/10.1101/2021.01.08.425864 41 http://doi.org/10.1101/2021.01.06.425465doi: 41 http://doi.org/10.1101/2021.01.06.425465 40 http://doi.org/10.1101/2021.01.08.425958doi: 40 http://doi.org/10.1101/2021.01.08.425958 40 http://doi.org/10.1101/2021.01.06.425584doi: 40 http://doi.org/10.1101/2021.01.06.425584 40 http://doi.org/10.1101/2020.12.29.424482doi: 40 http://doi.org/10.1101/2020.12.29.424482 38 http://doi.org/10.1101/2021.01.06.425392doi: 38 http://doi.org/10.1101/2021.01.06.425392 36 http://doi.org/10.1101/2021.01.08.423993doi: 36 http://doi.org/10.1101/2021.01.08.423993 35 http://doi.org/10.1101/2021.01.06.425657doi: 35 http://doi.org/10.1101/2021.01.06.425657 34 http://doi.org/10.1101/2021.01.04.425171doi: 34 http://doi.org/10.1101/2021.01.04.425171 32 http://doi.org/10.1101/2021.01.08.425918doi: 32 http://doi.org/10.1101/2021.01.08.425918 32 http://doi.org/10.1101/2021.01.05.425448doi: 32 http://doi.org/10.1101/2021.01.05.425448 32 http://doi.org/10.1101/2021.01.03.425155doi: 32 http://doi.org/10.1101/2021.01.03.425155 31 http://doi.org/10.1101/2021.01.08.425855doi: Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 6 j.t.pronk@tudelft.nl 4 seb@dbb.su.se 4 becerrap@nei.nih.gov 3 ya-ming.hou@jefferson.edu 3 limk@ecu.edu 2 w.j.c.dekker@tudelft.nl 2 tsumoto@bioeng.t.u-tokyo.ac.jp 2 shantanus@igib.res.in 2 rlg2118@columbia.edu 2 raul.ortiz@tudelft.nl 2 peter.mchugh@imm.ox.ac.uk 2 nshabek@ucdavis.edu 2 luclar@kemi.dtu.dk 2 jose@phar.kyushu-u.ac.jp 2 fang.wu@sjtu.edu.cn 2 c.mooiman@tudelft.nl 2 axw47@case.edu 2 anthony.nash@ndcn.ox.ac.uk 1 vgohil@tamu.edu 1 timbeck@leicester.ac.uk 1 suditmukhopadhy@yahoo.com 1 stephen.high@manchester.ac.uk 1 sarah.okeefe@manchester.ac.uk 1 romain.vives@ibs.fr 1 rbg.ravelli@maastrichtuniversity.nl 1 pxr150@case.edu 1 pleao@ciimar.up.pt 1 pj.peters@maastrichtuniversity.nl 1 pilartocortes@gmail.com 1 patrick.cahan@jhmi.edu 1 opher.gileadi@cmd.ox.ac.uk 1 odile.filhol-cochet@cea.fr 1 nod@wustl.edu 1 nancy.moran@austin.utexas.edu 1 mxb150@case.edu 1 mkozak@amu.edu.pl 1 maciejgielnik@amu.edu.pl 1 luke.gilbert@ucsf.edu 1 lpipes@berkeley.edu 1 lilia@ibb.waw.pl 1 liangjiuxing@m.scnu.edu.cn 1 kenji.inaba.a1@tohoku.ac.jp 1 juliusbattjes@hotmail.com 1 jmp111@ic.ac.uk 1 jimhurley@berkeley.edu 1 jfreyre@ccg.unam.mx 1 jfcollet@uclouvain.be 1 j.snijder@uu.nl 1 ioannis.vakonakis@bioch.ox.ac.uk 1 igor@ibb.waw.pl Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 1539 version posted january 9 version posted december 7 cells were then 4 cells did not 3 cells had lighter 3 data represent mean 3 data was normalized 3 protein was then 3 proteins using thermal 2 cells following pos 2 cells using capillary 2 cells using nanobodies 2 cells were first 2 data are mean 2 data is representative 2 data was log 2 protein is also 2 proteins comparing il-27 2 proteins were also 2 proteins were then 2 structures are cyan 1 % increase references 1 % was first 1 al increased ion 1 al showed bel 1 analyses increases faster 1 analyses showed very 1 analyses using ded- 1 analyses using ultra 1 analysis did not 1 analysis following multiplex 1 analysis has never 1 analysis identified again 1 analysis identified biological 1 analysis identified mitochondrial 1 analysis identified unreported 1 analysis identifies full 1 analysis is advantageous 1 analysis is compatible 1 analysis is critical 1 analysis is not 1 analysis showed similar 1 analysis using differential 1 analysis using gorilla 1 analysis using primary 1 analysis using saxs 1 analysis using semi 1 analysis was also 1 analysis was then 1 authors did not Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 1 analysis is not straightforward 1 cells are not always 1 cells is not exactly 1 cells was not evident 1 data was not normal 1 protein had no obvious 1 structures are not well 1 structures did not substantially Sizes of items; "Measures in words, how big is each item?" ---------------------------------------------------------- 36633 10_1101-2021_01_08_425379 23040 10_1101-2020_12_31_424971 19304 10_1101-2021_01_05_425432 18706 10_1101-2021_01_07_425723 18403 10_1101-2020_03_27_012757 16409 10_1101-2020_12_31_424931 16261 10_1101-2021_01_06_425584 15457 10_1101-2020_12_29_424482 15285 10_1101-2021_01_04_425348 14821 10_1101-2021_01_06_425657 13260 10_1101-2020_12_31_424969 12979 10_1101-2021_01_04_425218 12176 10_1101-2021_01_08_425897 11618 10_1101-2021_01_06_425465 10845 10_1101-2020_09_18_291195 10257 10_1101-2021_01_08_425875 10191 10_1101-2021_01_08_425952 9146 10_1101-2020_04_24_059154 8192 10_1101-2021_01_06_425536 8025 10_1101-2021_01_08_425918 7945 10_1101-2020_12_31_424989 7280 10_1101-2021_01_08_425855 7264 10_1101-2021_01_07_425675 7229 10_1101-2020_05_10_087288 7047 10_1101-2021_01_08_425976 6886 10_1101-2021_01_08_425887 6045 10_1101-2021_01_04_425209 5880 10_1101-2021_01_03_425159 5468 10_1101-2021_01_05_425440 4668 10_1101-2021_01_04_425177 4384 10_1101-2021_01_07_425737 4220 10_1101-2021_01_08_425967 3682 10_1101-2021_01_08_426008 3512 10_1101-674051 10_1101-2021_01_08_423993 10_1101-2021_01_06_425392 10_1101-2021_01_08_425864 10_1101-2021_01_08_425958 10_1101-2020_07_08_188672 10_1101-2021_01_05_425448 10_1101-2020_06_17_156679 10_1101-2021_01_06_425610 10_1101-2021_01_07_425621 10_1101-2021_01_02_425093 10_1101-2021_01_04_425171 10_1101-2021_01_05_425367 10_1101-2020_11_24_390039 10_1101-2020_12_30_424894 10_1101-2021_01_01_425047 10_1101-2021_01_03_425155 Readability of items; "How difficult is each item to read?" ----------------------------------------------------------- 78.0 10_1101-2021_01_04_425348 76.0 10_1101-2021_01_08_425379 75.0 10_1101-2021_01_06_425657 74.0 10_1101-2020_05_10_087288 70.0 10_1101-2021_01_08_425875 69.0 10_1101-2021_01_06_425584 68.0 10_1101-2021_01_06_425465 68.0 10_1101-2020_12_29_424482 68.0 10_1101-2020_12_31_424931 66.0 10_1101-2021_01_08_425918 66.0 10_1101-2020_12_31_424971 65.0 10_1101-2021_01_06_425536 65.0 10_1101-2021_01_05_425432 65.0 10_1101-2020_12_31_424969 65.0 10_1101-2021_01_03_425159 64.0 10_1101-2021_01_04_425177 63.0 10_1101-2021_01_08_425897 63.0 10_1101-2021_01_08_426008 62.0 10_1101-2020_03_27_012757 61.0 10_1101-2020_09_18_291195 60.0 10_1101-2021_01_07_425723 60.0 10_1101-2021_01_07_425675 59.0 10_1101-2021_01_07_425737 58.0 10_1101-2020_04_24_059154 58.0 10_1101-2021_01_04_425218 58.0 10_1101-2020_12_31_424989 56.0 10_1101-2021_01_08_425952 56.0 10_1101-2021_01_08_425967 56.0 10_1101-2021_01_05_425440 55.0 10_1101-674051 53.0 10_1101-2021_01_08_425887 53.0 10_1101-2021_01_08_425855 50.0 10_1101-2021_01_08_425976 49.0 10_1101-2021_01_04_425209 10_1101-2021_01_08_423993 10_1101-2021_01_06_425392 10_1101-2021_01_08_425864 10_1101-2021_01_08_425958 10_1101-2020_07_08_188672 10_1101-2021_01_05_425448 10_1101-2020_06_17_156679 10_1101-2021_01_06_425610 10_1101-2021_01_07_425621 10_1101-2021_01_02_425093 10_1101-2021_01_04_425171 10_1101-2021_01_05_425367 10_1101-2020_11_24_390039 10_1101-2020_12_30_424894 10_1101-2021_01_01_425047 10_1101-2021_01_03_425155 Item summaries; "In a narrative form, how can each item be abstracted?" ----------------------------------------------------------------------- 10_1101-2020_03_27_012757 measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, 62 randomly shuffled gene-pair profiles generated from the training data of 22 tumor types to 144 CCLs. We mined RNA-seq expression data of 657 different cell lines across 20 cancer types 174 classification range for cell lines of each tumor type (Fig 2A, Supp Tab 1). CCN scores > threshold for tumor types other than that of the cell line''s origin were annotated 198 Cell lines that did not receive a CCN score > threshold for any tumor type were 199 purity of general tumor type and mean CCN classification scores of CCLs in the corresponding 224 training data of general CCN classifier has little impact in the classification of SKCM cell lines. classified cell lines from each general tumor type (Fig 4D). subjected the RNA-seq expression profiles of 415 PDX models from 13 different types of cancer 357 10_1101-2020_04_24_059154 newly isolated AA16 family of Lytic Polysaccharide Monooxygenase (LPMO) from 9 study, we have biochemically and functionally characterized the new AA16 family of LPMO 48 characterization, biomass degradation activity of AfLPMO16, and cellulase cocktail 51 Keywords: A.fumigatus, Auxiliary activity, Cloning, Kinetics, LPMO, Lignocelluloses, 55 (unnamed domain) and later identified as C1-oxidizing LPMO active on cellulose [21]. The AfLPMO16 contains 19 amino acids long N-terminal signal peptide before His1 catalytic 147 interaction study suggests that the AfLPMO16 may also bind to cellulose [52]. AfLPMO16 showed efficient depolymerization activity on both CMC and PASC (Fig. 6a & 231 0.04 mg/ml to 0.06 mg/ml, reducing sugar quantified for AfLPMO16 treated biomass (Fig. 281 activity two-fold compared to the only cellulase treated biomass (Fig. 7c). 7c), where the only AfLPMO16 and only cellulase treated biomass hydrolysis activity is low 335 Biomass and cellulose hydrolysis by cellulase and AfLPMO16 429 10_1101-2020_05_10_087288 Surface plasma resonance (SPR) showed the SARS-CoV-2 spike protein binds SARS-CoV-2, coronavirus, heparan sulfate, heparin, spike glycoprotein, microarray, glycoprotein of the virus to angiotensin-converting enzyme 2 (ACE2) of the host.2 SARSCoV is closely related to SARS-CoV-2 and employs the same receptor.3 The spike protein RBD domain of SARS-CoV-2 spike protein can bind with heparin. binding of immobilized heparin with SARS-CoV-2 related proteins (A) RBD, (B) spike monomer, Intrigued by these results, we examined if the SARS-CoV-2 proteins bind to heparan Binding of synthetic heparan sulfate oligosaccharides to SARS-CoV-2-spike and RBD (A) Binding of SARS-CoV-2-spike (10 µg/mL) to the heparan sulfate microarray. immobilized ACE2 with SARS-CoV-2 derived proteins (A) RBD, (B) spike monomer, and (C) Binding of ACE2 antibody, SARS-CoV-2 RBD, and heparan sulfate antibody to of heparin to inhibit the binding between RBD or spike with ACE2. 8. Mycroft-West, C.; Su, D.; Elli, S.; Li, Y.; Guimond, S.; Miller, G.; Turnbull, J.; 10_1101-2020_06_17_156679 10_1101-2020_07_08_188672 10_1101-2020_09_18_291195 Dimerization mechanism and structural features of human LI-cadherin Dimerization mechanism and structural features of human LI-cadherin Keywords: Cadherin, dimerization, cell adhesion, protein chemistry, crystal structure, small‐angle X‐ray The crystal structure also revealed that LI-cadherin the crystal structure is necessary for LI-cadherindependent cell adhesion by performing cell calcium ion-dependent cell adhesion molecule as For example, classical cadherins form a homodimer To validate if LI-cadherin-dependent cell adhesion LI-cadherin in our crystal structure (Fig. S8), its crucial for LI-cadherin-dependent cell adhesion and 4 homodimer observed by crystal structure (Fig. 3) movement of Ca2+-free linker to maintain LIcadherin-dependent cell adhesion (Fig. S15). The roles of LI-cadherin on cancer cells of cancer cells suggest that LI-cadherin acts differently with E-cadherin on cancer cells. role of LI-cadherin in cancer cells with respect to LI-cadherin-dependent cell adhesion. LI-cadherin-dependent cell adhesion. (2002) C-cadherin ectodomain structure and implications for cell adhesion mechanisms. by crystal structures of type i cadherins. 10_1101-2020_11_24_390039 10_1101-2020_12_29_424482 B6 cross-reactivity with β-coronaviruses from three lineages along with proof-ofconcept for antibody-mediated broad coronavirus neutralization elicited through (mAbs) with potent neutralizing activity were identified against the SARS-CoV-2, SARSCoV and MERS-CoV RBDs and shown to protect against viral challenge in vivo (Alsoussi Crystal structures of B6 in complex with MERS-CoV S, SARSCoV/SARS-CoV-2 S, OC43 S and HKU4 S stem helix peptides combined with binding conformational changes leading to membrane fusion and identify a key target for nextgeneration structure-guided design of a pan-coronavirus vaccine. (A-B) Molecular surface representation of a composite model of the B6-bound MERSCoV S cryoEM structure and of the B6-bound MERS-CoV S stem helix peptide crystal docking the crystal structure of B6 bound to the MERS-CoV stem helix in the cryoEM Comparison of the B6-bound structures of MERS-CoV, HKU4, SARS-CoV/SARSCoV-2 and OC43 S stem helix peptides explains the broad mAb cross-reactivity with βclassification of neutralizing antibodies against the SARS-CoV-2 spike receptor-binding domain 10_1101-2020_12_30_424894 10_1101-2020_12_31_424931 SDS-PAGE shows that SAMHD1-ΔCtD did not co-elute with DDB1/DCAF1-CtD/Vprmus (Fig 1A, B, 138 Crystal Structure analysis of apoand Vprmus-bound DDB1/DCAF1-CtD protein 154 Superposition of the apo-DDB1/DCAF1-CtD and Vprmus-bound crystal structures reveals 176 DDB1/DCAF1-CtD/Vprmus/SAMHD1 complex peak (Fig 2D, fraction 6), when compared to the wild 199 To obtain mechanistic insight into Vprmus-recruitment of SAMHD1-CtD, we initiated cryo-EM analyses 209 Molecular models of DDB1 BP domains A and C (BPA, BPC), DCAF1-CtD and Vprmus, derived from 218 biochemical data, showing that SAMHD1-CtD is sufficient for recruitment to DDB1/DCAF1/Vprmus, 226 SAMHD1-CtD cross-links were with the C-terminus of CUL4 and the "acidic loop" of DCAF1 (Fig 245 A66W mutant showed a reduction of DDB1/DCAF1-CtD/Vprmus/SAMHD1 complex peak intensity (Fig 261 Like SIV Vpx, "hybrid" Vpr proteins down-regulate the host restriction factor SAMHD1 by recruiting 324 both DDB1/DCAF1-CtD/Vprmus (core) and CUL4/ROC1 (stalk). (A) GF analysis of in vitro reconstitution of protein complexes containing DDB1/DCAF1-CtD, Vprmus 972 Crystal structure of the DDB1/DCAF1-CtD/Vprmus complex. 10_1101-2020_12_31_424969 A genome wide copper-sensitized screen identifies novel regulators of mitochondrial cytochrome c oxidase activity A genome wide copper-sensitized screen identifies novel regulators of mitochondrial Keywords: Copper, mitochondria, vacuole, cytochrome c oxidase, pH, AP-3, Rim20, Rim21 respiratory growth of yeast mutants such as these genes in cellular copper homeostasis, ATPase function, mitochondrial copper genes required for respiratory growth, mitochondrial genome maintenance and nuclear gene function in respiratory growth and expression of the mitochondrial genome Yeast genes required for respiratory growth. Genes required for copper homeostasis. Normalization of vacuolar pH in rim20Δ cells restores mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper Genetic regulators of mitochondrial copper 10_1101-2020_12_31_424971 codons by a +1-frameshifting tRNA, SufB2, that contains an extra nucleotide in its anticodon loop. We show that SufB2 uses triplet anticodon-codon pairing in the 0-frame to initially decode the 33 loops (ASLs) of +1-frameshifting tRNAs have been found to use triplet anticodon-codon pairing 78 +1 frameshifting activity of SufB2 relative to its closest counterpart, ProL, at a CCC-C motif, and 103 SufB2 and ProL lacking all post-transcriptional modifications (termed the G37-state tRNAs), or 131 ribosome in response to the CCC-C motif, and that the efficiency of delivery by G37-state SufB2 202 SufB2 exhibits triplet pairing in the 0-frame at the A site (Figures 3a-c, Supplementary Table 2, 290 SufB2 anticodon loop and/or the CCC-C motif at the 2nd codon position of the mRNA. as a function of time for delivery of G37or native-state SufB2or ProL-TC to the A site of a 70S 1267 10_1101-2020_12_31_424989 Distinct cryo-EM Structure of α-synuclein Filaments derived by Tau report structural characterizations of -synuclein filaments derived by a potential co-factor, tau, However, the Nand C-terminal regions of the tau-promoted -synuclein filament have investigation of tau-promoted α-synuclein filaments using solid-state NMR and cryo-EM to state NMR studies indicate that the tau-promoted α-synuclein filaments have similar structural Resolution of the tau-promoted α-synuclein filament density map was not high enough for de novo Solid-state NMR of Tau-promoted α-synuclein filaments Representative TEM images of tau-promoted α-synuclein filaments showing the Cryo-EM structure of the tau-promoted α-synuclein filaments Overlaid structures of the tau-promoted α-synuclein filament (purple) and polymorph 2b (green). In this work, we solved cryo-EM structure of α-synuclein filaments derived by tau and increased helical twist of the tau-promoted full-length α-synuclein filaments may result from the Gath, J., Bousset, L., Habenstein, B., Melki, R., Bockmann, A., and Meier, B. 10_1101-2021_01_01_425047 10_1101-2021_01_02_425093 10_1101-2021_01_03_425155 10_1101-2021_01_03_425159 6-gingerol interferes with amyloid-beta (Aβ) peptide aggregation 6-gingerol interferes with amyloid-beta (Aβ) peptide aggregation Key Words: Alzheimer''s disease; Amyloid aggregation; Neurodegeneration; Ginger; gingerols and Aβ peptides have not been studied at the molecular level. To monitor the effect of 6-gingerol on Aβ40 aggregation kinetics, 15 µM control sample without 6-gingerol but containing 2% DMSO was prepared. the samples to aggregate into mature fibrils that could be observed with AFM imaging gingerol, has minor effects on the aggregation kinetics, i.e. by slightly increasing the With 6-gingerol, some additions produce aggregation kinetics µM 6-gingerol appears to speed up the aggregation (τlag = 0.5 h; τ½ = 1.7 h). gingerol has no systematic effect on Aβ40 aggregation or amyloid formation. sample with 300 µM of 6-gingerol and 2% DMSO does however display a different interactions between 6-gingerol and the monomeric Aβ40 peptide. Alzheimer peptides aggregate Biophysical studies of the amyloid beta-peptide: interactions with metal 10_1101-2021_01_04_425171 10_1101-2021_01_04_425177 The engineered peptide construct NCAM1-Aβ inhibits aggregation of the human prion protein (PrP) aggregation of the human prion protein (PrP) engineered cell-penetrating peptide constructs can reduce the amount of prion Udgaonkar, 2018; Verma et al., 2015), such as the prion (PrP) protein (CreutzfeldtJakob disease), α-synuclein (Parkinson''s disease), and amyloid-β (Aβ) and tau As the NCAM1-Aβ construct inhibits fibrillation of the Aβ peptide (HenningKnechtel et al., 2020), but promotes (co-)aggregation of the S100A9 protein (Pansieri huPrP protein + 0.5 µM NCAM1-Aβ peptide, incubated for 72 hours. between NCAM1-Aβ and S100A9 protein, where amyloid aggregation is promoted might affect the aggregation of other disease-related prion proteins, such as those prion aggregates in infected cells, is that these peptide constructs directly interact with properties of prion protein-derived cell-penetrating peptides. Cytotoxicity of prion protein-derived cell-penetrating peptides is modulated by pH ProInflammatory S100A9 Protein Aggregation Promoted by NCAM1 Peptide Constructs. Formation and properties of amyloid fibrils of prion protein. 10_1101-2021_01_04_425209 Amino acids targeted based metabolomics study in non-segmental Vitiligo: a pilot study Methodology: The study of amino acid profiles in plasma of people with non-segmental vitiligo Keywords: Vitiligo, plasma, metabolomics, amino acids, liquid chromatography, R programing Oxidative stress biomarkers could be finding in the skin and blood of vitiligo patients. amino acids differences in concentration between vitiligo and healthy samples. (a) Volcano graph related to amino acids concentration change in the studied Vitiligo samples, (b) Gini Following the question on the variation of amino acids concentration inside Vitiligo cases with diagram for different ratio of amino acids in the Vitiligo samples are prepared. To best of our knowledge, there are few studied on the role of amino acids in vitiligo. profile of free amino acids, to investigate changes in those and metabolic pathways of vitiligo. autoimmunity, and oxidative stress (increased glutamic acid and proline and decreased arginine, 10_1101-2021_01_04_425218 Extracellular endosulfatase Sulf-2 harbours a chondroitin/dermatan sulfate chain that modulates its enzyme activity In conclusion, our results highlight HSulf-2 as a unique proteoglycan enzyme and its newlyidentified GAG chain as a critical non-catalytic modulator of the enzyme activity. also failed to detect the C-terminal chain containing the enzyme HD domain using PAGE analysis (Figure Nano-scale liquid chromatography MS/MS analysis of trypsinand chondroitinase ABC-digested PGs isolated from the culture medium of human neuroblastoma SHSY5Y cells led to the identification of a HSulf-2 specific, 21 amino acid long glycopeptide highlighting a HSulf-2 GAG chain modulates enzyme activity in vitro These data therefore suggest that HSulf-2 GAG chain may also influence enzyme HSulf-2 GAG chain modulates tumor growth and metastasis in vivo Hsulf-2 GAG chain could also modulate the enzyme function through other mechanisms. We thus next analyzed the effect of HSulf-2 GAG chain in an in vivo mouse xenograft model of cancer 10_1101-2021_01_04_425348 ERp46-catalyzed disulfide bond formation in ribosome-associated nascent chains 35 and PDI could introduce a disulfide bond into the ribosome-associated HSA nascent 103 In contrast to PDI, ERp46 could introduce a native disulfide bond into RNC 181 Like PDI, ERp46 also introduced a non-native disulfide bond 182 Accessibility of PDI/ERp46 to cysteines on the ribosome-HSA nascent chain 195 Disulfide bond introduction into a longer HSA nascent chain by PDI/ERp46 251 somehow prevent PDI and ERp46 from introducing a disulfide bond into RNC 95-aa. PDI and ERp46 are predicted to bind RNCs transiently during disulfide bond 315 PDI I289A and ERp57 were found to introduce a disulfide bond into RNC 82-aa at a 375 B, D PDI(B) and ERp46 (D)-mediated disulfide bond introduction into RNC 82-aa 776 Figure 4 Disulfide bond introduction into RNC 95-aa by PDI and ERp46 787 A Disulfide bond introduction into RNC 82-aa by PDI I289A (upper) and ERp57 838 10_1101-2021_01_05_425367 10_1101-2021_01_05_425432 urease inhibitor and clinically used drug for the treatment of bacterial infection. To determine the reversibility of the inhibition by panobinostat, dacinostat, EBS, captan 150 To identify the active chemical moiety of panobinostat, dacinostat, EBS or captan 280 inhibitory effects of panobinostat, dacinostat, EBS, captan and disulfiram, as well as the 453 analogs of EBS in the in vitro JBU activity assay, were determined in the presence of 454 Bacterial-cell-based assay for measuring the activity of urease in culture 592 Dose-dependent effects of panobinostat, dacinostat, EBS, captan and disulfiram on the activity of JBU 775 Figure 2 Panobinostat, dacinostat, EBS and captan inhibit the activity of JBU. Figure 3 EBS or captan allosterically inhibits the activity of urease by covalently modifying a 797 (B) Panobinostat, dacinostat, EBS, captan and disulfiram inhibit the activity of purified HPU 817 activity of JBU or the inhibition potency of panobinostat, dacinostat, EBS, captan or 211 10_1101-2021_01_05_425440 Thermal proteome profiling reveals distinct target selectivity for differentially oxidized oxysterols systematic identification of oxysterol target proteins using thermal proteome profiling (TPP). two proteins identified as targets for more than one oxysterol. Identification of oxysterol target proteins using thermal proteome profiling To identify potential oxysterol target proteins, TPP was selected as the method of choice [11][12] Target identification of oxysterols using thermal proteome profiling. profiling experiments and criteria for the identification of putative oxysterol targets; B) Structures of the tested proteins, upon the incubation of HeLa cell lysates with the different oxysterols (Figure 1C). validates the use of TPP for identifying novel oxysterol target proteins, but also highlights the polymerase III transcription complex was identified as putative oxysterol targets (Figure 2C). We focused our initial analysis of specific oxysterol target proteins with 7-KC, as it is the most approach for oxysterol target protein identification. profiles, with only two proteins identified as targets of more than one oxysterol. 10_1101-2021_01_05_425448 10_1101-2021_01_06_425392 10_1101-2021_01_06_425465 the substitution of a few amino acids can alter ligand specificity between KAI2 duplicated the legume crystal structure of Pisum sativum KAI2B at 1.6Å resolution (Figure 4 and Table 1). structural rearrangements between PsKAI2B and the previously determined Arabidopsis KAI2 To further determine the differential ligand specificity between PsKAI2A and PsKAI2B, we utilized the PsKAI2B crystal structure reported here to generate a 3D model for PsKAI2A. Structural comparative analysis within the ligandbinding pocket shows divergent solvent accessibility between PsKAI2A and PsKAI2B (Figure reported apo and ligand bound D14/KAI2 crystal structures11,12,19,36,50. structure highlights a potential new intermediate in the ligand cleavage mechanism by KAI2 Structural basis of unique ligand specificity of KAI2-like protein from Structural analysis of HTL and D14 proteins reveals the basis for ligand The Structure of the Karrikin-Insensitive Protein (KAI2) in The crystal structure of legume KAI2. Structural basis of PsKAI2B ligand interaction. 10_1101-2021_01_06_425536 Recognition of a Tandem Lesion by DNA Glycosylases Explored Combining Molecular Dynamics and Machine Learning Keywords: MutM, DNA repair glycosylase, tandem lesion, molecular dynamics simulations important MutM/Fpg residues (K60, H74, Y242, K258, and R264) are known to stabilize the DNA helix by interacting with its structural behavior of Fpg and MutM in presence of tandem DNA lesions has been reported, despite the relevance that such show that the presence of the tandem lesion induces important structural deformations to the DNA that significantly perturb The interaction network as found in the MutM:DNA crystal containing a single 8-oxoG lesion is conserved stable along our Cartoon representation of MutM interacting with the DNA helix harboring a single 8-oxoG lesion (OG19, A) or dC7/Ap20 H-bond acceptor atoms lies at 2.6±1.1 Å and 3.0±1.8 Å, respectively, in the tandem-damaged MutM:DNA complex. Importance of the contribution of residues to the MutM:DNA complex bonding for the singly-damaged (blue) and 10_1101-2021_01_06_425584 induced by simulated diabetes on coronary artery endothelial cells 2 Coronary artery endothelial cells (CAEC) exert an important role in the development of 26 novel signatures of DNA/RNA, aminoacid, peptide, and lipid metabolism in cells under a diabetic 35 Keywords: SWATH-Proteomics; Metabolomics; Type 2 Diabetes Mellitus; Endothelial cells; 44 Artery Endothelial Cells (BCAEC) under a prolonged diabetic environment. composite network comprising PPI between the modulated proteins by simulated diabetes (seed 338 To better understand the effects that simulated diabetes exerts on endothelial cells the changes 364 used an in vitro model involving endothelial cells that modulate the heart function, CAEC (46). the understanding of amino acid metabolism in endothelial cells under simulated diabetes. glucose enhances oxidative stress and apoptosis in human coronary artery endothelial cells. high glucose on the aerobic metabolism of endothelial EA.hy926 cells. Bovine coronary artery endothelial cells (BCAEC) metabolite molecular network. by simulated diabetes in bovine coronary artery endothelial cells (BCAEC). 10_1101-2021_01_06_425610 10_1101-2021_01_06_425657 The SCFMet30 ubiquitin ligase senses cellular redox state to regulate the transcription of sulfur metabolism gene In yeast, control of sulfur amino acid metabolism relies upon Met4, a transcription factor which 15 When yeast cells sense sufficiently high levels of sulfur in the environment, the MET gene 40 work together to regulate levels of MET gene transcripts in response to the availability of sulfur or 51 acute depletion of sulfur metabolites and the activation of the MET gene regulon (Sutter et al., 88 sulfur metabolic pathway was sufficient to rescue Met30 E3 ligase activity and re-ubiquitinate 125 yeast cells starved of sulfur readily reversed Met30 cysteine oxidation. Since the cysteine residues within Met30 became rapidly oxidized in sulfur-free conditions, the 214 we performed in parallel the Met30-Flag IP with cells grown in both high and low sulfur 223 metabolite levels in yeast, Met30, is regulated by reversible cysteine oxidation. 10_1101-2021_01_07_425621 10_1101-2021_01_07_425675 Mass spectrometry-based sequencing of the anti-FLAG-M2 antibody using multiple proteases and a dual fragmentation scheme Mass spectrometry-based sequencing of the anti-FLAG-M2 antibody using multiple 1 mass spectrometry, antibody, de novo sequencing, EThcD, stepped HCD, Herceptin, FLAG tag, 16 method for direct de novo sequencing of monoclonal IgG from the purified antibody products. Direct mass spectrometry (MS)-based sequencing of the secreted antibody products is a useful 50 protocol achieved full sequence coverage of the variable domains of both heavy and light chains, 81 mass spectrometry-based de novo sequencing of the monoclonal antibody Herceptin. Mass spectrometry based de novo sequence of the mouse monoclonal anti-FLAG-M2 antibody. We next applied our sequencing protocol to the mouse monoclonal anti-FLAG-M2 antibody as a 159 antibody produced with the experimentally determined sequence demonstrate equivalent FLAG-tag binding 181 There are four other monoclonal antibody sequences against the FLAG tag publicly available 205 10_1101-2021_01_07_425723 cerevisiae, fast anaerobic growth on synthetic media requires supplementation with a source of 44 S. cerevisiae can import exogenous sterols under severely oxygen-limited or anaerobic conditions20. chemostat cultures with different aeration and anaerobic-growth-factor (AGF) supplementation 137 Fig. 3, contrast 31) in severely oxygen-limited cultures supplemented with ergosterol and Tween 80. these anaerobic growth factors from the medium of these cultures (Supplementary Fig. 6, contrast 43). marxianus strains NBRC1777 and CBS6556 were each anaerobically incubated in four replicate shake-230 For whole-genome sequencing, yeast cells were harvested from overnight cultures and DNA 562 To determine if an oxygen-limited pre-culture was required for the strict anaerobic growth of IMX2323 640 Cerevisiae in Anaerobic Glucose-Limited Chemostat Cultures. Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth 935 Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth 935 Strains were grown in shake-flask cultures in an oxygen-limited (a) and strict anaerobic 979 10_1101-2021_01_07_425737 basal body structural proteins and for flagellum type III export machinery. Buchnera of the pea aphid (Acyrthosiphon pisum) maintains 26 genes coding for flagellum Buchnera maintains genes coding for the proteins required for a functional T3SS2,12, procedure for isolation of flagellum basal body complexes adapted for an endosymbiont26, allowing for removal of these structures directly from Buchnera and enrichment of flagellum basal samples were enriched during the isolation procedure: structural proteins FilE, FliF, FlgI, FlgE, Type III secretion proteins FlhA and FliP were shown to be enriched by this procedure The widespread enrichment of Buchnera flagellum proteins Though heavily expressed in Buchnera of pea aphids, components of the flagellum basal with type III secretion activity (flhA, flhB, fliP, fliQ, and fliR) and basal body structural proteins (fliE, Isolation of flagellum basal bodies from Buchnera cells accessory proteins maintained by Buchnera aphidicola in pea aphids. 10_1101-2021_01_08_423993 10_1101-2021_01_08_425379 Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders STAT1/3 effector activation to an unbiased and quantitative multi-omics approach: phosphoproteomics after early cytokine stimulation, kinetics of transcriptomic changes and alteration action between sustained pSTAT1 and IRF1 expression to drive the induction of an interferonlike gene signature that profoundly shaped the T-cell proteome. IL-27 induces a more sustained STAT1 activation than HypIL-6 in human Th-1 cells little effect on STAT1 activation kinetics when compared to RPE1 wild type cells, suggesting Our data suggest that STAT molecules compete for binding to a limited number of phosphoTyr motifs in the intracellular domains of cytokine receptors. immune cells differ in their expression of cytokine receptors and STATs, we investigated levels STAT1 protein levels in SLE patients modify HypIL-6 and IL-27 signaling responses STAT1 expression could significantly change cytokine-induced cellular responses by HypIL-6 10_1101-2021_01_08_425855 DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites. deep learning model DeepHBV to predict HBV integration sites by learning local learning model DeepHBV to predict HBV integration sites by learning local genomic DeepHBV effectively predicts HBV integration sites by adding genomic features. mixed HBV integration sequences, positive genome feature samples, and randomly peaks and DeepHBV with HBV integration sequences + TCGA Pan Cancer peaks) on model trained with HBV integrated sequences + TCGA Pan Cancer showed an performed better compared with DeepHBV model with HBV integration sequences + HBV integration sites + TCGA Pan Cancer, a cluster of attention weights much output of DeepHBV with HBV integration sites plus TCGA Pan Cancer showed the of DeepHBV with HBV integration sequences + TCGA Pan Cancer showed strong DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) DeepHBV with HBV integration sequences + TCGA Pan Cancer model on (a) 10_1101-2021_01_08_425864 10_1101-2021_01_08_425875 A high content lipidomics method using scheduled MRM with variable retention time window and relative dwell time weightage However, as the retention time window width varies for each lipid species, a variable window width for each lipid species could reduce the time necessary to develop high 611 species were identified in positive mode (SM, CE, Cer, TAG, DAG, MAG) and 625 The raw analytical signal obtained for standards from plasma lipid extract (spiked with We developed a scheduled-MRM method that can identify more than 1000 lipid species identify lipid species that are altered due to vitamin B12 deficiency. bonds in fatty acid chain, we could identify considerably higher number of lipid species Lipidomics study in normal and vitamin B12 deficient human plasmaUsing the method developed we identified lipid species that are altered in individuals to detect more than 1000 lipid species in plasma, including isomers of TAG, DAG and 10_1101-2021_01_08_425887 the same structured model, so that these can be used as input to rule-based or deep learning algorithms for data extraction. example, at this point in this article the main headers are ''abstract'' followed by ''introduction'' and ''materials and methods'' that could make up a digraph. We use this process to evaluate new potential synonyms for existing terms and identify abstract → introduction → materials → results → discussion → conclusion → acknowledgements → footnotes section → references. Based on the digraph, we then assigned data and data description to be synonyms of the materials section, and participants From the analysis of ego-networks four new potential categories were identified: disclosure, graphical abstract, highlights and participants. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications. Newly identified synonyms for existing IAO terms (00006xx) from the digraph mapping of 2,441 publications. 10_1101-2021_01_08_425897 APOBEC1 mediated C-to-U RNA editing: target sequence and trans-acting factor contribution to Cofactor dominance was associated with editing frequency, with RNAs targeted by both RBM47 for site-specific Apob RNA editing and editosome assembly (Backus and Smith 1991). four subgroups based on overall structure and location of the edited cytidine: loop (Cloop), stem editing frequency (Table 1) with RNAs targeted by both RBM47 and A1CF observed to be mismatches in mooring sequence, spacer length, location of the edited cytidine, and relative The current study reflects our analysis of 177 C-to-U RNA editing sites from 119 target mRNAs, downstream sequences, host tissue and secondary structure of target mRNA were associated mooring sequence model as applied to the entire range of C-to-U RNA editing targets. independently associated with co-factor dominance in RNA editing sites. of AU base content (%) of nucleotides flanking modified cytidine in RNA editing targets and 10_1101-2021_01_08_425918 CanDI identifies genes that are conditionally essential in BRCA-mutant ovarian cancer. (A) Average gene essentiality for KRAS and EGFR in groups of NSCLC cell lines Gene essentiality is an averaged Bayes Factor score for each group of cell lines. Average gene essentiality for KRAS and EGFR in groups of NSCLC cell lines stratified by identify genes that are differentially expressed between male and female cell lines within each factor gene essentiality scores in male and female CRC cell lines. differentially essential genes are shown in violin plots split by the sex of the cell lines. differentially essential genes are shown in violin plots split by the sex of the cell lines. differentially essential genes are shown in violin plots split by the sex of the cell lines. CD151 SLC4A2 B2M ITGA3 SLC3A2 HLA-C CD44 LRPAP1 DDR1 VDAC2 SLC29A1 SLCO4A1 B2M CD151 THY1 SLC3A2 SLC4A2 LRPAP1 HLA-C DDR1 SLC29A1 ITGA3 PTGFRN VDAC2 10_1101-2021_01_08_425952 publication is by no means a complete tutorial, we will expand on some of the main package features, such as, how to: Isolate patients by first drug prescriptions at given clinical Fabricated CPRD clinical and CPRD prescription records in addition to age, gender and index of multiple deprivation scores are included Several rdrugtrajectory functions use the CPRD product.txt file for assigning a text description to a prescription prodcode. dataframes require, as a minimum, a patid and eventdate column, and either medcode or prodcode (for therapy data, issueseq is necessary), and presented in that order. functions to retrieve CPRD data, including, patient year of birth, gender (male or female) and The examples presented here and those in the reference manual rely on searching and subsetting EHR data using a list or vector of patient identifier. The final example of EHR dataframe manipulation presented here demonstrates how to retrieve all prescription records for patients prescribed a specific prescription treatment. 10_1101-2021_01_08_425958 10_1101-2021_01_08_425967 Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data noise in clustered omics and systems biology data noise is embedded in the resulting clustered vector. clustering algorithm orders the data, with several measures regarding external and internal classification yielded in clustering analysis of the data. Such partition vector is colored according to the classification that each item is associated cluster, this noise may be due to the intrinsic metric or marker used to order the data set. to a vector of numeric labels, in which a number represents a classification, to be able to calculate SC. Effect of the length and number of partitions of the vector in the Z-score distributions. statistical significance of the PQA score because of the less the number of items in the VP, the greater Finally, to assess the PQA methodology using systems biology data we clustered 210 networks 10_1101-2021_01_08_425976 Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data yielding highly biased disease risk estimators if used as event time labels (Cipparone and others, 2015; Uno and SCORNET utilizes current status labels while also employing a robust semisupervised imputation approach on the extensive unlabeled set to maximize survival estimation efficiency. further illustrate the utility of SCORNET in clinical applications, we apply it to a real-world EHR study estimating the risk of heart failure among rheumatoid arthritis patients in Section 4. existing survival function estimators with current status data: 1) parametric Weibull Accelerated Failure Time confidence intervals constructed with the bootstrap (red) and plug-in (blue) standard error estimators in various simulated settings with = = 200 observed current status labels. set with observed current status labels, the SCORNET estimator serves as a robust and efficient alternative 10_1101-2021_01_08_426008 AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster At low identities, these methods produce uneven clusters where the majority of sequences are are no clustering methods that can accurately cluster large taxonomically divergent metabarcoding reference databases such as databases (Schoch et al., 2020), there is a need for new computationally efficient methods that can cluster divergent sequences. To cluster divergent sequences, we developed AncestralClust clustering methods: UCLUST (Edgar, 2010), meshclust2 (James dataset against UCLUST because it is the most widely used clustering program and it performs better than CD-HIT on low identity We developed a phylogenetic-based clustering method, AncestralClust, specifically to cluster divergent metabarcode sequences. Comparisons of clustering methods using 13,043 COI sequences from 11 different species. 10_1101-674051 Q.C.V.)*100 for Vmax, showing a non normal distribution of the values generated for 3 of the tested substrates medians values of Vmax generated by Easy Kinetics for all the tested substrates were correlated with those Fig.2 Model''s precision analysis: A) Kinetic curves of the two enzymes tested for different limiting substrates, respectively Xanthine or B-D) Boxplots representing the distributions of Vmax, Km and nH values generated with Easy Kinetics showing graphically the linear correlation between the Vmax and Km generated both by Easy Kinetics (n=4) and GraphPad prism 8 (n=4). The tested substrates show a normal distributions of medians values for Vmax generated using Easy Kinetics (W = 0.87392, p-value = test, while a non normal distribution of mean values for Km generated using Easy Kinetics (W = 0.67175, p-value = 0.005173) and of by commercial software to evaluate the main kinetic parameters of an enzyme.