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\n \n\nXBB : India and Germany records the high frequencies 70.92%,50% respectively, while other countries have frequnency less than 20%
\n \n\nNorway doesn't have occurances of XBB.1.9.2,XBB.1.16 while other countries has recorded occurances.
\n \n\nEG.5 is more prevalent in Norway and India for two months straight than in other countries
\n \n\nXBB.1.9.1,EG.5 : Norway records occurances only for two months and it is highest than frequencies of other countries. No gradual increase or decrease recorded.
\n \n \n\nThe CI would say how true are these sudden occurances.
\n \n\n\nXBB.1.9.1 : not present in Australia
\n \n\nB.1.1.529(Omicron): The Final spike after a downhill trend during the last month of occurance(jun23) in Norway(66.66%) and Germany(16%) can be interesting.
\n \n\nCH.1.1: Norway records high frequencies (61%) compared to other countries.
\n \n\nXBB.2.3: Starts in Jan23 in India and is continuous till May23. There is a break of 3 months Jun23,Jul23,Aug23. Then there is a spike(highest frequency) in the month of Sept23-Oct23.(intereseting), while in other countries the trend is monotonous.
\n \n \n\n10. BA.2.86 \n\nOccurances only observed in Denmark, Germany,Spain,Uk,USA \nThough the frequency of all the occuraces in all these countries are less than 10%, by the 95% CI Denmark records the highest frequncy (40%) of occurance in the month of Jul23. \nDenmark records occurances from Jul23-Sep23, spain and UK records occurances from Sep23-Oct23. Germany,USA records occurances only in oct23 which is very low 0.9% and 0.3% respectively. \n \n \n
\n\nNo records of the following variants B.1.429(Epsilon), B.1.525(Eta), B.1.526(Iota), C.37(Lambda), B.1.621(Mu), P.3(Theta), P.2(Zeta), B.1.640 in the 10 chosen countries during Jan22-Oct23. \n \nInteresting plots of lineages not variant \n\nAll the following plots might change if the lineage mapping is done based on the characteristic mutations obtained from OutBreakInfo. Presently the mapping is based on mutations obtained from the GISAID database for each country.
\n \n\nBF.7: Peaks of Denamrk around 30% and Germany 15% are notable because no other country has recorded such frequencies for this lineage - they are less than 5%. And Norway has no trace of this lineage. \nHW.1: Aaustralia has highest frequency of this lineage in the month of Sept23 - above 50%, while other countries have all their frequncies below 10%. South Koreaa and USA are the other two countries which have meager occurances of this ineage other 7 countries have no trace of this lineage. \nXBF: Australia and Canada records highest frequency - around 30%-35% while other countries have frequencies less than 20%. Australia records long period exposure this lineage. \nXBK: Denmark records the highest occurance of this lineage in Jun23 - more than 30% while other countries record frequencies less than 10%. India has reported no cases from this lineage. \nXBC.1.6: Australia records the highest frequency which is around 30% and the occurance in Australia is for a long period May23-Oct23. Minimal occurances are found in South korea, Canada, UK, USA. \nXBC.1.3: Australia has long season (Nov22-Oct23) of this lineage and also records the highest frequency - more than 20%. \nFL.10: Germany records the highest frequecy of 50% for a month in May23 while other countries have frequencies less than 5%. \nGE.1: India Records frequency of more than 30% in Jun23 while other countries have frequencies less than 10%. Uk has the prolonged season of this variant with minimal occurance. \nDV.x: Norway records the highest frequency of this lineage in Jun23 which is more than 30% while most countries record frequencies around 10%. Denmark has the longesst season of this lineage. Jan23-Oct23. \nBL.1: India records the longest season with this lineage - Jun22-Jun23 and has the hoghest frequency in Jun23 (more than 30%). It should be noted that there are no occurances in the months Jan23-May23. \nBQ.1.1: Spain has the longest season of this lineage with the highest frequency going upto more than 40%. \nBR.2.1: Australia has the longest season(Jan22-Jun23) of this lineage with the highest frequency going around 40%. Feb22-Sep22 there are no occurances recorded. \nBY.1: Australia has the longest season with this lineage but with meager frequency. Canada has recorded the highest frequency in month Nov22 which is more than 30%. \nBR.1: Norway records highest frequency of this lineage which is more than 20% in the month of Sep22. \nGL.1: Australia records the highest frequency in Aug23 (more than 30%). \nEG.1.x: Germany has the highest frequency recorded 25% in Jul23. Frequencies in other countries are less than 5%. \nFK.1.x: Denmark records the highest frequency in May23 and South Korea has the longest season with htis lineage (May23-Oct23). \n \nSummary for Variants \nB.1.1.7(alpha) \n\nThis variants are observed only in 4 countries - Germany,Spain,UK,USA. \nThere are less than 5 datapoints in each of these countries and the frequencies are very low. The range is 0.006% - 0.3% \nSo considering not informative. \n \nBA.2.75 \n\nObserved in all the ten countries \nExcept in India, in all other countries the trend starts from the mid of 2022 (jun22-Aug22) . The occurances stop around Dec22 or in the first 3 months of 2023 for all the countries. \nIn India the occurances start from Jan22 onwards. However the frequency is very low in the start of the year - less than 0.5%. By the 95% CI the values can be atmost around 1.9% in start of 2022. There are no data points observed for the months Feb22,Mar22. \nIndia(56.3%),Canada(53%) has recorded the highest frequency (more than 50%) of occurance in the month of Aug22 \nSpain, Uk, Denmark has recorded constantly low frequency (less than 2.5%) in months of their existance from Aug22,jun22,jul22 respectively to Dec22. \nFor all the countries but Spain,Denmark,UK the highest peaks are observed in the months Aug22 or Sept22. \n \nBA.2.86 \n\nOccurances only observed in Denmark, Germany,Spain,Uk,USA \nThough the frequency of all the occuraces in all these countries are less than 10%. By the 95% CI Denmark records the highest frequncy (40%) of occurance in the month of Jul23. \nDenmark records occurances from Jul23-Sep23, spain and UK records occurances from Sep23-Oct23. Germany,USA records occurances only in oct23 which is very low 0.9% and 0.3% respectively. \n \nB.1.351(Beta) \n\nOccurances recorded only in Germany and USA \nIn the Month Mar22 both the countries has data points of 0.01%(Germany) and 0.02%(USA) frequency. \nAgain in sept22 Germany has recorded a datapoint of 0.3% frequency. However by 95% CI upper limit value can go upto 6.3% in sept22 \n \nB.1.617.2(Delta) \n\nThe occurances are recorded starting Jan22 in all the 10 countries. \nThe trend follows a common pattern in all the countries. High in Jan22 and a drop in Feb22, followed by continuous low frequency. \nFrom the trend I suspect that this variant would have it's first apprarance in the year 2021 which is out of the observation horizon. \nI confirm my notion by realising this is the delta variant. \nUSA,UK has frequency of 60%,65% in Jan22. Spain and Germany has frequency around 80% in Jan22. Canada and South_korea has frequency around 96% in Jan22. While countries Denmark,India,Australia,Norway has frequency less than 25% in Jan22. \nDiscontinuous occurance: In south Korea and Australia there are long time periods (more than 6 months) with no occurances and then sudden occurances are noted. For example in South_korea Apr22-Oct22 there are no datapoints and from Dec22-May23 there are no occurances. In USA there are 5 continuous months of no occurance. India has 4 months of No data points. These countries have the trend extending from 2022-2023 but it has many months of no occurnaces. \nSpain has the longest trend with no long periods of silence. From Jan22-Feb23 \nHowever apart from the high frequency in the month of Jan22, the frequency trend is quite monotonous with low numbers (less than 5%) in all the countries. \n \nP.1(Gamma) \n\nOccurance recorded only in Uk and only in Apr22 with a low frequency of 0.02% \n \nB.1.1.529(Omicron) \n\nOccurances start from Jan22 in all the ten countries. \nIn all the countries around the months of Feb22-Jun22 the frequency is very high (more than 97%) \nOn Average month from Aug22 onwards the trends are downstream in all the countries. There are some spikes during this downhill trend in South_korea(Nov22),Canada(Oct22),India(Dec22) . The Final spike during the last month of occurance(jun23) in Norway(66.66%) and Germany(16%) can be interesting. \n \nB.1.617.1(Kappa) \n\nOccurance recorded only in India in the months March22-May22. \nHighest frequency is 1.1% in Apr22 \n \nEG.5 \n\nOccurs in all the countries. \nIn India and Norway the this variant has dominated occurance for 2 months. India - Jul23-Aug23 (100%), in Norway in the only two months of occurance Aug23-Sept23 records a frequency of 100%. \nThe time line of occurance of this variant in all the countries are similar. \nNorway has the shortest trend Aug23-Sep23 \n \nXBB.1.16(omicron) \n\nThere are no occurances from Norway \nIndia sees the Spike earlier than the rest of the countries. The lowest frequency in india is higher than peaks of all other countries. \n \nXBB.1.5(omicron) \n\nWhile in most of the countries the datapoints start around Dec22-Jan23, USA sees this variant in Aug22. \nPeaks in the countries - India,Germany,Canada,Spain,Uk,USA are in the months Feb23-Apr23. \nUSA records the highest frequency \nNorway has the shortest trend Mar23-May23. \n \nXBB 1.9.1 \n\nNot Present in Australia \nNorway has the shortest trend but highest frequency (Apr23-May23) \nIn countries except Norway and Canada(May23-sept23) the onset of this varient is in the beginning months of 2023 (jan,feb) . \nIn countries Denmark,Uk, Canada there is a break of continuous 2 months where there is no datapoint \n \nXBB.1.9.2 \n\nNot present in Norway \nThere are discontinuous occurances but the periods of no occurance is less than or equal to 2 months. \nThe overall frequency of the occurances in all the country is less than 10% \n \nXBB.2.3 \n\nStarts in jan23 in India and is continuous till May23. There is a break of 3 months Jun23,Jul23,Aug23. Then there is a spike(highest frequency) in the month of Sept23-Oct23.(intereseting) \nTrends of other countries are mostly monotonous. \n \nCH.1.1 \n\nNorway records the highest frequency(61%) while for the rest of the countries the highest recorded frequency is 32%(denmark) \nIndia has the shortest trend \n \nXBB(omicron) \n\nIndia and Germany records the high frequencies 70.92%,50% respectively, while other countries have frequnency less than 20% \n \nB.1.429(Epsilon), B.1.525(Eta), B.1.526(Iota), C.37(Lambda), B.1.621(Mu), P.3(Theta), P.2(Zeta), B.1.640 No data for these variants.
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\nIntroduction: \nAll the mathematical models/equations used in the computation is taken from the lab's previous work. \nBut in lab's work x,y denotes prevalent variants of SARS_Cov2 \nIn the present work x,y denote amino acid substitutions like \"Spike_A344T\", \"Spike_A344A\" - can be assumed like covid variant harbouring only one of the aa_substitutions at a time. \nAssumption of point 3 is followed throughout. \nCurrently the work is studies the immunity against a single infection happended at the start of tthe time horizon. \n \nCalculation of probability of neutralisation: \nTo calculate the probability of neutralisation, the probability of antibodies(grouped by their epitope classes) binding to an variant harbouring an amino acid substituiton has to be found. \nThis binding probability is: b v ( t , x , y ) = e − k ∗ [ t − s ] F R x , y ( v ) ∗ I C 50 ( v ) + e − k ∗ [ t − s ] b_v(t,x,y)=\\frac{e^{-k*[t-s]}}{FR_{x,y}(v)*IC50(v)+e^{-k*[t-s]}} b v ( t , x , y ) = F R x , y ( v ) ∗ I C 50 ( v ) + e − k ∗ [ t − s ] e − k ∗ [ t − s ] \nThis equation is more like the pharmacokinetics equation - c o n c e n t r a t i o n o f l i g a n d _ r e c e p t o r c o m p l e x c o n c e n t r a t i o n o f u n b o u n d r e c e p t o r + c o n c e n t r a t i o n o f l i g a n d _ r e c e p t o r c o m p l e x \\frac{concentration\\space of\\space ligand\\_receptor\\space complex}{concentration\\space of\\space unbound\\space receptor +concentration\\space of\\space ligand\\_receptor\\space complex} co n ce n t r a t i o n o f u nb o u n d rece pt or + co n ce n t r a t i o n o f l i g an d _ rece pt or co m pl e x co n ce n t r a t i o n o f l i g an d _ rece pt or co m pl e x \nBased on x and y, the values plugged in for Fold resistance differs, that is, if x=Spike_A344T, y=Spike_A344T then FR=1; else if x=Spike_A344T,y=Spike_A344A(wt) the FR>1 (taken from the table). \nFR taken from a table is calculated for a position,epitope class combination. IC50 is fixed for an antibody antibody and doesn't change according to aa_substitution. Since IC50 and FR values considered here doesn't depend on the aa_substituition, b v ( t , x , y ) b_v(t,x,y) b v ( t , x , y ) \nIn detail: In accordance to the previous point, for a position 344, on t the b v ( t , x , y ) b_v(t,x,y) b v ( t , x , y ) 1 : x ≠ y , F R x , y ( v ) > 1 b v ( t , s _ a 344 t , s _ a 344 a ) = = b v ( t , s _ a 344 t , s _ a 344 e ) = = b v ( t , s _ a 344 t , s _ a 344 d e l ) 2 : x = = y , F R x , y ( v ) = 1 b v ( t , s _ a 344 t , s _ a 344 t ) = = b v ( t , s _ a 344 e , s _ a 344 e ) = = b v ( t , s _ a 344 a , s _ a 344 a ) \\\\1: \\space x\\neq y,\\space FR_{x,y}(v)>1\\\\ b_v(t,s\\_a344t, s\\_a344a)==b_v(t,s\\_a344t, s\\_a344e)==b_v(t,s\\_a344t, s\\_a344del)\\\\2: \\space x==y,\\space FR_{x,y}(v)=1\\\\ b_v(t,s\\_a344t, s\\_a344t)==b_v(t,s\\_a344e, s\\_a344e)==b_v(t,s\\_a344a, s\\_a344a) 1 : x = y , F R x , y ( v ) > 1 b v ( t , s _ a 344 t , s _ a 344 a ) == b v ( t , s _ a 344 t , s _ a 344 e ) == b v ( t , s _ a 344 t , s _ a 344 d e l ) 2 : x == y , F R x , y ( v ) = 1 b v ( t , s _ a 344 t , s _ a 344 t ) == b v ( t , s _ a 344 e , s _ a 344 e ) == b v ( t , s _ a 344 a , s _ a 344 a ) \nSo for each position, b v b_v b v \nEventually for each position 2 probability of neutralisation values were computed. \n \ncalculating the expected immunity against a variant/aa_substitution: \nE [ I m m u n e y ( t ) ] = ∑ x ∈ X ∑ s < t π x ( s ) ∗ I ( s ) ∗ P N e u t ( t − s , x , y ) E[Immune_y(t)]=\\sum_{x\\in X}\\sum_{s<t}\\space \\pi_x(s)*I(s)*P_{Neut}(t-s,x,y) E [ I mm u n e y ( t )] = ∑ x ∈ X ∑ s < t π x ( s ) ∗ I ( s ) ∗ P N e u t ( t − s , x , y ) here π x ( s ) \\pi_x(s) π x ( s ) \nI ( s ) I(s) I ( s ) Probability of neutralisation values are plugged in according to the x,y combination. If x==y the PNeut for the position, calculated using FR=1 is used else the other one is used. \n \n\ny = S p i k e _ F 140 I X = { S p i k e _ F 140 I , S p i k e _ F 140 F } t = 03 − 01 − 2022 E [ S _ f 140 i i m m u n e ( 03 − 01 − 2022 ) ] = { [ π S _ f 140 i ( 01 − 01 − 2022 ) ∗ P 1 N e u t ( p o s = 140 , t = 01 − 01 − 2022 ) ] + [ π S _ f 140 i ( 02 − 01 − 2022 ) ∗ P 1 N e u t ( p o s = 140 , t = 02 − 01 − 2022 ) ] } + { [ π S _ f 140 f ( 01 − 01 − 2022 ) ∗ P 2 N e u t ( p o s = 140 , t = 01 − 01 − 2022 ) ] + [ π S _ f 140 f ( 02 − 01 − 2022 ) ∗ P 2 N e u t ( p o s = 140 , t = 02 − 01 − 2022 ) ] } \\\\y=Spike\\_F140I\\\\ X=\\{Spike\\_F140I,Spike\\_F140F\\}\\\\t=03-01-2022\\\\E[S\\_f140i_{immune}(03-01-2022)]\\\\=\\{[\\pi_{S\\_f140i}(01-01-2022)*P1_{Neut}(pos=140,t=01-01-2022)]+[\\pi_{S\\_f140i}(02-01-2022)*P1_{Neut}(pos=140,t=02-01-2022)]\\}+\\\\\\{[\\pi_{S\\_f140f}(01-01-2022)*P2_{Neut}(pos=140,t=01-01-2022)]+[\\pi_{S\\_f140f}(02-01-2022)*P2_{Neut}(pos=140,t=02-01-2022)]\\} y = Sp ik e _ F 140 I X = { Sp ik e _ F 140 I , Sp ik e _ F 140 F } t = 03 − 01 − 2022 E [ S _ f 140 i imm u n e ( 03 − 01 − 2022 )] = {[ π S _ f 140 i ( 01 − 01 − 2022 ) ∗ P 1 N e u t ( p os = 140 , t = 01 − 01 − 2022 )] + [ π S _ f 140 i ( 02 − 01 − 2022 ) ∗ P 1 N e u t ( p os = 140 , t = 02 − 01 − 2022 )]} + {[ π S _ f 140 f ( 01 − 01 − 2022 ) ∗ P 2 N e u t ( p os = 140 , t = 01 − 01 − 2022 )] + [ π S _ f 140 f ( 02 − 01 − 2022 ) ∗ P 2 N e u t ( p os = 140 , t = 02 − 01 − 2022 )]}
\n \nThis way for each variant/aa_substitution the expected immunity is calculated for everyday in the period of observation.
\nAlgorithm of computation of expected immunity: Function get_dis_fact:\n Pass In: start_date,end_date\n num_day=end_date-start_date\n k - is the half life\n For i=0 to num_day\n discount_factor_vector.append(round(exp(-k*i),3))\n return(discount_factor_vector)\n\nFunction get_bv:\n Pass In: fr,IC50\n Constants: start_date, End_date\n calling get_dis_fact function\n time_iter=start_date\n while(time_iter<End_date):\n days_dif=time_iter-start_date\n bv_vec.append(discount_factor_vector[days_dif]/\n (fr*IC50+discount_factor_vector[days_diff]))\n increment time_iter\n return(bv_vec)\n\nFunction Exp_immune:\n Pass In: Frequency_df,P1_neut,P2_neut, incidence\n Constant: Start_day\n For y in variants:\n For d in observation_dates:\n initialise data_frames freq_df,pneut_df,incidence_df\n dates=[start_date:d) (d not included s<t)\n dates_diff=[start_date-dates]\n For x in variants with mutation on same position as y:\n freq_df.append(Frequency_df[dates,x])\n incidence_df.append(incidence[dates])\n if y==x:\n pneut_df.append(p1[pos,dates_diff])\n elif y!=x:\n pneut_df.append(p2[pos,dates_diff])\n double_sum.sum(freq_df*pneut_df*incidence_df)\n immune_df.append(double_sum)\n return(immune_df) \nAll the scripts are present in \"/Users/vishnushirishyamsaisundar/Documents/Master_Thesis_work/Work/Data_Analysis/Expected_immunity_computation\"
\nObservations and modifications: \nIt was observed that P1(FR==1),P2(FR>1) are very similar for a position. Thats because probability of binding doesn't just depend on the FR values; Very low IC50 values can also rise the binding probability. \nIf one epitope classes's binding probability is higher, it can inturn increase the probaility of neutralisation of that position. \nTo Tackle this for all positions' average IC50 (mean of all the IC50 values available in the DMS data) values were used. \nSince we are not keen on the antibodies and the epitope class they belong to, average FR values for each position was computed and plugged in. \nNow PNeut becomes equal to the Binding probability, since the antibody epitope class distinction is ruled out. \nComputing probability of neutralisation/binding probability with FR=1 will remain the same for all positions for all the time steps. So this is computed only once and then is used irrespective of position. \n \nVisualisation and analysis: \nThere are around 199 plots \nAll the positions along with the variants have similar trends \nIn majority of the plots there is one variant for which there is high immunity while the others group at lower level \nThe trend of the variant having the high immunity has three distinct crests; Few positions have slight variation in this, the trend that has has only 2 crests. \nThese positions include 493, 446, 346, 490, 486, 445. \nIn all the above cases, except for position 493, wildtype has the highest immunity among people. \n180 positions out of 199 positions have high immunity against the wildtype variant. \nTo see what sets the immunity of one variant appart, the frequency of the variants of a position is plotted. \nIt is observed that the variant with high frequency records high immunity against aswell. \nFor an instance if we consider position 493, \nMy inference from the plot is that, since the frequency is high, more people are infected and so more people are immune against the same variant. \nImmunity against variants that has higher frequecy after a period in the observation period doesn't have peaks high like the former one. This can be because we consider the infection to happen on the first day of the observation horizon. \n \nFurther questions: Finding_surface_residues \nDSSP server \nDSSP server
\n\nInput : PDB file (6xlu) \nThe output contains a column names ACC. The values in this column is either number of water molecule in contact with the residue*10 or Residue's water exposed surface in A ˚ 2 \\AA^2 A ˚ 2 \nThe ouput obtained was causing problems when read through R. \nTo circumvent this, dssp was installed in the system and the protein stucture files (6xlu.pdb) was used to obtain the dssp file. \n \ndssp 6xlu.pdb 6xlu.dssp\n\nThis dssp file was parsed in R using the function parse.dssp from ptm package. \n \nGet Area server \nGETAREA
\n\nInput: PDB file (6xlu) \nThe output contains a columns saying if the residue is inside or outside - represented as (i/o)\n \nThe output is displayed on the webpage \nThis output was manually copied and was converted into a csv file after doing some text editing. This way the file could be easily read in R \n \nNetSurfP 3.0 \nNetSurfP 3.0
\n\nInput: FASTA sequence is submitted \nOutput: It gives the solvent accessibility, relative solvent accessibility which based on a treshold of 25% - more than 25% the residue is exposed, less than that the residue is burried.\n \nThe RSA values can be taken from the csv file which could be downloaded from the result site. \n \n\nNote:\nA pariwise sequence alignment was done with the uniprot spike sequence and the sequence obtained from the 6xlu structure.
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\n \n\nGISAID was again queried for number of entries for one of the months for which the values deviated in South_korea data (OCT23).
\n \n\nThe result of the query neighter matched with the number in the file or with the number got from the snippet. It was higher than both.
\n \n\nThis it when I realised that entries from older months are uploaded to GISAID anytime. This can be seen through the submission date column in GISAID - Entries with old collection date and recent submission date.
\n \n\nSo the difference between the numbers in the file and the numbers from downloaded entries could be reasoned out to be the data submitted during the time gaap between compiling the csv file and downloading the data (nearly 1 week).
\n \n \nBacklinks \n","noteIndex":{"id":"paa0s59lp320n6q8rghycjw","title":"Work on SARS Cov2","desc":"","updated":1700240746536,"created":1700233379150,"custom":{"nav_order":0,"permalink":"/"},"fname":"root","type":"note","vault":{"fsPath":".","selfContained":true,"name":"Literature"},"contentHash":"6124b7f80065618c049c13a49f0dbed1","links":[],"anchors":{},"children":["1hnn8maoednnxdw5feqd4kq","nve3ld8zkeg6b2hgygrh1rp","v4t96j5kch5mq12272qsyih","r423m96u71ix4pb458fk8u2"],"parent":null,"data":{},"body":"\n"},"collectionChildren":null,"customHeadContent":null,"config":{"version":5,"dev":{"enablePreviewV2":true,"enableSelfContainedVaults":true},"commands":{"lookup":{"note":{"selectionMode":"extract","confirmVaultOnCreate":true,"vaultSelectionModeOnCreate":"smart","leaveTrace":false,"bubbleUpCreateNew":true,"fuzzThreshold":0.2}},"randomNote":{},"insertNoteLink":{"aliasMode":"none","enableMultiSelect":false},"insertNoteIndex":{"enableMarker":false},"copyNoteLink":{"aliasMode":"title"},"templateHierarchy":"template"},"workspace":{"vaults":[{"fsPath":".","selfContained":true,"name":"Literature"}],"journal":{"dailyDomain":"daily","name":"journal","dateFormat":"y.MM.dd","addBehavior":"childOfDomain"},"scratch":{"name":"scratch","dateFormat":"y.MM.dd.HHmmss","addBehavior":"asOwnDomain"},"task":{"name":"task","dateFormat":"y.MM.dd","addBehavior":"asOwnDomain","statusSymbols":{"":" ","wip":"w","done":"x","assigned":"a","moved":"m","blocked":"b","delegated":"l","dropped":"d","pending":"y"},"taskCompleteStatus":["done","x"],"prioritySymbols":{"H":"high","M":"medium","L":"low"},"todoIntegration":false,"createTaskSelectionType":"selection2link"},"graph":{"zoomSpeed":1,"createStub":false},"enableAutoCreateOnDefinition":false,"enableXVaultWikiLink":false,"enableRemoteVaultInit":true,"enableUserTags":true,"enableHashTags":true,"workspaceVaultSyncMode":"noCommit","enableAutoFoldFrontmatter":false,"enableEditorDecorations":true,"maxPreviewsCached":10,"maxNoteLength":204800,"enableFullHierarchyNoteTitle":false},"preview":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enablePrettyRefs":true,"enableKatex":true,"automaticallyShowPreview":false},"publishing":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enablePrettyRefs":true,"enableKatex":true,"copyAssets":true,"siteHierarchies":["root"],"writeStubs":false,"siteRootDir":"docs","seo":{"title":"Dendron","description":"Personal Knowledge Space"},"github":{"enableEditLink":true,"editLinkText":"Edit this page on GitHub","editBranch":"main","editViewMode":"tree"},"enableSiteLastModified":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enableRandomlyColoredTags":true,"enableTaskNotes":true,"enablePrettyLinks":true,"searchMode":"search","siteUrl":"https://vishnushiri02.github.io","assetsPrefix":"/SARS-Cov2","siteFaviconPath":"favicon.ico","siteIndex":"root"}}},"__N_SSG":true}
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\nSteps in the workflow \n\n graph TD;\n1[start]--Based on data quantity-->A[Choosing countries for the study]--checking for extreme low value outliers-->B[Downloading sequence and meta data from these countries for 22 months]--Mapping lineages to parental lineage-->C[Finding the difference in the trend of variant frequencies in these countries]\n
\n\nData acquisition \n\nList of locations available in GISAID was obtained from GISAIDR Git repo - GISAID_LOCATIONS.txt \nThe following points/steps are coded in the file Work/Data_Analysis/Monthwise_data_submission.Rmd\n\nFrom the GISAID, the total number of high coverge Work/Data_Analysis/country_monthly_submission.csv \nCountries that had more than 10,000 sequences deposited in total in the past 22 months were alone considered further. \nThis included 10 countries : India, South Korea, Denmark, Germany, Norway, Spain, United Kingdom, Canada, USA, Australia. Work/Data_Analysis/chosen_ten_country_submission.csv \nThe trend of the time series submission statistics of these countries were plotted. It is present in the file Work/Data_Analysis/plots/ten_Country_data_trend_plots.pdf\n \nOutlier detection and pruning of the observation horizon(Time period considered): Extreme low values were searched for, using the Hampler Filter (one of the Outlier Estimation and methods ). outliers_mad from Routliers package was used . Since there were no extreme low values found in any of the countries, the observation time period was not pruned. \nThe sequence data for 10 countries were downloaded. There are files with crude data - all data retrieved from GISAID for each of the accesssion ID (29 cols of data) and there are files with essential columns: strain,virus,collection_date,country,pangolineage,sequence, length of the sequence, GISAID accession_id. The crude data for 10 countries are present 10 files in the folder Work/Data_Analysis/ten_country_crude_data_seq and the essential column files are in the folder Work/Data_Analysis/ten_country_ess_data \nThe Rmd also has a codeblock for downloading data with AA_substitution. This data was needed for mapping lineages to their parental lineage. For this I used the modified download script(modified by me) Work/Data_Analysis/Modified_GISAIDRdownload.R.\n\nThis can be done by fix(Download) and modifying the script as we need
\n \n \nThis mutation data are stored in Work/Data_Analysis/ten_country_mut_data \n \n \n \n\nThere were some inconsistencies in the Date entries (only year no month or year and month an no date) such entries were removed.\nOn the course of doing this it was identified that the number of entries that was downloaded for each countries do not exactly match the numbers in Work/Data_Analysis/chosen_ten_country_submission.csv excluding few countries.\nAfter removing the inconsistent date entries the numbers(downloaded and number in file) for India match.\nThe inconsistencies are reported in the file Work/Data_Analysis/inconsistencies_in_data.numbers.\nThe number of downloaded entries are more than then numbers in file.\nAfter looking into it, it was observed that there were few more entries added to these countries in the time gap(around a week) between the compiling Work/Data_Analysis/chosen_ten_country_submission.csv and downloading data. checking inconsistencies
\nSummary \nSummary of the downloaded data
\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Country Total number of entries downloaded (Jan22-Oct23) Entries after removing the date inconsistencies Number of unassigned Percentage of unassigned Asia / India 18106 15783 0 0 % Asia / South Korea 13808 13808 0 0 % Europe / Denmark 163900 163900 7 0,004 % Europe / Germany 66374 66236 0 0 % Europe / Norway 12264 12264 0 0 % Europe / Spain 17991 17921 0 0 % Europe / United Kingdom 32835 30597 74 0,242 % North America / Canada 20791 20791 0 0 % North America / USA 75487 74971 5 0,007 % Oceania / Australia 13267 13257 5 0,038 %
Note on the unassigned
\nMapping lineages \n\nawk -F\";\" '{gsub(/[()]/,\"\",$6);print $5\";\"$6}' \nten_country_mut_data/* \\ \n> ten_country_mut_data/ten_country_lineage_mut.csv\n\ngsub is used to remove the brackets in the AA_mutations column.
\n \n\nFrequency computation \n\nAfter the lineage mapping, for each country the frequency of presence of each parental_lineage(which also includes the sublienages) in a month was calculated (c o u n t o f l i n e a g e B i n j u l t o t a l e n t r i e s i n j u l \\frac{count\\space of\\space lineage\\space B\\space in\\space jul}{total\\space entries\\space in\\space jul} t o t a l e n t r i es in j u l co u n t o f l in e a g e B in j u l Confidence interval . This function also calculates frequency/proportion so a seperate computation was not required.95% confidence interval with method sisonglaz was computed. The plots are stored in PDF file, 1 plot for each country so 10 pages - Work/Data_Analysis/plots/country_wise_frequency_trend.pdf without CI and Work/Data_Analysis/plots/country_wise_frequency_trend_CI.pdf with CI. \nTo make it more convenient plots for each Parental_lineage/variant grouped by country was plotted to do the analysis. These are present in the pdf files - Work/Data_Analysis/plots/Variant_wise_trend_all_countries.pdf without CI and Work/Data_Analysis/plots/Variant_wise_trend_all_countries_CI.pdf with CI \nThese plots are analysed to bring out the difference in the variant trend among countries. An entire spreedsheet was developed manually basically describing the variant trends in words Work/Data_Analysis/plot_Analysis_report.numbers, and the summary of the analysis is in Analysis of the varient trend plots file. \nTo not miss on the difference of pandemic in countries the lineages which are not variants were also used for comparison like the variants. There are 300+ plots in the file each page corresponding to one lineage which is not a variants and the data is grouped by country. This is presetn in Work/Data_Analysis/plots/Lineages_no_var_freq_countrywise_CI with CI and in Work/Data_Analysis/plots/Lineages_no_var_freq_countrywise without CI. \nFrom these plots trends that look interesting are chosen and noted in Analysis of the varient trend plots \n \nDealing with the unassigned \n\nFor few entries of the GISAID downloaded data the lineage was not assigned. The lineage column had either a blank space or it was termed as unassigned. There were in total 91 unassigned entries in the downloaded 10 country data. \nDenmark 7 entries ,UK 74 entries, USA 5 entries, Australia 5 which is Denmark 0.004%, UK 0.242%, USA 0.007%, Australia 0.038% of the entries in the respective countries. \nMajority of the unassigned entries were collected in Oct23 \nTo get the possible assignment for these entries few methods were tried out. \nFirst among them was to calculate distance between the unassigned and all other lineages based on their spike mutations. For this process the characteristics mutation for each lineage was obtained using the outbreakinfo function getMutationsByLineage \nThough this method seems to work when the method was checked with entries with known assignments, the method did not perform well. \nThe main reason that this wouldn't work is beacuse not all the characteristic spike mutation is always present in the GISAID downloaded data, so when distance is computed there is always discrepencies. \nHence to go with an accurate method genome sequences of these unassigned entries were obtained and a fasta file was compiled. \nThis file unassigned_sequences.fasta was uploaded in the https://pangolin.cog-uk.io to get the assignment. \nOnce the assignment was obtained it was added to the dataset. This part is included in the fill_parental function of lineage_mapping. The trends were reanalysed after assigning the unassigned.\n\nThe https://pangolin.cog-uk.io was not able to analyse the 5 sequences from australia. No reasons was provided.
\n \n \n \n
\n
\n\nAll these are present in the Work/Data_Analysis/assigning_the_unassigned.Rmd \n \nFinding positions under pressure (BIG GOAL) \nThe big goal is to find the positions under pressure. To obtain this, firstly the frequency of mutation on each position of Spike RBD and NTD Epitopes are calculated. (RBD spike mutations in position 330-530, NTD(14-20,140-158,245-264) mutations)(Question on the position ) For this the aa_substitution data obtained from GISAID was used.\n
\nSTEPS:
\n\nFor Each country the mutation data from GISAID has been used as the input. \nThis data consist of the lineage, collected date, location of collection, a string of aa_substitution. \nAll the aa_substitution strings that are collected on the same day are considered together. This also gives the number of entries collected in a day = number sequences colleceted on a day. \nFrom these strings the spike RBD, NTD epitope mutations are alone extracted using getintrmut function. \nThe data frame gets reduced to date,number of sequences,spike RBD/NTD mutations string. \nTo calculate the frequency of a mutation for a time step, all the possible unique spike RBD, NTD mutations present in the country is first compiled. \nThen the frequency of each of this mutation for a day is calculated.\n\nFrequency of pos_373 on 01-01-2022 = c o u n t o f m u t a t i o n s i n p o s _ 373 o n 01 − 01 − 2022 N u m b e r o f s e q u e n c e s o n 01 − 01 − 2022 \\frac{count\\space of\\space mutations\\space in\\space pos\\_373\\space on\\space 01-01-2022}{Number\\space of\\space sequences\\space on\\space 01-01-2022} N u mb er o f se q u e n ces o n 01 − 01 − 2022 co u n t o f m u t a t i o n s in p os _373 o n 01 − 01 − 2022
\n \n \nTo have the frequency data for everyday from Jan 1 2022 - Oct 31 2023, linear interpolation is done using the approximate method. The result of the interpolation is added to the corresponding date in the country data_frame. This step is necessary to fill in the data gaps. \n \nQuestion on interpolation
\nComputing the pressure on the position \nWith the formula (given by Prof.Max) the pressure on each position from t0 to t was computed\nP ( p o s , t ) = ∑ s = t 0 t exp − k [ t − s ] × f ( p o s , s ) \\\\ P(pos,t)=\\sum_{s=t_0}^{t}\\exp^{-k[t-s]}\\times f(pos,s) \\\\ P ( p os , t ) = ∑ s = t 0 t exp − k [ t − s ] × f ( p os , s )
\n\nWhere the f(pos,t) is the frequency of the position on time t. \ne x p − k [ t − s ] exp^{-k[t-s]} e x p − k [ t − s ] k ∼ l n ( 2 ) 45 + 14 k\\sim \\frac{ln(2)}{45+14} k ∼ 45 + 14 l n ( 2 ) By suggestion of the Prof. the vector for discount factor was first computed. For this the date range for each of the country_df was found. If the difference between the start day and the end day is 9 then [t-s] could be in the range 0-9. Hence with this as base the discount factor was computed for [t-s] ranging 0-[difference between the start day to end day in the dataframe]. All these values are stored in a vector. \nFrom the start date to the end date the pressure was calculated for everday in the following manner \n\nP ( p o s , d 1 ) = exp − k [ d 1 − d 1 ] × f ( p o s , d 1 ) P ( p o s , d 2 ) = exp − k [ d 2 − d 1 ] × f ( p o s , d 1 ) + exp − k [ d 2 − d 2 ] × f ( p o s , d 2 ) P ( p o s , d 3 ) = exp − k [ d 3 − d 1 ] × f ( p o s , d 1 ) + exp − k [ d 3 − d 2 ] × f ( p o s , d 2 ) + exp − k [ d 3 − d 3 ] × f ( p o s , d 3 ) \\\\ P(pos,d1)=\\exp^{-k[d1-d1]}\\times f(pos,d1) \\\\ P(pos,d2)=\\exp^{-k[d2-d1]}\\times f(pos,d1) + \\exp^{-k[d2-d2]}\\times f(pos,d2)\\\\ P(pos,d3)=\\exp^{-k[d3-d1]}\\times f(pos,d1) + \\exp^{-k[d3-d2]}\\times f(pos,d2) + \\exp^{-k[d3-d3]}\\times f(pos,d3) P ( p os , d 1 ) = exp − k [ d 1 − d 1 ] × f ( p os , d 1 ) P ( p os , d 2 ) = exp − k [ d 2 − d 1 ] × f ( p os , d 1 ) + exp − k [ d 2 − d 2 ] × f ( p os , d 2 ) P ( p os , d 3 ) = exp − k [ d 3 − d 1 ] × f ( p os , d 1 ) + exp − k [ d 3 − d 2 ] × f ( p os , d 2 ) + exp − k [ d 3 − d 3 ] × f ( p os , d 3 ) \nThis was done for all the RBD, NTD positions in a country and repeated for all the 10 countries. \nThe output will have one column with the RBD and NTD positions and with columns as many as the number of days with data in the country\n \n \nMasking \n\nThe objective of masking to find the exposed positions among the RBD, NTD spike positions. \nTo know the exposed residues the solvent accessibility of each of the residues in the spike protein were found. Question on solvent acessibility \nTo find solvent accessibility of the protein various tools were utilitsed. This can be found hereFinding_surface_residues \nFrom the output of each of the tool Spike RBD,NTD surface positions were found. This process was direct in the case of the output from GetArea and Netsurf3.0 but in the case of DSSP, relative solvent accessibility was computed from the absolute solvent accessibility in the dssp output file. Using this computed relative solvent accessibility the surface residues were found. \n \n\nThe categorising treshold is set as 25% similar to Netsurf3.0. This would need advice.
\n \n\nThe positions being picked by each of the tools are compared with the positions given by Prof.Max(screenshot). The tabulation is available in Work/Data_Analysis/Positions_being_picked.numbers
\nVisualization of the positions under pressure \nLike previous variant trend visualization, the pressure trend was also visualised using geom_line in two ways, positionwise and countrywise. These plots are present in Work/Data_Analysis/pressure_plots. There are 6 plots - 3 countrywise, 3 positionsise. The 3 denotes the 3 masking tool outputs that were used to generate the plots.
\nEntropy for each positions: \nThe amino acid substitution on a position is not constant. Example: on position 14 mutations found in india include Spike_Q14H,Spike_Q14del,Spike_Q14R. So calculating the number of such prevailing mutations on a position is the entropy. This was calculated to weigh down the pressure on a postiion .... Not processed further yet
\nNeutralisation probability: \nFor knowing how impactful would a mutation on a site be, the probaility of neutralisation is being calculated. The Probability of neutralisation of a variant y by the antibodies elicited by variant x at time t.
\nP N e u t ( t , x , y ) = 1 − ∏ v ∈ A x / y ( 1 − b v ( t , x , y ) ) \\\\ P_{Neut}(t,x,y)=1-\\prod_{v\\in A_{x/y}}(1-b_v(t,x,y)) P N e u t ( t , x , y ) = 1 − v ∈ A x / y ∏ ( 1 − b v ( t , x , y )) To compute this probability the binding probability b v ( t , x , y ) b_v(t,x,y) b v ( t , x , y )
\nb v ( t , x , y ) = e x p − k [ t − s ] F R x , y ( v ) . I C 5 0 x ( v ) + e x p − k [ t − s ] \\\\ b_v(t,x,y)=\\frac{exp^{-k[t-s]}}{FR_{x,y}(v).IC50_x(v)+exp^{-k[t-s]}} b v ( t , x , y ) = F R x , y ( v ) . I C 5 0 x ( v ) + e x p − k [ t − s ] e x p − k [ t − s ] \nThe discount factor is used here to represent the diminishing concentration of the antibodies elicited by variant x. F R x , y ( v ) FR_{x,y}(v) F R x , y ( v ) ( v ) (v) ( v ) \nThe probability of binding is computed in the same manner as the pressure. For each postion that is getting mutated, for each of the antibodies the fold resistance and IC50 that was provided was used. These values were obtained from DMS data and was used in the predecessor work of the lab. \nTo compute the binding probability the FR and IC50 of antibodies belonging to the same epitope class was averaged and then used to compute the binding probability for an epitope class. \nThe resulting dataframe has the position, epitope class, b v b_v b v \nWith the computed two binding probability dataframes, two probability of neutralisation dataframes are computed. P 1 N e u t P_{1_{Neut}} P 1 N e u t P 2 N e u t P_{2_{Neut}} P 2 N e u t \nTo compute the probability of neutralisation, for each position, for each time step, the binding probability of all the antibody classes for the time step are considered. \nThe weight is computed by P 1 N e u t ( w i t h F R = 1 ) P 2 N e u t \\frac{P_{1_{Neut}}\\space (with FR=1)}{ P_{2_{Neut}}} P 2 N e u t P 1 N e u t ( w i t h FR = 1 ) \nThe computed weight is then multiplied with the pressure timestepwise and then the weighted pressure is plotted - Positionwise and countrywise; all the positions are considered irrespective of being exposed or burried. Work/Data_Analysis/pressure_plots/pos_pressure_trend_avg_wtpressure.pdf, Work/Data_Analysis/pressure_plots/pressure_trend_avg_wtpressure.pdf \nWork/Data_Analysis/Big_goal.Rmd, Work/Data_Analysis/neutralisation_probability.Rmd has all the scripts regarding the big goal. \n\nAnother method: binding probability for all the time steps for all the antibodies were computed first and then averaging these binding probability of antibodies across an antibody class was done and used to find the probability of neutralisation. The weight values did not differ drasitically to be precise there are only 116 values that differ. Just sticking to the earlier method.\nDefinition Reference
\n \nAnalysis of the weighted plot \n\n\nOverall trend across all the countries:
\n\nThe plots Pictures represents the overall trend for the grouped positions
\n \n Grouping positions by similar pressure trends
\n The similar trend positions were visualised to see if, the are very near to experience same trend of pressure.
\n\n\n373,142,501,19,440,505,478,371,417,376,339,408,375,405,498,477, 484
\n\nposition 19 is missing in the structure
\n \n \n\n496,143,145
\n\nposition 145 is missing in the structure
\n \n \n\n493 the trend is like the previous but with an offset in the peak
\n\nThis position is 8.1A ˚ \\AA A ˚
\n \n \n\n446,346
\n\npos 446 is missing in the structure
\n \n \n\n157,452
\n \n\n158,156
\n \n\n144 similar to the previous but many countries have trends of high pressure at the end of the observation timeline
\n\nposition 144 not found in the structure
\n \n \n\n245,153,450,18
\n\npos 18,153 are absent in the struct
\n \n \n\n146,445,368,252,490,460,486
\n\n146, 252, 445 are absent in the structure
\n \n \n\n152,147,257,356,444
\n\n152, 147, 257 are absent in the structure
\n \n \n\n253,521
\n\npos 253 is absent in the structure
\n \n \n\n455,456
\n \n \n \n\nVisually the positions that have similar pressure trends are not always in proximity on the folded protein. Since this is a static structure, there might be dynamics which can bring these positions together but this is just an hypothesis.
\n \n\n256 high pressure is observed in only australia at the end of the observation period
\n \n\n344,494 Germany has higher pressure than others
\n \n\n248 India and Norway records high pressure comparing to others
\n \n \nComparison of pressure trend with the variant trend \n\nTo do this the characteristic mutations of each variants were obtained using the getMutationsByLineage function from outbreakinfo package. From these the positions were alone taken. \nBut set of positions are not exclusive for a variant, there is major overlap. That is positions in first group belongs to the characteristics mutation of almost all the variants. So based on the mutation position pressure trend its difficult to draw a corelation with variant trend. \nConsidering mutations would be a better way to go ahead. \nI think considering individual mutations would give allow us to visualise occurance of mutation on a position after the pressure peaks on a position \n \nRepeating the process with Mutations \n\nFrom frequency calculation to computing the weighted pressure the process was repeated again for individual mutations. \nThe visualization is slightly different. For each country there is a separate pdf. The pressure trends of mutations on the same positions are plotted together in a graph with each line representing a mutation. \nFew trends are promising. \nBut there are trends that needs little more effort to look into \n \nChildren \n\nExpected_immunity Finding_surface_residues Mapping_lineages Possible_questions checking inconsistencies \nBacklinks \n","noteIndex":{"id":"paa0s59lp320n6q8rghycjw","title":"Work on SARS Cov2","desc":"","updated":1700240746536,"created":1700233379150,"custom":{"nav_order":0,"permalink":"/"},"fname":"root","type":"note","vault":{"fsPath":".","selfContained":true,"name":"Literature"},"contentHash":"6124b7f80065618c049c13a49f0dbed1","links":[],"anchors":{},"children":["1hnn8maoednnxdw5feqd4kq","nve3ld8zkeg6b2hgygrh1rp","v4t96j5kch5mq12272qsyih","r423m96u71ix4pb458fk8u2"],"parent":null,"data":{},"body":"\n"},"collectionChildren":null,"customHeadContent":null,"config":{"version":5,"dev":{"enablePreviewV2":true,"enableSelfContainedVaults":true},"commands":{"lookup":{"note":{"selectionMode":"extract","confirmVaultOnCreate":true,"vaultSelectionModeOnCreate":"smart","leaveTrace":false,"bubbleUpCreateNew":true,"fuzzThreshold":0.2}},"randomNote":{},"insertNoteLink":{"aliasMode":"none","enableMultiSelect":false},"insertNoteIndex":{"enableMarker":false},"copyNoteLink":{"aliasMode":"title"},"templateHierarchy":"template"},"workspace":{"vaults":[{"fsPath":".","selfContained":true,"name":"Literature"}],"journal":{"dailyDomain":"daily","name":"journal","dateFormat":"y.MM.dd","addBehavior":"childOfDomain"},"scratch":{"name":"scratch","dateFormat":"y.MM.dd.HHmmss","addBehavior":"asOwnDomain"},"task":{"name":"task","dateFormat":"y.MM.dd","addBehavior":"asOwnDomain","statusSymbols":{"":" ","wip":"w","done":"x","assigned":"a","moved":"m","blocked":"b","delegated":"l","dropped":"d","pending":"y"},"taskCompleteStatus":["done","x"],"prioritySymbols":{"H":"high","M":"medium","L":"low"},"todoIntegration":false,"createTaskSelectionType":"selection2link"},"graph":{"zoomSpeed":1,"createStub":false},"enableAutoCreateOnDefinition":false,"enableXVaultWikiLink":false,"enableRemoteVaultInit":true,"enableUserTags":true,"enableHashTags":true,"workspaceVaultSyncMode":"noCommit","enableAutoFoldFrontmatter":false,"enableEditorDecorations":true,"maxPreviewsCached":10,"maxNoteLength":204800,"enableFullHierarchyNoteTitle":false},"preview":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enablePrettyRefs":true,"enableKatex":true,"automaticallyShowPreview":false},"publishing":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enablePrettyRefs":true,"enableKatex":true,"copyAssets":true,"siteHierarchies":["root"],"writeStubs":false,"siteRootDir":"docs","seo":{"title":"Dendron","description":"Personal Knowledge Space"},"github":{"enableEditLink":true,"editLinkText":"Edit this page on GitHub","editBranch":"main","editViewMode":"tree"},"enableSiteLastModified":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enableRandomlyColoredTags":true,"enableTaskNotes":true,"enablePrettyLinks":true,"searchMode":"search","siteUrl":"https://vishnushiri02.github.io","assetsPrefix":"/SARS-Cov2","siteFaviconPath":"favicon.ico","siteIndex":"root"}}},"__N_SSG":true}
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\nConfidence interval \nIn general a 95% confidence interval means there is 95% probability that the confidence interval contains the mean.2 To understand what is CI of a sample proportion, the term population proportion is defined first.
\n\nA population proportion is the proportion of individuals in a population sharing a certain trait, denoted as p. The sample proportion is the proportion of individuals in a sample sharing a certain trait, denoted ˆp.3
\n \nJust like the estimating the CI of mean the CI of proportion is estimated by adding and subtracting margin of error from ^p to get the limits of CI.
\nM a r g i n o f E r r o r = z × p ^ × ( 1 − p ^ ) n \\\\ Margin\\space of\\space Error\\space =z\\times\\sqrt{\\frac{\\hat p\\times(1-\\hat p)}{n}} M a r g in o f E rror = z × n p ^ × ( 1 − p ^ ) Where z is the z-score for 95% confidence level.4 \nFor multinomial sample prortions the confidence intervals are often approximated by single binomial confidence interval, I assume the trait of iterest is considered as p ^ \\hat p p ^ ( 1 − p ^ ) (1-\\hat p) ( 1 − p ^ )
\nInterpolation \nFinding a new datapoint based on the preexisting data point is called the interpolation. Common methods of interpolation includes linear,polynomial, spline interpolation. Linear interpolation fits a stright line between known points and uses the slope off the line to interpolate the missing data points. In both polynomial and spline interpolation polynomials are used to do the interpolation. The difference is that spline fits multiple piecewise polynomials to the subset of data to do the interpolation, on the other hand polynomial interpolation fits one polynomial to the entire data to do the interpolation.5
\nBacklinks \n","noteIndex":{"id":"paa0s59lp320n6q8rghycjw","title":"Work on SARS Cov2","desc":"","updated":1700240746536,"created":1700233379150,"custom":{"nav_order":0,"permalink":"/"},"fname":"root","type":"note","vault":{"fsPath":".","selfContained":true,"name":"Literature"},"contentHash":"6124b7f80065618c049c13a49f0dbed1","links":[],"anchors":{},"children":["1hnn8maoednnxdw5feqd4kq","nve3ld8zkeg6b2hgygrh1rp","v4t96j5kch5mq12272qsyih","r423m96u71ix4pb458fk8u2"],"parent":null,"data":{},"body":"\n"},"collectionChildren":null,"customHeadContent":null,"config":{"version":5,"dev":{"enablePreviewV2":true,"enableSelfContainedVaults":true},"commands":{"lookup":{"note":{"selectionMode":"extract","confirmVaultOnCreate":true,"vaultSelectionModeOnCreate":"smart","leaveTrace":false,"bubbleUpCreateNew":true,"fuzzThreshold":0.2}},"randomNote":{},"insertNoteLink":{"aliasMode":"none","enableMultiSelect":false},"insertNoteIndex":{"enableMarker":false},"copyNoteLink":{"aliasMode":"title"},"templateHierarchy":"template"},"workspace":{"vaults":[{"fsPath":".","selfContained":true,"name":"Literature"}],"journal":{"dailyDomain":"daily","name":"journal","dateFormat":"y.MM.dd","addBehavior":"childOfDomain"},"scratch":{"name":"scratch","dateFormat":"y.MM.dd.HHmmss","addBehavior":"asOwnDomain"},"task":{"name":"task","dateFormat":"y.MM.dd","addBehavior":"asOwnDomain","statusSymbols":{"":" ","wip":"w","done":"x","assigned":"a","moved":"m","blocked":"b","delegated":"l","dropped":"d","pending":"y"},"taskCompleteStatus":["done","x"],"prioritySymbols":{"H":"high","M":"medium","L":"low"},"todoIntegration":false,"createTaskSelectionType":"selection2link"},"graph":{"zoomSpeed":1,"createStub":false},"enableAutoCreateOnDefinition":false,"enableXVaultWikiLink":false,"enableRemoteVaultInit":true,"enableUserTags":true,"enableHashTags":true,"workspaceVaultSyncMode":"noCommit","enableAutoFoldFrontmatter":false,"enableEditorDecorations":true,"maxPreviewsCached":10,"maxNoteLength":204800,"enableFullHierarchyNoteTitle":false},"preview":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enablePrettyRefs":true,"enableKatex":true,"automaticallyShowPreview":false},"publishing":{"enableFMTitle":true,"enableNoteTitleForLink":true,"enablePrettyRefs":true,"enableKatex":true,"copyAssets":true,"siteHierarchies":["root"],"writeStubs":false,"siteRootDir":"docs","seo":{"title":"Dendron","description":"Personal Knowledge Space"},"github":{"enableEditLink":true,"editLinkText":"Edit this page on GitHub","editBranch":"main","editViewMode":"tree"},"enableSiteLastModified":true,"enableFrontmatterTags":true,"enableHashesForFMTags":false,"enableRandomlyColoredTags":true,"enableTaskNotes":true,"enablePrettyLinks":true,"searchMode":"search","siteUrl":"https://vishnushiri02.github.io","assetsPrefix":"/SARS-Cov2","siteFaviconPath":"favicon.ico","siteIndex":"root"}}},"__N_SSG":true}
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If there are no lineages having the same first \n character but there are entries in the input df, the lineage is \n mapped to it's own and is stored in alias_df.\n\n 3. else If the there are no more entries in the \n lineage_cmut then the lineage is mapped to itself and return is called.\n\n 4. else the chunk stored in temp_df is passed to\n long_sublineage\n\n End\n\n Function long_sublineage(temp_df,lineage_cmut,\n alias_df,alias_df_temp)\n\n 1. Finds the sublineage with longest character string\n and stores it in longlineage_df\n\n 2. If multiple lineages have long character string\n both the lineages are stored in longlineage_df\n\n 3. If linegaes length is just one then it is the\n parental lineage and it is mapped to it's own and is\n removed from temp_df amd is stored in alias_df\n\n END\n\n Function match_merge(longlineage_df,temp_df\n lineage_cmut,alias_df,alias_df_temp)\n\n 1. Iterates through the longlineage_df, forms pattern\n from the first element taken and tries to find\n neighbours in longlineage_df based on jaccard value\n using function find_jaccard\n\n 1. If neighbours are found their mutations are\n combined (union).\n\n 2. Checks if these neighbors are paretnal lineage to\n some other lineage in the alias_temp_df\n\n 3. Checking if there is a parental lineage to the\n neighbours in the temp_df\n\n 4. If parental lineage is found and if the length of the parental \n lineage string is more than one, the mutations of the\n neighbours and the parernal lineages are again\n combined (union) and stored in the place of \n mutations of the parental lineage in temp_df. Neighbours are \n mapped to their found parent and are stored in alias_df_temp, \n since there is potential for surther mapping. This parental \n lineage also becomes the parental lineage for the \n sublineages that had these neighbours as parental \n lineage in alias_df_temp.These neighbors are removed from \n longlineage_df and the loop is iterated for the next round.\n \n 5. Else if the length of the parental lineage is \n equal to 1 then everything in the previous point \n that was written in the alias_df_temp is written to\n alias_df. Mutations are not meddled with, since it is \n the ultimate paretnal lineage and there is no go further.\n\n 6. If no parental lineage was found then the the\n Neighbours are mapped to the pattern which is the\n name of the neigbors without the last character. This\n pattern concatinated with x becomes the parental \n lineage of the neighbors. This also becomes the \n parental lineage for those sublineages for which the \n nighbors were parental lineage.\n\n 2. If there are no neighbors found\n\n 1. Code directly starts finding the parental \n lineage for the element being considered.\n\n 2. If paretnal lineage is found and the length of \n the lineage is more than 1, the element in hand \n is mapped to the found paretnal lineage and jaccard \n value is stored in the alias_df_temp. Mutations of \n the element and the found parental lineage is combined and \n stored in the place of the parental mutations in temp_df\n\n 3. Sublineages for which the lineage in hand is the parental\n lineage in alias_df_temp gets mapped to the newly found parental lineage.\n\n 4. If the length of the parental lineage being \n found is equal to one then point 2,3 is repeated \n but difference would be that instaed of \n alias_temp_df, alias_df is used and mutations are not meddeled with.\n\n 3. If no parerntal and neighbors were found\n\n 1. The lineage being considered is mapped to \n itself.\n\n 2. For sublineages in the alias_df_temp that has \n the lineage being considered as parental lineage is \n remains the same. It is just transfered to \n alias_df with no changes.\n\n \n Once the longlineage_df has been fully processed if \n there are entries in temp_df long_sublineage is called else chunk lineage is called.\n \n\n\n\n END\n\nEnd\nFunction find_jaccard(pat,search_df,pat_mutations=0)\n\n search_lineage_loc<-grep(pat,search_df$lineage)\n\n 1. If pat_mutations==0 means the function is finding the\n neighbours. Else the function is overloaded\n to find the parental lineage.\n\n 2. If pat_mutations==0 the length(search_lineage_loc) should be\n more than 1 - neighbours other than the lineage in hand.\n\n 3. If no neighbours were found then function returns \n neighbours=\"0\", jaccard_value=-1,neighbour_loc=0\n\n 4. Other than point 2 the overloaded function does the\n same functionality for both the overloaded purposes\n and returns jaccard value, neighbours, neighbour_loc \nEnd\nFunction Find_parental(pat,parental_df)\n\n 1. Recursively searches with the pattern \n until it finds the parental lineage satisfying \n the conditions \n 2. The pattern is shortened every iteration.\nEnd\nquestion on the treshold
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Why/How was this particular time period (Jan22-Oct23) chosen, while pandemic has been prevelant 2020 onwards?","value":"1-whyhow-was-this-particular-time-period-jan22-oct23-chosen-while-pandemic-has-been-prevelant-2020-onwards","line":10,"column":0,"depth":3},"2-there-are-continuous-deposition-of-sequences-to-gisaid-even-for-the-earlier-dates-the-following-tables-shows-the-difference-in-numbers-will-calculating-ci-account-these-changes-":{"type":"header","text":"2. There are continuous deposition of sequences to GISAID even for the earlier dates, the following tables shows the difference in numbers. Will calculating CI account these changes ?","value":"2-there-are-continuous-deposition-of-sequences-to-gisaid-even-for-the-earlier-dates-the-following-tables-shows-the-difference-in-numbers-will-calculating-ci-account-these-changes-","line":14,"column":0,"depth":3},"3-the-jaccard-index-treshold-chosen-to-decide-a-parent-or-a-neighbour-is-50-is-this-okay":{"type":"header","text":"3. 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Country Number of downloaded(18-20 Nov 23) Number in GISAID at later date(8 Dec 2023) India 15783 15809 South_korea 13808 13826 Denmark 163900 163905 Germany 66236 66244 Norway 12264 12264 Spain 17921 18039 UK 30597 30606 Canada 20791 20802 USA 74971 75165 australia 13257 13261
\nANS: If there is major difference CI wouldn't account for these. In that case we have to either redownload new set of data or we have to jjust specify the date of download in the report \n \n3. The jaccard index treshold chosen to decide a parent or a neighbour is 50%. Is this okay? \n\nANS: Jaccard index is not the right way to go about, we have to get the distance, create a distance table based on the spike mutations. \n \n4. Why do we do linear interpolation, why not spline interpolation? \n-ANS: In a sparse data using a spline interpolation might give unexpected results which might not be right.
\n5. Aaccording to Uniprot the RBD region in spike is 319-541aa \nANS: There are no interesting epitopes before position 330 and after position 530 so it's fine.
\n6. If a RBD spike position in the wildtype is occupied by a hydrophobic residue and it is replaced by hydrophilic residue, the solvent accessibility might change probably due to the difference in the fold. In that case should we study these positions in each of the VOI? \nANS: The Mutation doesnt alter the fold to a great extent, if that happens it will affect the function of the protein, so using the wildtype to compute the solvent accessibility is not so bad idea.
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+ * @license
+ * Copyright 2020 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */ let tr=t=>void 0===t.strings,tn={ATTRIBUTE:1,CHILD:2,PROPERTY:3,BOOLEAN_ATTRIBUTE:4,EVENT:5,ELEMENT:6},to=t=>(...e)=>({_$litDirective$:t,values:e});class tl{constructor(t){}get _$AU(){return this._$AM._$AU}_$AT(t,e,i){this._$Ct=t,this._$AM=e,this._$Ci=i}_$AS(t,e){return this.update(t,e)}update(t,e){return this.render(...e)}}/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */ let th=(t,e)=>{var i,s;let r=t._$AN;if(void 0===r)return!1;for(let n of r)null===(s=(i=n)._$AO)||void 0===s||s.call(i,e,!1),th(n,e);return!0},ta=t=>{let e,i;do{if(void 0===(e=t._$AM))break;(i=e._$AN).delete(t),t=e}while((null==i?void 0:i.size)===0)},td=t=>{for(let e;e=t._$AM;t=e){let i=e._$AN;if(void 0===i)e._$AN=i=new Set;else if(i.has(t))break;i.add(t),tp(e)}};function tc(t){void 0!==this._$AN?(ta(this),this._$AM=t,td(this)):this._$AM=t}function tu(t,e=!1,i=0){let s=this._$AH,r=this._$AN;if(void 0!==r&&0!==r.size){if(e){if(Array.isArray(s))for(let n=i;n{var e,i,s,r;t.type==tn.CHILD&&(null!==(e=(s=t)._$AP)&&void 0!==e||(s._$AP=tu),null!==(i=(r=t)._$AQ)&&void 0!==i||(r._$AQ=tc))};class t_ extends tl{constructor(){super(...arguments),this._$AN=void 0}_$AT(t,e,i){super._$AT(t,e,i),td(this),this.isConnected=t._$AU}_$AO(t,e=!0){var i,s;t!==this.isConnected&&(this.isConnected=t,t?null===(i=this.reconnected)||void 0===i||i.call(this):null===(s=this.disconnected)||void 0===s||s.call(this)),e&&(th(this,t),ta(this))}setValue(t){if(tr(this._$Ct))this._$Ct._$AI(t,this);else{let e=[...this._$Ct._$AH];e[this._$Ci]=t,this._$Ct._$AI(e,this,0)}}disconnected(){}reconnected(){}}/**
+ * @license
+ * Copyright 2020 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */ let t$=()=>new tv;class tv{}let tg=new WeakMap,tf=to(class extends t_{render(t){return L}update(t,[e]){var i;let s=e!==this.Y;return s&&void 0!==this.Y&&this.rt(void 0),(s||this.lt!==this.ct)&&(this.Y=e,this.dt=null===(i=t.options)||void 0===i?void 0:i.host,this.rt(this.ct=t.element)),L}rt(t){var e;if("function"==typeof this.Y){let i=null!==(e=this.dt)&&void 0!==e?e:globalThis,s=tg.get(i);void 0===s&&(s=new WeakMap,tg.set(i,s)),void 0!==s.get(this.Y)&&this.Y.call(this.dt,void 0),s.set(this.Y,t),void 0!==t&&this.Y.call(this.dt,t)}else this.Y.value=t}get lt(){var t,e,i;return"function"==typeof this.Y?null===(e=tg.get(null!==(t=this.dt)&&void 0!==t?t:globalThis))||void 0===e?void 0:e.get(this.Y):null===(i=this.Y)||void 0===i?void 0:i.value}disconnected(){this.lt===this.ct&&this.rt(void 0)}reconnected(){this.rt(this.ct)}});var tm,tA,ty,tS,tE,tb,tw=Object.defineProperty,tC=Object.getOwnPropertyDescriptor,tU=(t,e,i,s)=>{for(var r,n=s>1?void 0:s?tC(e,i):e,o=t.length-1;o>=0;o--)(r=t[o])&&(n=(s?r(e,i,n):r(n))||n);return s&&n&&tw(e,i,n),n};let tP=class extends tt{constructor(){super(),this.GISCUS_SESSION_KEY="giscus-session",this.GISCUS_DEFAULT_HOST="https://giscus.app",this.ERROR_SUGGESTION="Please consider reporting this error at https://github.com/giscus/giscus/issues/new.",this.__session="",this._iframeRef=t$(),this.messageEventHandler=this.handleMessageEvent.bind(this),this.host=this.GISCUS_DEFAULT_HOST,this.strict="0",this.reactionsEnabled="1",this.emitMetadata="0",this.inputPosition="bottom",this.theme="light",this.lang="en",this.loading="eager",this.setupSession(),window.addEventListener("message",this.messageEventHandler)}get iframeRef(){return this._iframeRef.value}connectedCallback(){super.connectedCallback()}disconnectedCallback(){super.disconnectedCallback(),window.removeEventListener("message",this.messageEventHandler)}_formatError(t){return`[giscus] An error occurred. 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+
+ `}};tP.styles=h`
+ :host,
+ iframe {
+ width: 100%;
+ border: none;
+ color-scheme: normal;
+ min-height: 150px;
+ }
+ `,tU([ts({reflect:!0})],tP.prototype,"host",2),tU([ts({reflect:!0})],tP.prototype,"repo",2),tU([ts({reflect:!0})],tP.prototype,"repoId",2),tU([ts({reflect:!0})],tP.prototype,"category",2),tU([ts({reflect:!0})],tP.prototype,"categoryId",2),tU([ts({reflect:!0})],tP.prototype,"mapping",2),tU([ts({reflect:!0})],tP.prototype,"term",2),tU([ts({reflect:!0})],tP.prototype,"strict",2),tU([ts({reflect:!0})],tP.prototype,"reactionsEnabled",2),tU([ts({reflect:!0})],tP.prototype,"emitMetadata",2),tU([ts({reflect:!0})],tP.prototype,"inputPosition",2),tU([ts({reflect:!0})],tP.prototype,"theme",2),tU([ts({reflect:!0})],tP.prototype,"lang",2),tU([ts({reflect:!0})],tP.prototype,"loading",2),tP=tU([(tm="giscus-widget",t=>{var e,i;return"function"==typeof t?(window.customElements.define(tm,t),t):((t,e)=>{let{kind:i,elements:s}=e;return{kind:i,elements:s,finisher(e){window.customElements.define(t,e)}}})(tm,t)})],tP)}}]);
\ No newline at end of file
diff --git a/_next/static/chunks/935-4dee79e80b8641c6.js b/_next/static/chunks/935-4dee79e80b8641c6.js
new file mode 100644
index 0000000..37289a1
--- /dev/null
+++ b/_next/static/chunks/935-4dee79e80b8641c6.js
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c$,cG,cq,cK,cY,cX,cJ,cQ,c0,c1,c2,c4,c6,c3,c8,c5,c9,c7,le,lt,ln,lr,li,lo,la,ls,lu,lc,ll,lf,ld,lp,lh,lm,lv,lg,ly,lb,lE,lw,lx,l_,lS,lC,lA,lD,lO,lk,lT,lN,lP,lI,lM,lF,lR,lL,lj,lB=function(e){function t(t){var n=e.call(this)||this;return n.input=t,n}return u9(t,e),t.prototype.resolveMiddleware=function(e,t,n){this.middlewareStack.use(i_(t,this.serialize,this.deserialize));var r,i=e.concat(this.middlewareStack),o={logger:t.logger,clientName:"CognitoIdentityClient",commandName:"GetIdCommand",inputFilterSensitiveLog:th.filterSensitiveLog,outputFilterSensitiveLog:tm.filterSensitiveLog},a=t.requestHandler;return i.resolve(function(e){return a.handle(e.request,n||{})},o)},t.prototype.serialize=function(e,t){return ci(e,t)},t.prototype.deserialize=function(e,t){return cs(e,t)},t}(iO),lU="IdentityIds",lZ=function(){function e(e){void 0===e&&(e="aws:cognito-identity-ids"),this.dbName=e}return e.prototype.getItem=function(e){return this.withObjectStore("readonly",function(t){var n=t.get(e);return new 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u9(t,e),t.prototype.destroy=function(){e.prototype.destroy.call(this)},t}(iD),l8=function(){return(l8=Object.assign||function(e){for(var t,n=1,r=arguments.length;n0&&i[i.length-1])&&(6===o[0]||2===o[0])){a=0;continue}if(3===o[0]&&(!i||o[1]>i[0]&&o[1]new Date().getTime())?(l7.debug("token not expired"),this._setCredentialsFromFederation({provider:t,token:r,user:n,identity_id:i,expires_at:o})):a._refreshHandlers[t]&&"function"==typeof a._refreshHandlers[t]?(l7.debug("getting refreshed jwt token from federation provider"),this._providerRefreshWithRetry({refreshHandler:a._refreshHandlers[t],provider:t,user:n})):(l7.debug("no refresh handler for provider:",t),this.clear(),Promise.reject("no refresh handler for provider"))},e.prototype._providerRefreshWithRetry=function(e){var t=this,n=e.refreshHandler,r=e.provider,i=e.user;return uq(n,[],1e4).then(function(e){return l7.debug("refresh federated token sucessfully",e),t._setCredentialsFromFederation({provider:r,token:e.token,user:i,identity_id:e.identity_id,expires_at:e.expires_at})}).catch(function(e){return"string"==typeof e&&0===e.toLowerCase().lastIndexOf("network error",e.length)||t.clear(),l7.debug("refresh federated token failed",e),Promise.reject("refreshing federation token failed: "+e)})},e.prototype._isExpired=function(e){if(!e)return l7.debug("no credentials for expiration check"),!0;l7.debug("are these credentials expired?",e);var t=Date.now();return e.expiration.getTime()<=t},e.prototype._isPastTTL=function(){return this._nextCredentialsRefresh<=Date.now()},e.prototype._setCredentialsForGuest=function(){return l5(this,void 0,void 0,function(){var e,t,n,r,i,o,a,s,u=this;return l9(this,function(c){switch(c.label){case 0:if(l7.debug("setting credentials for guest"),t=(e=this._config).identityPoolId,n=e.region,r=e.mandatorySignIn)return[2,Promise.reject("cannot get guest credentials when mandatory signin enabled")];if(!t)return l7.debug("No Cognito Identity pool provided for unauthenticated access"),[2,Promise.reject("No Cognito Identity pool provided for unauthenticated access")];if(!n)return l7.debug("region is not configured for getting the credentials"),[2,Promise.reject("region is not configured for getting the credentials")];return o=this,[4,this._getGuestIdentityId()];case 1:return i=o._identityId=c.sent(),a=new l3({region:n,customUserAgent:nn()}),s=void 0,s=i?cV({identityId:i,client:a})():l5(u,void 0,void 0,function(){var e,n;return l9(this,function(r){switch(r.label){case 0:return[4,a.send(new lB({IdentityPoolId:t}))];case 1:return e=r.sent().IdentityId,this._identityId=e,[2,(n=cV({client:a,identityId:e}))()]}})}).catch(function(e){return l5(u,void 0,void 0,function(){return l9(this,function(t){throw e})})}),[2,this._loadCredentials(s,"guest",!1,null).then(function(e){return e}).catch(function(e){return l5(u,void 0,void 0,function(){var n=this;return l9(this,function(r){switch(r.label){case 0:if(!("ResourceNotFoundException"===e.name&&e.message==="Identity '"+i+"' not found."))return[3,2];return l7.debug("Failed to load guest credentials"),[4,this._removeGuestIdentityId()];case 1:return r.sent(),s=l5(n,void 0,void 0,function(){var e,n;return l9(this,function(r){switch(r.label){case 0:return[4,a.send(new lB({IdentityPoolId:t}))];case 1:return e=r.sent().IdentityId,this._identityId=e,[2,(n=cV({client:a,identityId:e}))()]}})}).catch(function(e){return l5(n,void 0,void 0,function(){return l9(this,function(t){throw e})})}),[2,this._loadCredentials(s,"guest",!1,null)];case 2:return[2,e]}})})})]}})})},e.prototype._setCredentialsFromFederation=function(e){var t=e.provider,n=e.token,r=e.identity_id,i={google:"accounts.google.com",facebook:"graph.facebook.com",amazon:"www.amazon.com",developer:"cognito-identity.amazonaws.com"}[t]||t;if(!i)return Promise.reject("You must specify a federated provider");var o={};o[i]=n;var a=this._config,s=a.identityPoolId,u=a.region;if(!s)return l7.debug("No Cognito Federated Identity pool provided"),Promise.reject("No Cognito Federated Identity pool provided");if(!u)return l7.debug("region is not configured for getting the credentials"),Promise.reject("region is not configured for getting the credentials");var c=new l3({region:u,customUserAgent:nn()}),l=void 0;if(r){var f={identityId:r,logins:o,client:c};l=cV(f)()}else{var f={logins:o,identityPoolId:s,client:c};l=lH(f)()}return this._loadCredentials(l,"federated",!0,e)},e.prototype._setCredentialsFromSession=function(e){var t=this;l7.debug("set credentials from session");var n=e.getIdToken().getJwtToken(),r=this._config,i=r.region,o=r.userPoolId,a=r.identityPoolId;if(!a)return l7.debug("No Cognito Federated Identity pool provided"),Promise.reject("No Cognito Federated Identity pool provided");if(!i)return l7.debug("region is not configured for getting the credentials"),Promise.reject("region is not configured for getting the credentials");var s={};s["cognito-idp."+i+".amazonaws.com/"+o]=n;var u=new l3({region:i,customUserAgent:nn()}),c=l5(t,void 0,void 0,function(){var e,t,n,r,i,o,c,l,f,d;return l9(this,function(p){switch(p.label){case 0:return[4,this._getGuestIdentityId()];case 1:if(e=p.sent())return[3,3];return[4,u.send(new lB({IdentityPoolId:a,Logins:s}))];case 2:t=n=p.sent().IdentityId,p.label=3;case 3:return[4,u.send(new cB({IdentityId:e||t,Logins:s}))];case 4:if(o=(i=(r=p.sent()).Credentials).AccessKeyId,c=i.Expiration,l=i.SecretKey,f=i.SessionToken,d=r.IdentityId,this._identityId=d,!e)return[3,6];return l7.debug("The guest identity "+e+" has been successfully linked to the logins"),e===d&&l7.debug("The guest identity "+e+" has become the primary identity"),[4,this._removeGuestIdentityId()];case 5:p.sent(),p.label=6;case 6:return[2,{accessKeyId:o,secretAccessKey:l,sessionToken:f,expiration:c,identityId:d}]}})}).catch(function(e){return l5(t,void 0,void 0,function(){return l9(this,function(t){throw e})})});return this._loadCredentials(c,"userPool",!0,null)},e.prototype._loadCredentials=function(e,t,n,r){var i=this,o=this;return new Promise(function(a,s){e.then(function(e){return l5(i,void 0,void 0,function(){var i,s,u,c,l;return l9(this,function(f){switch(f.label){case 0:if(l7.debug("Load credentials successfully",e),this._identityId&&!e.identityId&&(e.identityId=this._identityId),o._credentials=e,o._credentials.authenticated=n,o._credentials_source=t,o._nextCredentialsRefresh=new Date().getTime()+fe,"federated"===t){i=Object.assign({id:this._credentials.identityId},r.user),s=r.provider,u=r.token,c=r.expires_at,l=r.identity_id;try{this._storage.setItem("aws-amplify-federatedInfo",JSON.stringify({provider:s,token:u,user:i,expires_at:c,identity_id:l}))}catch(d){l7.debug("Failed to put federated info into auth storage",d)}}if("guest"!==t)return[3,2];return[4,this._setGuestIdentityId(e.identityId)];case 1:f.sent(),f.label=2;case 2:return a(o._credentials),[2]}})})}).catch(function(t){if(t){l7.debug("Failed to load credentials",e),l7.debug("Error loading credentials",t),s(t);return}})})},e.prototype.set=function(e,t){return"session"===t?this._setCredentialsFromSession(e):"federation"===t?this._setCredentialsFromFederation(e):"guest"===t?this._setCredentialsForGuest():(l7.debug("no source specified for setting credentials"),Promise.reject("invalid source"))},e.prototype.clear=function(){return l5(this,void 0,void 0,function(){return l9(this,function(e){return this._credentials=null,this._credentials_source=null,l7.debug("removing aws-amplify-federatedInfo from storage"),this._storage.removeItem("aws-amplify-federatedInfo"),[2]})})},e.prototype._getGuestIdentityId=function(){return l5(this,void 0,void 0,function(){var e,t;return l9(this,function(n){switch(n.label){case 0:e=this._config.identityPoolId,n.label=1;case 1:return n.trys.push([1,3,,4]),[4,this._storageSync];case 2:return n.sent(),[2,this._storage.getItem(this._getCognitoIdentityIdStorageKey(e))];case 3:return t=n.sent(),l7.debug("Failed to get the cached guest identityId",t),[3,4];case 4:return[2]}})})},e.prototype._setGuestIdentityId=function(e){return l5(this,void 0,void 0,function(){var t,n;return l9(this,function(r){switch(r.label){case 0:t=this._config.identityPoolId,r.label=1;case 1:return r.trys.push([1,3,,4]),[4,this._storageSync];case 2:return r.sent(),this._storage.setItem(this._getCognitoIdentityIdStorageKey(t),e),[3,4];case 3:return n=r.sent(),l7.debug("Failed to cache guest identityId",n),[3,4];case 4:return[2]}})})},e.prototype._removeGuestIdentityId=function(){return l5(this,void 0,void 0,function(){var e;return l9(this,function(t){return e=this._config.identityPoolId,l7.debug("removing "+this._getCognitoIdentityIdStorageKey(e)+" from 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commas, or single quotes"},5815:function(e,t,n){"use strict";var r,i,o,a,s,u;Object.defineProperty(t,"__esModule",{value:!0}),t.BacklinkPanelSortOrder=t.getWebEditorViewEntry=t.getWebTreeViewEntry=t.isWebViewEntry=t.TREE_VIEWS=t.EDITOR_VIEWS=t.DendronTreeViewKey=t.DendronEditorViewKey=void 0;let c=n(25903);(a=r=t.DendronEditorViewKey||(t.DendronEditorViewKey={})).CONFIGURE="dendron.configure",a.NOTE_GRAPH="dendron.graph-note",a.SCHEMA_GRAPH="dendron.graph-schema",a.NOTE_PREVIEW="dendron.note-preview",a.SEED_BROWSER="dendron.seed-browser",(s=i=t.DendronTreeViewKey||(t.DendronTreeViewKey={})).SAMPLE_VIEW="dendron.sample",s.TREE_VIEW="dendron.treeView",s.BACKLINKS="dendron.backlinks",s.CALENDAR_VIEW="dendron.calendar-view",s.LOOKUP_VIEW="dendron.lookup-view",s.TIP_OF_THE_DAY="dendron.tip-of-the-day",s.HELP_AND_FEEDBACK="dendron.help-and-feedback",s.GRAPH_PANEL="dendron.graph-panel",s.RECENT_WORKSPACES="dendron.recent-workspaces",t.EDITOR_VIEWS={[r.NOTE_PREVIEW]:{desc:"Note 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key`})};t.getWebEditorViewEntry=d,(u=o=t.BacklinkPanelSortOrder||(t.BacklinkPanelSortOrder={})).PathNames="PathNames",u.LastUpdated="LastUpdated"},13079:function(e,t,n){"use strict";var r=this&&this.__createBinding||(Object.create?function(e,t,n,r){void 0===r&&(r=n);var i=Object.getOwnPropertyDescriptor(t,n);(!i||("get"in i?!t.__esModule:i.writable||i.configurable))&&(i={enumerable:!0,get:function(){return t[n]}}),Object.defineProperty(e,r,i)}:function(e,t,n,r){void 0===r&&(r=n),e[r]=t[n]}),i=this&&this.__setModuleDefault||(Object.create?function(e,t){Object.defineProperty(e,"default",{enumerable:!0,value:t})}:function(e,t){e.default=t}),o=this&&this.__importStar||function(e){if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)"default"!==n&&Object.prototype.hasOwnProperty.call(e,n)&&r(t,e,n);return i(t,e),t},a=this&&this.__importDefault||function(e){return e&&e.__esModule?e:{default:e}};Object.defineProperty(t,"__esModule",{value:!0}),t.SchemaUtils=t.NoteUtils=t.DNodeUtils=void 0;let s=a(n(9675)),u=a(n(96486)),c=a(n(61254)),l=a(n(1864)),f=a(n(2891)),d=n(17373),p=n(67949),h=n(8115),m=n(95120),v=n(52973),g=n(60494),y=n(85390),b=n(83694),E=n(49500),w=o(n(69606));class x{static addChild(e,t){e.children=Array.from(new Set(e.children).add(t.id)),t.parent=e.id}static removeChild(e,t){e.children=u.default.reject(e.children,e=>e===t.id)}static create(e){let t=u.default.defaults(e,{updated:v.Time.now().toMillis(),created:v.Time.now().toMillis(),id:(0,b.genUUID)(),desc:"",links:[],anchors:{},children:[],parent:null,body:"",data:{},title:e.title||_.genTitle(e.fname)}),n=["stub","schema","schemaStub","custom","color","tags","image",];return u.default.forEach(n,n=>{e[n]&&(t[n]=e[n])}),t}static basename(e,t){t&&e.endsWith(".md")&&(e=e.slice(void 0,-3));let[n,...r]=e.split(".");return u.default.isEmpty(r)?n:r.slice(-1)[0]}static dirName(e){return e.split(".").slice(0,-1).join(".")}static domainName(e){return e.split(".")[0]}static fname(e){return l.default.basename(e,".md")}static enhancePropForQuickInput({props:e,schema:t,vaults:n,wsRoot:r}){let i=E.VaultUtils.matchVault({vaults:n,wsRoot:r,vault:e.vault});if(!i)throw Error("enhancePropForQuickInput, no vault found");let o=E.VaultUtils.getName(i),a=` (${o})`;if("note"===e.type){let s=x.isRoot(e),u=s?"root":e.fname,c=e.desc,l=_.genSchemaDesc(e,t)+a,f={...e,label:u,detail:c,description:l};return f}{let d=x.isRoot(e)?"root":e.id,p=e.desc,h={...e,label:d,detail:p,description:a};return h}}static enhancePropForQuickInputV3(e){let{alwaysShow:t}=u.default.defaults(e,{alwaysShow:!1});return{...this.enhancePropForQuickInput(e),alwaysShow:t}}static enhancePropForQuickInputV4(e){let{props:t,schema:n}=e,r=E.VaultUtils.getName(e.props.vault),i=`(${r})`;if("note"===e.props.type){let o=x.isRoot(t),a=o?"root":t.fname,s=t.desc,u=_.genSchemaDesc(t,n)+i,c={...t,label:a,detail:s,description:u};return c}{let l=x.isRoot(t)?"root":t.id,f=t.desc,d={...t,label:l,detail:f,description:i};return d}}static findClosestParent(e,t,n){let{vault:r}=n,i=x.dirName(e);if(""===i){let o=m.NoteDictsUtils.findByFname({fname:"root",noteDicts:t,vault:r})[0];if(u.default.isUndefined(o))throw new h.DendronError({message:`no root found for ${e}`});return o}let a=m.NoteDictsUtils.findByFname({fname:i,noteDicts:t,vault:r})[0];return a&&!(null==n?void 0:n.noStubs)||a&&(null==n?void 0:n.noStubs)&&!a.stub&&!a.schemaStub?a:x.findClosestParent(i,t,n)}static async findClosestParentWithEngine(e,t,n){let{vault:r,excludeStub:i}=n,o=x.dirName(e);if(""===o){let a=await t.findNotesMeta({fname:"root",vault:r});if(0===a.length)throw new h.DendronError({message:`no root found for ${e}`});return a[0]}let s=await t.findNotesMeta({fname:o,vault:r,excludeStub:i});return s.length>0?s[0]:x.findClosestParentWithEngine(o,t,n)}static getCustomProps(e){let t=[...Object.values(g.DNodeExplicitPropsEnum),...Object.values(g.DNodeImplicitPropsEnum),];return u.default.omit(e,t)}static getDepth(e){return this.getFNameDepth(e.fname)}static getFNameDepth(e){return"root"===e?0:e.split(".").length}static getFullPath(e){let t=l.default.isAbsolute(e.vault.fsPath)?E.VaultUtils.getRelPath(e.vault):l.default.join(e.wsRoot,E.VaultUtils.getRelPath(e.vault));return l.default.join(t,e.basename)}static isRoot(e){return"root"===e.fname}static getLeafName(e){return u.default.split(e.fname,".").pop()}}t.DNodeUtils=x;class _{static getNoteTraits(e){return u.default.get(e,"traitIds",[])}static addOrUpdateParents(e){let{note:t,noteDicts:n,createStubs:r}=e,i=[];if(x.isRoot(t))return i;let o=x.dirName(t.fname).toLowerCase(),a=m.NoteDictsUtils.findByFname({fname:o,noteDicts:n,vault:t.vault})[0];if(a){let s={...a};x.addChild(a,t),i.push({status:"update",prevNote:s,note:a})}if(!a&&!r){let u={status:p.ERROR_STATUS.NO_PARENT_FOR_NOTE,msg:JSON.stringify({fname:t.fname})};throw h.DendronError.createFromStatus(u)}if(!a){let c=x.findClosestParent(t.fname,n,{vault:t.vault}),l={...c},f=_.createStubs(c,t);f.forEach(e=>{i.push({status:"create",note:e})}),i.push({status:"update",prevNote:l,note:c})}return i}static addSchema(e){let{note:t,schema:n,schemaModule:r}=e;t.schema={schemaId:n.id,moduleId:r.root.id}}static create(e){let t=u.default.defaults(e,{schemaStub:!1});return x.create({...t,type:"note"})}static async createWithSchema({noteOpts:e,engine:t}){let n=_.create(e),r=await S.matchPath({notePath:e.fname,engine:t});if(r){let{schema:i,schemaModule:o}=r;_.addSchema({note:n,schemaModule:o,schema:i})}return n}static async updateStubWithSchema(e){let{stubNote:t,engine:n}=e,r=u.default.omit(t,"stub"),i=await S.matchPath({notePath:r.fname,engine:n});if(i){let{schema:o,schemaModule:a}=i;_.addSchema({note:r,schemaModule:a,schema:o})}return r}static createRoot(e){return x.create({...e,type:"note",fname:"root",id:(0,b.genUUID)()})}static createStubs(e,t){let n=[],r=e.fname;x.isRoot(e)&&(r="");let i=t.fname,o=i.indexOf(r)+r.length,a=u.default.trimStart(i.slice(o),".").split("."),s=r,c=e;return a.slice(0,-1).forEach(e=>{u.default.isEmpty(s)?s=e:s+=`.${e}`;let r=_.create({fname:s,stub:!0,vault:t.vault});n.push(r),x.addChild(c,r),c=r}),x.addChild(c,t),n}static createWikiLink(e){let{note:t,anchor:n,useVaultPrefix:r,alias:i}=e,o=null==i?void 0:i.mode,a=null==i?void 0:i.value,s=null==i?void 0:i.tabStopIndex,{fname:u,vault:c}=t,l=t.title;if(t.fname.startsWith(p.TAGS_HIERARCHY)){let f=t.fname.split(p.TAGS_HIERARCHY)[1];return`#${f}`}let d="";if(n){let{value:h,type:m}=n,v;"header"===m?(l=h,v=(0,y.getSlugger)().slug(h)):v=h,d=`#${v}`}let g=r?`${p.CONSTANTS.DENDRON_DELIMETER}${E.VaultUtils.getName(c)}/`:"",b="";switch(o){case"snippet":b=`\${${s}:alias}|`;break;case"title":b=`${l}|`;break;case"value":b=""!==a?`${a}|`:""}let w=`[[${b}${g}${u}${d}]]`;return w}static fromSchema({fname:e,schemaModule:t,schemaId:n,vault:r}){let i=t.schemas[n];return _.create({fname:e,schemaStub:!0,desc:i.desc,schema:{moduleId:t.root.id,schemaId:n},vault:r})}static genSchemaDesc(e,t){let n=[];if(e.title!==e.fname&&n.push(e.title),(e.stub||e.schemaStub)&&n.push("$(gist-new)"),e.schema){if(!t)throw Error("schema mod required");let r=t.root,i=t.schemas[e.schema.schemaId];n.push(`$(repo) ${r.title||r.id}`),r.id!==e.schema.schemaId&&(n.push("$(breadcrumb-separator)"),i.data.isIdAutoGenerated?i.title!==i.id?n.push(i.title):n.push(i.data.pattern):n.push(i.title||i.id))}return n.join(" ")}static genJournalNoteTitle(e){let{fname:t,journalName:n}=e,r=t.indexOf(n),i=_.genTitle(t);if(r<0)return i;let o=t.slice(r+n.length+1);return o.match(/\d\d\d\d\.\d\d\.\d\d$/)?o.replace(/\./g,"-"):i}static updateNoteLocalConfig(e,t,n){return e.config||(e.config={}),e.config[t]?u.default.merge(e.config[t],n):e.config[t]=n,e}static genTitle(e){let t=x.basename(e,!0);return u.default.toLower(e)===e?(e=t.replace(/-/g," "),(0,f.default)(e)):t}static genTitleFromFullFname(e){let t=e.replace(/\./g," ");return(0,f.default)(t)}static getNotesWithLinkTo({note:e,notes:t}){let n=t.map(t=>u.default.find(t.links,t=>{var n,r;return(null===(r=null===(n=t.to)||void 0===n?void 0:n.fname)||void 0===r?void 0:r.toLowerCase())===e.fname.toLowerCase()})?t:void 0);return u.default.reject(n,u.default.isUndefined)}static getFullPath({note:e,wsRoot:t}){try{let n=x.getFullPath({wsRoot:t,vault:e.vault,basename:e.fname+".md"});return n}catch(r){throw new h.DendronError({message:"bad path",payload:{note:e,wsRoot:t}})}}static getURI({note:e,wsRoot:t}){return d.URI.file(this.getFullPath({note:e,wsRoot:t}))}static getNoteWithParents({note:e,notes:t,sortDesc:n=!0}){let r=[];if(!e||u.default.isUndefined(e))return[];for(;null!==e.parent;){r.push(e);try{let i=t[e.parent];if(u.default.isUndefined(i))throw"note is undefined";e=i}catch(o){throw Error(`no parent found for note ${e.id}`)}}return r.push(e),n&&u.default.reverse(r),r}static getPathUpTo(e,t){return e.split(".").slice(0,t).join(".")}static getRoots(e){return u.default.filter(u.default.values(e),x.isRoot)}static hydrate({noteRaw:e,noteHydrated:t,opts:n}){let r=u.default.pick(t,["parent","children"]);return(null==n?void 0:n.keepBackLinks)&&(e.links=t.links.filter(e=>"backlink"===e.type)),{...e,...r}}static match({notePath:e,pattern:t}){return(0,c.default)(e,t)}static isDefaultTitle(e){return e.title===_.genTitle(e.fname)}static normalizeFname(e){return(e=u.default.trim(e)).endsWith(".md")&&(e=e.slice(void 0,-3)),e}static isNoteProps(e){let t=["id","title","desc","links","anchors","fname","type","updated","created","parent","children","data","body","vault",];return u.default.isObject(e)&&t.every(t=>t in e&&(0,y.isNotUndefined)(e[t]))}static serializeExplicitProps(e){let t=Object.fromEntries(Object.entries(e).filter(([e,t])=>(0,y.isNotUndefined)(t)));if(!this.isNoteProps(t))throw new h.DendronError({message:`Note is missing some properties that are required. Found properties: ${JSON.stringify(e)}`});let n={...t};t.traits&&(n={...t,traitIds:t.traits.map(e=>e)});let r=u.default.pick(n,[...Object.values(g.DNodeExplicitPropsEnum),"stub",]),{custom:i}=t,o={...r,...i};return o}static serialize(e,t){let n=e.body,r=["parent","children"];(null==t?void 0:t.excludeStub)&&r.push("stub");let i=u.default.omit(_.serializeExplicitProps(e),r);i.title=u.default.toString(i.title),i.id=u.default.toString(i.id);let o=s.default.stringify(n||"",i);return"\n"!==n.slice(-1)?o.slice(0,-1):o}static toLogObj(e){let{fname:t,id:n,children:r,vault:i,parent:o}=e;return{fname:t,id:n,children:r,vault:i,parent:o}}static toNoteLoc(e){let{fname:t,id:n,vault:r}=e;return{fname:t,id:n,vaultName:E.VaultUtils.getName(r)}}static toNoteLocString(e){let t=this.toNoteLoc(e),n=[];return t.vaultName&&n.push(`dendron://${t.vaultName}/`),n.push(t.fname),t.id&&n.push(` (${t.id})`),n.join("")}static uri2Fname(e){return l.default.basename(e.fsPath,".md")}static validate(e){return u.default.isUndefined(e)?{error:h.DendronError.createFromStatus({status:p.ERROR_STATUS.BAD_PARSE_FOR_NOTE,message:"NoteProps is undefined"})}:u.default.isUndefined(e.vault)?{error:h.DendronError.createFromStatus({status:p.ERROR_STATUS.BAD_PARSE_FOR_NOTE,message:"note vault is undefined"})}:u.default.isString(e.title)?{data:!0}:{error:h.DendronError.createFromStatus({status:p.ERROR_STATUS.BAD_PARSE_FOR_NOTE,message:"note title not set as string"})}}static validateFname(e){for(let t of e.split(".")){let n=""===t;if(n)return{isValid:!1,reason:p.InvalidFilenameReason.EMPTY_HIERARCHY};let r=t.startsWith(" ")||t.endsWith(" ");if(r)return{isValid:!1,reason:p.InvalidFilenameReason.LEADING_OR_TRAILING_WHITESPACE}}let i=/[(),']/,o=u.default.isNull(e.match(i));return o?{isValid:!0}:{isValid:!1,reason:p.InvalidFilenameReason.ILLEGAL_CHARACTER}}static cleanFname(e){let{fname:t,replaceWith:n}=e,r=t.replace(/[(),'']/g,void 0===n?" ":n),i=r.split("."),o=i.map(e=>u.default.trim(e," ")).filter(e=>""!==e).join(".");return o}static color(e){let{fname:t,note:n}=e;return n&&n.color?{color:n.color,type:"configured"}:{color:(0,y.randomColor)(t),type:"generated"}}static async ancestors(e){let{fname:t,engine:n,includeSelf:r,nonStubOnly:i}=e,{vault:o}=e,a=t.split("."),s=(await n.findNotesMeta({fname:t,vault:o}))[0];if(s&&r&&!(i&&s.stub))return s;if("root"!==t){for(;a.length>1;)if((s=(await n.findNotesMeta({fname:(a=a.slice(void 0,a.length-1)).join("."),vault:o}))[0])&&!(i&&s.stub))return s;if(s)return o||(o=s.vault),s=(await n.findNotesMeta({fname:"root",vault:o}))[0]}}static isNote(e){return e.fsPath.endsWith(".md")}static genIdForFile({filePath:e,wsRoot:t}){let n=(0,y.normalizeUnixPath)(l.default.relative(t,e));return`${this.FILE_ID_PREFIX}${n}`}static isFileId(e){return e.startsWith(this.FILE_ID_PREFIX)}static createForFile(e){let t=this.genIdForFile(e);return this.create({fname:l.default.basename(e.filePath),id:t,vault:E.VaultUtils.createForFile(e),body:e.contents})}static createForFake(e){return this.create({fname:e.fname,id:`${this.FAKE_ID_PREFIX}${e.id}`,vault:e.vault,body:e.contents})}}t.NoteUtils=_,_.RE_FM=/^---(.*)^---/ms,_.RE_FM_UPDATED=/^updated:\s+(\d+)$/m,_.RE_FM_CREATED=/^created:.*$/m,_.RE_FM_UPDATED_OR_CREATED=/^(?(updated|created): *)(?[0-9]+)$/,_.FILE_ID_PREFIX="file-",_.FAKE_ID_PREFIX="fake-";class S{static createFromSchemaRaw(e){let t=["namespace","pattern","template",],n=u.default.omit(e,t),r=u.default.pick(e,t);this.processUntypedTemplate(r);let i=x.create({...u.default.defaults(n,{title:n.id,data:r,fname:"__empty"}),type:"schema"});return e.isIdAutoGenerated&&(i.data.isIdAutoGenerated=!0),i}static createFromSchemaOpts(e){var t;let n=["namespace","pattern","template",],r=u.default.omit(e,n),i=u.default.pick(e,n);this.processUntypedTemplate(i);let o=e.vault,a=x.create({vault:o,...u.default.defaults(r,{title:r.id,data:i,fname:"__empty"}),type:"schema"});return(null===(t=e.data)||void 0===t?void 0:t.isIdAutoGenerated)&&(a.data.isIdAutoGenerated=!0),a}static processUntypedTemplate(e){if(u.default.isString(e.template)){let t={id:e.template,type:"note"};e.template=t}}static createModule(e){return e}static createModuleProps(e){let{fname:t,vault:n}=e,r=S.createFromSchemaOpts({id:`${t}`,fname:t,parent:"root",created:1,updated:1,children:[],vault:n});return{version:1,fname:t,root:r,schemas:{[r.id]:r},imports:[],vault:n}}static createRootModule(e){let t=S.createFromSchemaOpts({id:"root",title:"root",fname:"root.schema",parent:"root",children:[],...e});return{version:1,imports:[],schemas:[t]}}static createRootModuleProps(e,t,n){let r=S.createFromSchemaOpts({id:"root",title:"root",fname:"root",parent:"root",children:[],vault:t,...n});return{version:1,imports:[],schemas:{root:r},fname:e,root:r,vault:t}}static enhanceForQuickInput({props:e,vaults:t}){var n;let r=t.length>1?` (${l.default.basename(null===(n=e.vault)||void 0===n?void 0:n.fsPath)})`:"",i=x.isRoot(e.root)?"root":e.root.id,o=e.root.desc,a={...e.root,fname:e.fname,label:i,detail:o,description:r,vault:e.vault};return a}static getModuleRoot(e){let t=u.default.find(e.schemas,{parent:"root"});if(!t){let n=u.default.find(e.schemas,{parent:null,id:"root"});if(n)return n;throw h.DendronError.createFromStatus({status:p.ERROR_STATUS.NO_ROOT_SCHEMA_FOUND})}return t}static getPath({root:e,fname:t}){return l.default.join(e,t+".schema.yml")}static async doesSchemaExist({id:e,engine:t}){let n=await t.getSchema(e);return!u.default.isUndefined(n.data)}static async getSchemaFromNote({note:e,engine:t}){if(e.schema)return(await t.getSchema(e.schema.moduleId)).data}static matchDomain(e,t,n){let r=n[e.fname];if(r){let i=r.schemas[r.root.id];return S.matchDomainWithSchema({noteCandidates:[e],notes:t,schemaCandidates:[i],schemaModule:r})}}static matchDomainWithSchema(e){let{noteCandidates:t,schemaCandidates:n,notes:r,schemaModule:i,matchNamespace:o}=u.default.defaults(e,{matchNamespace:!0}),a=u.default.map(t,e=>S.matchNotePathWithSchemaAtLevel({notePath:e.fname,schemas:n,schemaModule:i,matchNamespace:o})).filter(e=>!u.default.isUndefined(e));a.map(e=>{let{schema:n,notePath:a}=e,s=u.default.find(t,{fname:a});_.addSchema({note:s,schema:n,schemaModule:i});let c=!(n.data.namespace&&o),l=c?n.children.map(e=>i.schemas[e]):[n],f=s.children.map(e=>r[e]);return S.matchDomainWithSchema({noteCandidates:f,schemaCandidates:l,notes:r,schemaModule:i,matchNamespace:c})})}static async matchPath(e){let{notePath:t}=e,n=x.domainName(t),r=await e.engine.getSchema(n);if(r.data){let i=r.data.schemas[r.data.root.id];return n.length===t.length?{schema:i,notePath:t,namespace:i.data.namespace||!1,schemaModule:r.data}:S.matchPathWithSchema({notePath:t,matched:"",schemaCandidates:[i],schemaModule:r.data})}}static findSchemaFromModule(e){let{notePath:t,schemaModule:n}=e,r=x.domainName(t),i=n.schemas[n.root.id];return r.length===t.length?{schema:i,notePath:t,namespace:i.data.namespace||!1,schemaModule:n}:S.matchPathWithSchema({notePath:t,matched:"",schemaCandidates:[i],schemaModule:n})}static matchPathWithSchema({notePath:e,matched:t,schemaCandidates:n,schemaModule:r,matchNamespace:i=!0}){let o=(e,t)=>{let n=u.default.indexOf(e,".",t.length+1);return n>0?e.slice(0,n):e},a=o(e,t),s=S.matchNotePathWithSchemaAtLevel({notePath:a,schemas:n,schemaModule:r,matchNamespace:i});if(s){let{schema:c,namespace:l}=s;if(e===a)return{schemaModule:r,schema:c,namespace:l,notePath:e};let f=!(c.data.namespace&&i),d=f?c.children.map(e=>r.schemas[e]):[c];return S.matchPathWithSchema({notePath:e,matched:a,schemaCandidates:d,schemaModule:r,matchNamespace:f})}}static matchNotePathWithSchemaAtLevel({notePath:e,schemas:t,schemaModule:n,matchNamespace:r=!0}){let i=e.replace(/\./g,"/"),o=!1,a=u.default.find(t,e=>{var t;let a=S.getPatternRecursive(e,n.schemas);return(null===(t=null==e?void 0:e.data)||void 0===t?void 0:t.namespace)&&r?(o=!0,(0,c.default)(i,a.slice(0,-2))):(0,c.default)(i,a)});if(a)return{schema:a,namespace:o,notePath:e,schemaModule:n}}static serializeSchemaProps(e){let t=u.default.pick(e,["id","children",]),n=["title","desc",];u.default.forEach(n,n=>{e[n]&&(t[n]=e[n])});let r=e.data;return(null==e?void 0:e.parent)==="root"&&(t.parent="root"),{...t,...r}}static isSchemaUri(e){return e.fsPath.endsWith(".schema.yml")}static serializeModuleProps(e){let{version:t,imports:n,schemas:r}=e,i={version:t,imports:[],schemas:u.default.values(r).map(e=>S.serializeSchemaProps(e))};return n&&(i.imports=n),w.default.dump(i,{schema:w.JSON_SCHEMA})}}t.SchemaUtils=S,S.getPattern=(e,t)=>{var n,r;let i=(null===(n=null==e?void 0:e.data)||void 0===n?void 0:n.pattern)||e.id,o=(null===(r=null==e?void 0:e.data)||void 0===r?void 0:r.namespace)&&!(null==t?void 0:t.isNotNamespace)?`${i}/*`:i;return 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m=s.NoteUtils.create({...i.default.omit(c,["id","links","body"]),stub:!0});s.DNodeUtils.addChild(d,m),(r=r.concat(await this.updateChildrenWithNewParent(c,m))).push({note:m,status:"create"})}let v={...d};this.logger.info({ctx:n,noteAsLog:l,msg:"delete from parent"}),s.DNodeUtils.removeChild(d,p),r.push({prevNote:v,note:d,status:"update"});let g=(null==t?void 0:t.metaOnly)?await this.noteStore.deleteMetadata(e):await this.noteStore.delete(e);if(g.error)return{error:new u.DendronError({message:`Unable to delete note ${e}`,severity:a.ERROR_SEVERITY.MINOR,payload:g.error})};let y=await Promise.all(c.links.map(e=>this.removeBacklink(e)));return(r=r.concat(y.flat())).push({note:c,status:"delete"}),await this.queryStore.updateNotesIndex(r),await this.updateNoteMetadataStore(r),this.logger.info({ctx:n,msg:"exit",changed:r.map(e=>s.NoteUtils.toLogObj(e.note))}),{data:r}}async queryNotes(e){let{qs:t,vault:n,onlyDirectChildren:r,originalQS:o}=e;((null==n?void 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t,n,r;if(!(null===(t=e.to)||void 0===t?void 0:t.fname))return[];let a=o.BacklinkUtils.createFromDLink(e);if(a){let s=(null===(n=e.to)||void 0===n?void 0:n.vaultName)?l.VaultUtils.getVaultByName({vname:null===(r=e.to)||void 0===r?void 0:r.vaultName,vaults:this.vaults}):void 0,u=await this.noteStore.find({fname:e.to.fname,vault:s});if(u.data)return Promise.all(u.data.map(async e=>{let t=i.default.cloneDeep(e);return o.BacklinkUtils.addBacklinkInPlace({note:e,backlink:a}),{prevNote:t,note:e,status:"update"}}))}return[]}async removeBacklink(e){var t,n,r;if(!(null===(t=e.to)||void 0===t?void 0:t.fname))return[];let a=o.BacklinkUtils.createFromDLink(e);if(a){let s=(null===(n=e.to)||void 0===n?void 0:n.vaultName)?l.VaultUtils.getVaultByName({vname:null===(r=e.to)||void 0===r?void 0:r.vaultName,vaults:this.vaults}):void 0,u=await this.noteStore.find({fname:e.to.fname,vault:s});if(u.data)return Promise.all(u.data.map(async e=>{let t=i.default.cloneDeep(e);return 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n=this.parseJSXElementName(),r=this.parseJSXAttributes(),i=this.matchJSX("/");return i&&this.expectJSX("/"),this.expectJSX(">"),this.finalize(e,new a.JSXOpeningElement(n,i,r))},t.prototype.parseJSXEmptyExpression=function(){var e=this.createJSXChildNode();return this.collectComments(),this.lastMarker.index=this.scanner.index,this.lastMarker.line=this.scanner.lineNumber,this.lastMarker.column=this.scanner.index-this.scanner.lineStart,this.finalize(e,new a.JSXEmptyExpression)},t.prototype.parseJSXExpressionContainer=function(){var e,t=this.createJSXNode();return this.expectJSX("{"),this.matchJSX("}")?(e=this.parseJSXEmptyExpression(),this.expectJSX("}")):(this.finishJSX(),e=this.parseAssignmentExpression(),this.reenterJSX()),this.finalize(t,new a.JSXExpressionContainer(e))},t.prototype.parseJSXChildren=function(){for(var e=[];!this.scanner.eof();){var t=this.createJSXChildNode(),n=this.nextJSXText();if(n.start0){var o=this.finalize(e.node,new 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a.ArrayExpression(t))},e.prototype.parsePropertyMethod=function(e){this.context.isAssignmentTarget=!1,this.context.isBindingElement=!1;var t=this.context.strict,n=this.context.allowStrictDirective;this.context.allowStrictDirective=e.simple;var r=this.isolateCoverGrammar(this.parseFunctionSourceElements);return this.context.strict&&e.firstRestricted&&this.tolerateUnexpectedToken(e.firstRestricted,e.message),this.context.strict&&e.stricted&&this.tolerateUnexpectedToken(e.stricted,e.message),this.context.strict=t,this.context.allowStrictDirective=n,r},e.prototype.parsePropertyMethodFunction=function(){var e=!1,t=this.createNode(),n=this.context.allowYield;this.context.allowYield=!0;var r=this.parseFormalParameters(),i=this.parsePropertyMethod(r);return this.context.allowYield=n,this.finalize(t,new a.FunctionExpression(null,r.params,i,e))},e.prototype.parsePropertyMethodAsyncFunction=function(){var e=this.createNode(),t=this.context.allowYield,n=this.context.await;this.context.allowYield=!1,this.context.await=!0;var r=this.parseFormalParameters(),i=this.parsePropertyMethod(r);return this.context.allowYield=t,this.context.await=n,this.finalize(e,new a.AsyncFunctionExpression(null,r.params,i))},e.prototype.parseObjectPropertyKey=function(){var e,t=this.createNode(),n=this.nextToken();switch(n.type){case 8:case 6:this.context.strict&&n.octal&&this.tolerateUnexpectedToken(n,o.Messages.StrictOctalLiteral);var r=this.getTokenRaw(n);e=this.finalize(t,new a.Literal(n.value,r));break;case 3:case 1:case 5:case 4:e=this.finalize(t,new a.Identifier(n.value));break;case 7:"["===n.value?(e=this.isolateCoverGrammar(this.parseAssignmentExpression),this.expect("]")):e=this.throwUnexpectedToken(n);break;default:e=this.throwUnexpectedToken(n)}return e},e.prototype.isPropertyKey=function(e,t){return e.type===u.Syntax.Identifier&&e.name===t||e.type===u.Syntax.Literal&&e.value===t},e.prototype.parseObjectProperty=function(e){var t,n=this.createNode(),r=this.lookahead,i=null,s=null,u=!1,c=!1,l=!1,f=!1;if(3===r.type){var d=r.value;this.nextToken(),u=this.match("["),i=(f=!this.hasLineTerminator&&"async"===d&&!this.match(":")&&!this.match("(")&&!this.match("*")&&!this.match(","))?this.parseObjectPropertyKey():this.finalize(n,new a.Identifier(d))}else this.match("*")?this.nextToken():(u=this.match("["),i=this.parseObjectPropertyKey());var p=this.qualifiedPropertyName(this.lookahead);if(3===r.type&&!f&&"get"===r.value&&p)t="get",u=this.match("["),i=this.parseObjectPropertyKey(),this.context.allowYield=!1,s=this.parseGetterMethod();else if(3===r.type&&!f&&"set"===r.value&&p)t="set",u=this.match("["),i=this.parseObjectPropertyKey(),s=this.parseSetterMethod();else if(7===r.type&&"*"===r.value&&p)t="init",u=this.match("["),i=this.parseObjectPropertyKey(),s=this.parseGeneratorMethod(),c=!0;else if(i||this.throwUnexpectedToken(this.lookahead),t="init",this.match(":")&&!f)!u&&this.isPropertyKey(i,"__proto__")&&(e.value&&this.tolerateError(o.Messages.DuplicateProtoProperty),e.value=!0),this.nextToken(),s=this.inheritCoverGrammar(this.parseAssignmentExpression);else if(this.match("("))s=f?this.parsePropertyMethodAsyncFunction():this.parsePropertyMethodFunction(),c=!0;else if(3===r.type){var d=this.finalize(n,new a.Identifier(r.value));if(this.match("=")){this.context.firstCoverInitializedNameError=this.lookahead,this.nextToken(),l=!0;var h=this.isolateCoverGrammar(this.parseAssignmentExpression);s=this.finalize(n,new a.AssignmentPattern(d,h))}else l=!0,s=d}else this.throwUnexpectedToken(this.nextToken());return this.finalize(n,new a.Property(t,i,u,s,c,l))},e.prototype.parseObjectInitializer=function(){var e=this.createNode();this.expect("{");for(var t=[],n={value:!1};!this.match("}");)t.push(this.parseObjectProperty(n)),this.match("}")||this.expectCommaSeparator();return this.expect("}"),this.finalize(e,new a.ObjectExpression(t))},e.prototype.parseTemplateHead=function(){r.assert(this.lookahead.head,"Template literal must start with a template head");var e=this.createNode(),t=this.nextToken(),n=t.value,i=t.cooked;return this.finalize(e,new a.TemplateElement({raw:n,cooked:i},t.tail))},e.prototype.parseTemplateElement=function(){10!==this.lookahead.type&&this.throwUnexpectedToken();var e=this.createNode(),t=this.nextToken(),n=t.value,r=t.cooked;return this.finalize(e,new a.TemplateElement({raw:n,cooked:r},t.tail))},e.prototype.parseTemplateLiteral=function(){var e=this.createNode(),t=[],n=[],r=this.parseTemplateHead();for(n.push(r);!r.tail;)t.push(this.parseExpression()),r=this.parseTemplateElement(),n.push(r);return this.finalize(e,new a.TemplateLiteral(n,t))},e.prototype.reinterpretExpressionAsPattern=function(e){switch(e.type){case u.Syntax.Identifier:case u.Syntax.MemberExpression:case u.Syntax.RestElement:case u.Syntax.AssignmentPattern:break;case u.Syntax.SpreadElement:e.type=u.Syntax.RestElement,this.reinterpretExpressionAsPattern(e.argument);break;case u.Syntax.ArrayExpression:e.type=u.Syntax.ArrayPattern;for(var t=0;t")||this.expect("=>"),e={type:l,params:[],async:!1};else{var t=this.lookahead,n=[];if(this.match("..."))e=this.parseRestElement(n),this.expect(")"),this.match("=>")||this.expect("=>"),e={type:l,params:[e],async:!1};else{var r=!1;if(this.context.isBindingElement=!0,e=this.inheritCoverGrammar(this.parseAssignmentExpression),this.match(",")){var i=[];for(this.context.isAssignmentTarget=!1,i.push(e);2!==this.lookahead.type&&this.match(",");){if(this.nextToken(),this.match(")")){this.nextToken();for(var o=0;o")||this.expect("=>"),this.context.isBindingElement=!1;for(var o=0;o")&&(e.type===u.Syntax.Identifier&&"yield"===e.name&&(r=!0,e={type:l,params:[e],async:!1}),!r)){if(this.context.isBindingElement||this.throwUnexpectedToken(this.lookahead),e.type===u.Syntax.SequenceExpression)for(var o=0;o")){for(var u=0;u0){this.nextToken(),this.context.isAssignmentTarget=!1,this.context.isBindingElement=!1;for(var i=[e,this.lookahead],o=t,s=this.isolateCoverGrammar(this.parseExponentiationExpression),u=[o,n.value,s],c=[r];!((r=this.binaryPrecedence(this.lookahead))<=0);){for(;u.length>2&&r<=c[c.length-1];){s=u.pop();var l=u.pop();c.pop(),o=u.pop(),i.pop();var f=this.startNode(i[i.length-1]);u.push(this.finalize(f,new a.BinaryExpression(l,o,s)))}u.push(this.nextToken().value),c.push(r),i.push(this.lookahead),u.push(this.isolateCoverGrammar(this.parseExponentiationExpression))}var d=u.length-1;t=u[d];for(var p=i.pop();d>1;){var h=i.pop(),m=p&&p.lineStart,f=this.startNode(h,m),l=u[d-1];t=this.finalize(f,new a.BinaryExpression(l,u[d-2],t)),d-=2,p=h}}return t},e.prototype.parseConditionalExpression=function(){var e=this.lookahead,t=this.inheritCoverGrammar(this.parseBinaryExpression);if(this.match("?")){this.nextToken();var n=this.context.allowIn;this.context.allowIn=!0;var r=this.isolateCoverGrammar(this.parseAssignmentExpression);this.context.allowIn=n,this.expect(":");var i=this.isolateCoverGrammar(this.parseAssignmentExpression);t=this.finalize(this.startNode(e),new a.ConditionalExpression(t,r,i)),this.context.isAssignmentTarget=!1,this.context.isBindingElement=!1}return t},e.prototype.checkPatternParam=function(e,t){switch(t.type){case u.Syntax.Identifier:this.validateParam(e,t,t.name);break;case u.Syntax.RestElement:this.checkPatternParam(e,t.argument);break;case u.Syntax.AssignmentPattern:this.checkPatternParam(e,t.left);break;case u.Syntax.ArrayPattern:for(var n=0;n")){this.context.isAssignmentTarget=!1,this.context.isBindingElement=!1;var i=e.async,s=this.reinterpretAsCoverFormalsList(e);if(s){this.hasLineTerminator&&this.tolerateUnexpectedToken(this.lookahead),this.context.firstCoverInitializedNameError=null;var c=this.context.strict,f=this.context.allowStrictDirective;this.context.allowStrictDirective=s.simple;var d=this.context.allowYield,p=this.context.await;this.context.allowYield=!0,this.context.await=i;var h=this.startNode(t);this.expect("=>");var m=void 0;if(this.match("{")){var v=this.context.allowIn;this.context.allowIn=!0,m=this.parseFunctionSourceElements(),this.context.allowIn=v}else m=this.isolateCoverGrammar(this.parseAssignmentExpression);var g=m.type!==u.Syntax.BlockStatement;this.context.strict&&s.firstRestricted&&this.throwUnexpectedToken(s.firstRestricted,s.message),this.context.strict&&s.stricted&&this.tolerateUnexpectedToken(s.stricted,s.message),e=i?this.finalize(h,new a.AsyncArrowFunctionExpression(s.params,m,g)):this.finalize(h,new a.ArrowFunctionExpression(s.params,m,g)),this.context.strict=c,this.context.allowStrictDirective=f,this.context.allowYield=d,this.context.await=p}}else if(this.matchAssign()){if(this.context.isAssignmentTarget||this.tolerateError(o.Messages.InvalidLHSInAssignment),this.context.strict&&e.type===u.Syntax.Identifier){var y=e;this.scanner.isRestrictedWord(y.name)&&this.tolerateUnexpectedToken(n,o.Messages.StrictLHSAssignment),this.scanner.isStrictModeReservedWord(y.name)&&this.tolerateUnexpectedToken(n,o.Messages.StrictReservedWord)}this.match("=")?this.reinterpretExpressionAsPattern(e):(this.context.isAssignmentTarget=!1,this.context.isBindingElement=!1);var b=(n=this.nextToken()).value,E=this.isolateCoverGrammar(this.parseAssignmentExpression);e=this.finalize(this.startNode(t),new a.AssignmentExpression(b,e,E)),this.context.firstCoverInitializedNameError=null}}return e},e.prototype.parseExpression=function(){var e=this.lookahead,t=this.isolateCoverGrammar(this.parseAssignmentExpression);if(this.match(",")){var n=[];for(n.push(t);2!==this.lookahead.type&&this.match(",");)this.nextToken(),n.push(this.isolateCoverGrammar(this.parseAssignmentExpression));t=this.finalize(this.startNode(e),new a.SequenceExpression(n))}return t},e.prototype.parseStatementListItem=function(){var e;if(this.context.isAssignmentTarget=!0,this.context.isBindingElement=!0,4===this.lookahead.type)switch(this.lookahead.value){case"export":this.context.isModule||this.tolerateUnexpectedToken(this.lookahead,o.Messages.IllegalExportDeclaration),e=this.parseExportDeclaration();break;case"import":this.context.isModule||this.tolerateUnexpectedToken(this.lookahead,o.Messages.IllegalImportDeclaration),e=this.parseImportDeclaration();break;case"const":e=this.parseLexicalDeclaration({inFor:!1});break;case"function":e=this.parseFunctionDeclaration();break;case"class":e=this.parseClassDeclaration();break;case"let":e=this.isLexicalDeclaration()?this.parseLexicalDeclaration({inFor:!1}):this.parseStatement();break;default:e=this.parseStatement()}else e=this.parseStatement();return e},e.prototype.parseBlock=function(){var e=this.createNode();this.expect("{");for(var t=[];!this.match("}");)t.push(this.parseStatementListItem());return this.expect("}"),this.finalize(e,new a.BlockStatement(t))},e.prototype.parseLexicalBinding=function(e,t){var n=this.createNode(),r=[],i=this.parsePattern(r,e);this.context.strict&&i.type===u.Syntax.Identifier&&this.scanner.isRestrictedWord(i.name)&&this.tolerateError(o.Messages.StrictVarName);var s=null;return"const"===e?this.matchKeyword("in")||this.matchContextualKeyword("of")||(this.match("=")?(this.nextToken(),s=this.isolateCoverGrammar(this.parseAssignmentExpression)):this.throwError(o.Messages.DeclarationMissingInitializer,"const")):(!t.inFor&&i.type!==u.Syntax.Identifier||this.match("="))&&(this.expect("="),s=this.isolateCoverGrammar(this.parseAssignmentExpression)),this.finalize(n,new a.VariableDeclarator(i,s))},e.prototype.parseBindingList=function(e,t){for(var n=[this.parseLexicalBinding(e,t)];this.match(",");)this.nextToken(),n.push(this.parseLexicalBinding(e,t));return n},e.prototype.isLexicalDeclaration=function(){var e=this.scanner.saveState();this.scanner.scanComments();var t=this.scanner.lex();return this.scanner.restoreState(e),3===t.type||7===t.type&&"["===t.value||7===t.type&&"{"===t.value||4===t.type&&"let"===t.value||4===t.type&&"yield"===t.value},e.prototype.parseLexicalDeclaration=function(e){var t=this.createNode(),n=this.nextToken().value;r.assert("let"===n||"const"===n,"Lexical declaration must be either let or const");var i=this.parseBindingList(n,e);return this.consumeSemicolon(),this.finalize(t,new a.VariableDeclaration(i,n))},e.prototype.parseBindingRestElement=function(e,t){var n=this.createNode();this.expect("...");var r=this.parsePattern(e,t);return this.finalize(n,new a.RestElement(r))},e.prototype.parseArrayPattern=function(e,t){var n=this.createNode();this.expect("[");for(var r=[];!this.match("]");)if(this.match(","))this.nextToken(),r.push(null);else{if(this.match("...")){r.push(this.parseBindingRestElement(e,t));break}r.push(this.parsePatternWithDefault(e,t)),this.match("]")||this.expect(",")}return this.expect("]"),this.finalize(n,new a.ArrayPattern(r))},e.prototype.parsePropertyPattern=function(e,t){var n,r,i=this.createNode(),o=!1,s=!1,u=!1;if(3===this.lookahead.type){var c=this.lookahead;n=this.parseVariableIdentifier();var l=this.finalize(i,new a.Identifier(c.value));if(this.match("=")){e.push(c),s=!0,this.nextToken();var f=this.parseAssignmentExpression();r=this.finalize(this.startNode(c),new a.AssignmentPattern(l,f))}else this.match(":")?(this.expect(":"),r=this.parsePatternWithDefault(e,t)):(e.push(c),s=!0,r=l)}else o=this.match("["),n=this.parseObjectPropertyKey(),this.expect(":"),r=this.parsePatternWithDefault(e,t);return this.finalize(i,new a.Property("init",n,o,r,u,s))},e.prototype.parseObjectPattern=function(e,t){var n=this.createNode(),r=[];for(this.expect("{");!this.match("}");)r.push(this.parsePropertyPattern(e,t)),this.match("}")||this.expect(",");return this.expect("}"),this.finalize(n,new a.ObjectPattern(r))},e.prototype.parsePattern=function(e,t){var n;return this.match("[")?n=this.parseArrayPattern(e,t):this.match("{")?n=this.parseObjectPattern(e,t):(this.matchKeyword("let")&&("const"===t||"let"===t)&&this.tolerateUnexpectedToken(this.lookahead,o.Messages.LetInLexicalBinding),e.push(this.lookahead),n=this.parseVariableIdentifier(t)),n},e.prototype.parsePatternWithDefault=function(e,t){var n=this.lookahead,r=this.parsePattern(e,t);if(this.match("=")){this.nextToken();var i=this.context.allowYield;this.context.allowYield=!0;var o=this.isolateCoverGrammar(this.parseAssignmentExpression);this.context.allowYield=i,r=this.finalize(this.startNode(n),new a.AssignmentPattern(r,o))}return r},e.prototype.parseVariableIdentifier=function(e){var t=this.createNode(),n=this.nextToken();return 4===n.type&&"yield"===n.value?this.context.strict?this.tolerateUnexpectedToken(n,o.Messages.StrictReservedWord):this.context.allowYield||this.throwUnexpectedToken(n):3!==n.type?this.context.strict&&4===n.type&&this.scanner.isStrictModeReservedWord(n.value)?this.tolerateUnexpectedToken(n,o.Messages.StrictReservedWord):(this.context.strict||"let"!==n.value||"var"!==e)&&this.throwUnexpectedToken(n):(this.context.isModule||this.context.await)&&3===n.type&&"await"===n.value&&this.tolerateUnexpectedToken(n),this.finalize(t,new a.Identifier(n.value))},e.prototype.parseVariableDeclaration=function(e){var t=this.createNode(),n=[],r=this.parsePattern(n,"var");this.context.strict&&r.type===u.Syntax.Identifier&&this.scanner.isRestrictedWord(r.name)&&this.tolerateError(o.Messages.StrictVarName);var i=null;return this.match("=")?(this.nextToken(),i=this.isolateCoverGrammar(this.parseAssignmentExpression)):r.type===u.Syntax.Identifier||e.inFor||this.expect("="),this.finalize(t,new a.VariableDeclarator(r,i))},e.prototype.parseVariableDeclarationList=function(e){var t={inFor:e.inFor},n=[];for(n.push(this.parseVariableDeclaration(t));this.match(",");)this.nextToken(),n.push(this.parseVariableDeclaration(t));return n},e.prototype.parseVariableStatement=function(){var e=this.createNode();this.expectKeyword("var");var t=this.parseVariableDeclarationList({inFor:!1});return this.consumeSemicolon(),this.finalize(e,new a.VariableDeclaration(t,"var"))},e.prototype.parseEmptyStatement=function(){var e=this.createNode();return this.expect(";"),this.finalize(e,new a.EmptyStatement)},e.prototype.parseExpressionStatement=function(){var e=this.createNode(),t=this.parseExpression();return this.consumeSemicolon(),this.finalize(e,new a.ExpressionStatement(t))},e.prototype.parseIfClause=function(){return this.context.strict&&this.matchKeyword("function")&&this.tolerateError(o.Messages.StrictFunction),this.parseStatement()},e.prototype.parseIfStatement=function(){var e,t=this.createNode(),n=null;this.expectKeyword("if"),this.expect("(");var r=this.parseExpression();return!this.match(")")&&this.config.tolerant?(this.tolerateUnexpectedToken(this.nextToken()),e=this.finalize(this.createNode(),new a.EmptyStatement)):(this.expect(")"),e=this.parseIfClause(),this.matchKeyword("else")&&(this.nextToken(),n=this.parseIfClause())),this.finalize(t,new a.IfStatement(r,e,n))},e.prototype.parseDoWhileStatement=function(){var e=this.createNode();this.expectKeyword("do");var t=this.context.inIteration;this.context.inIteration=!0;var n=this.parseStatement();this.context.inIteration=t,this.expectKeyword("while"),this.expect("(");var r=this.parseExpression();return!this.match(")")&&this.config.tolerant?this.tolerateUnexpectedToken(this.nextToken()):(this.expect(")"),this.match(";")&&this.nextToken()),this.finalize(e,new a.DoWhileStatement(n,r))},e.prototype.parseWhileStatement=function(){var e,t=this.createNode();this.expectKeyword("while"),this.expect("(");var n=this.parseExpression();if(!this.match(")")&&this.config.tolerant)this.tolerateUnexpectedToken(this.nextToken()),e=this.finalize(this.createNode(),new a.EmptyStatement);else{this.expect(")");var r=this.context.inIteration;this.context.inIteration=!0,e=this.parseStatement(),this.context.inIteration=r}return this.finalize(t,new a.WhileStatement(n,e))},e.prototype.parseForStatement=function(){var e,t,n,r=null,i=null,s=null,c=!0,l=this.createNode();if(this.expectKeyword("for"),this.expect("("),this.match(";"))this.nextToken();else if(this.matchKeyword("var")){r=this.createNode(),this.nextToken();var f=this.context.allowIn;this.context.allowIn=!1;var d=this.parseVariableDeclarationList({inFor:!0});if(this.context.allowIn=f,1===d.length&&this.matchKeyword("in")){var p=d[0];p.init&&(p.id.type===u.Syntax.ArrayPattern||p.id.type===u.Syntax.ObjectPattern||this.context.strict)&&this.tolerateError(o.Messages.ForInOfLoopInitializer,"for-in"),r=this.finalize(r,new a.VariableDeclaration(d,"var")),this.nextToken(),e=r,t=this.parseExpression(),r=null}else 1===d.length&&null===d[0].init&&this.matchContextualKeyword("of")?(r=this.finalize(r,new a.VariableDeclaration(d,"var")),this.nextToken(),e=r,t=this.parseAssignmentExpression(),r=null,c=!1):(r=this.finalize(r,new a.VariableDeclaration(d,"var")),this.expect(";"))}else if(this.matchKeyword("const")||this.matchKeyword("let")){r=this.createNode();var h=this.nextToken().value;if(this.context.strict||"in"!==this.lookahead.value){var f=this.context.allowIn;this.context.allowIn=!1;var d=this.parseBindingList(h,{inFor:!0});this.context.allowIn=f,1===d.length&&null===d[0].init&&this.matchKeyword("in")?(r=this.finalize(r,new a.VariableDeclaration(d,h)),this.nextToken(),e=r,t=this.parseExpression(),r=null):1===d.length&&null===d[0].init&&this.matchContextualKeyword("of")?(r=this.finalize(r,new a.VariableDeclaration(d,h)),this.nextToken(),e=r,t=this.parseAssignmentExpression(),r=null,c=!1):(this.consumeSemicolon(),r=this.finalize(r,new a.VariableDeclaration(d,h)))}else r=this.finalize(r,new a.Identifier(h)),this.nextToken(),e=r,t=this.parseExpression(),r=null}else{var m=this.lookahead,f=this.context.allowIn;if(this.context.allowIn=!1,r=this.inheritCoverGrammar(this.parseAssignmentExpression),this.context.allowIn=f,this.matchKeyword("in"))this.context.isAssignmentTarget&&r.type!==u.Syntax.AssignmentExpression||this.tolerateError(o.Messages.InvalidLHSInForIn),this.nextToken(),this.reinterpretExpressionAsPattern(r),e=r,t=this.parseExpression(),r=null;else if(this.matchContextualKeyword("of"))this.context.isAssignmentTarget&&r.type!==u.Syntax.AssignmentExpression||this.tolerateError(o.Messages.InvalidLHSInForLoop),this.nextToken(),this.reinterpretExpressionAsPattern(r),e=r,t=this.parseAssignmentExpression(),r=null,c=!1;else{if(this.match(",")){for(var v=[r];this.match(",");)this.nextToken(),v.push(this.isolateCoverGrammar(this.parseAssignmentExpression));r=this.finalize(this.startNode(m),new a.SequenceExpression(v))}this.expect(";")}}if(void 0!==e||(this.match(";")||(i=this.parseExpression()),this.expect(";"),this.match(")")||(s=this.parseExpression())),!this.match(")")&&this.config.tolerant)this.tolerateUnexpectedToken(this.nextToken()),n=this.finalize(this.createNode(),new a.EmptyStatement);else{this.expect(")");var g=this.context.inIteration;this.context.inIteration=!0,n=this.isolateCoverGrammar(this.parseStatement),this.context.inIteration=g}return void 0===e?this.finalize(l,new a.ForStatement(r,i,s,n)):c?this.finalize(l,new a.ForInStatement(e,t,n)):this.finalize(l,new a.ForOfStatement(e,t,n))},e.prototype.parseContinueStatement=function(){var e=this.createNode();this.expectKeyword("continue");var t=null;if(3===this.lookahead.type&&!this.hasLineTerminator){var n=this.parseVariableIdentifier();t=n;var r="$"+n.name;Object.prototype.hasOwnProperty.call(this.context.labelSet,r)||this.throwError(o.Messages.UnknownLabel,n.name)}return this.consumeSemicolon(),null!==t||this.context.inIteration||this.throwError(o.Messages.IllegalContinue),this.finalize(e,new a.ContinueStatement(t))},e.prototype.parseBreakStatement=function(){var e=this.createNode();this.expectKeyword("break");var t=null;if(3===this.lookahead.type&&!this.hasLineTerminator){var n=this.parseVariableIdentifier(),r="$"+n.name;Object.prototype.hasOwnProperty.call(this.context.labelSet,r)||this.throwError(o.Messages.UnknownLabel,n.name),t=n}return this.consumeSemicolon(),null!==t||this.context.inIteration||this.context.inSwitch||this.throwError(o.Messages.IllegalBreak),this.finalize(e,new a.BreakStatement(t))},e.prototype.parseReturnStatement=function(){this.context.inFunctionBody||this.tolerateError(o.Messages.IllegalReturn);var e=this.createNode();this.expectKeyword("return");var t=(this.match(";")||this.match("}")||this.hasLineTerminator||2===this.lookahead.type)&&8!==this.lookahead.type&&10!==this.lookahead.type?null:this.parseExpression();return this.consumeSemicolon(),this.finalize(e,new a.ReturnStatement(t))},e.prototype.parseWithStatement=function(){this.context.strict&&this.tolerateError(o.Messages.StrictModeWith);var e,t=this.createNode();this.expectKeyword("with"),this.expect("(");var n=this.parseExpression();return!this.match(")")&&this.config.tolerant?(this.tolerateUnexpectedToken(this.nextToken()),e=this.finalize(this.createNode(),new a.EmptyStatement)):(this.expect(")"),e=this.parseStatement()),this.finalize(t,new a.WithStatement(n,e))},e.prototype.parseSwitchCase=function(){var e,t=this.createNode();this.matchKeyword("default")?(this.nextToken(),e=null):(this.expectKeyword("case"),e=this.parseExpression()),this.expect(":");for(var n=[];!(this.match("}")||this.matchKeyword("default")||this.matchKeyword("case"));)n.push(this.parseStatementListItem());return this.finalize(t,new a.SwitchCase(e,n))},e.prototype.parseSwitchStatement=function(){var e=this.createNode();this.expectKeyword("switch"),this.expect("(");var t=this.parseExpression();this.expect(")");var n=this.context.inSwitch;this.context.inSwitch=!0;var r=[],i=!1;for(this.expect("{");!this.match("}");){var s=this.parseSwitchCase();null===s.test&&(i&&this.throwError(o.Messages.MultipleDefaultsInSwitch),i=!0),r.push(s)}return this.expect("}"),this.context.inSwitch=n,this.finalize(e,new a.SwitchStatement(t,r))},e.prototype.parseLabelledStatement=function(){var e,t=this.createNode(),n=this.parseExpression();if(n.type===u.Syntax.Identifier&&this.match(":")){this.nextToken();var r=n,i="$"+r.name;Object.prototype.hasOwnProperty.call(this.context.labelSet,i)&&this.throwError(o.Messages.Redeclaration,"Label",r.name),this.context.labelSet[i]=!0;var s=void 0;if(this.matchKeyword("class"))this.tolerateUnexpectedToken(this.lookahead),s=this.parseClassDeclaration();else if(this.matchKeyword("function")){var c=this.lookahead,l=this.parseFunctionDeclaration();this.context.strict?this.tolerateUnexpectedToken(c,o.Messages.StrictFunction):l.generator&&this.tolerateUnexpectedToken(c,o.Messages.GeneratorInLegacyContext),s=l}else s=this.parseStatement();delete this.context.labelSet[i],e=new a.LabeledStatement(r,s)}else this.consumeSemicolon(),e=new a.ExpressionStatement(n);return this.finalize(t,e)},e.prototype.parseThrowStatement=function(){var e=this.createNode();this.expectKeyword("throw"),this.hasLineTerminator&&this.throwError(o.Messages.NewlineAfterThrow);var t=this.parseExpression();return this.consumeSemicolon(),this.finalize(e,new a.ThrowStatement(t))},e.prototype.parseCatchClause=function(){var e=this.createNode();this.expectKeyword("catch"),this.expect("("),this.match(")")&&this.throwUnexpectedToken(this.lookahead);for(var t=[],n=this.parsePattern(t),r={},i=0;i0&&this.tolerateError(o.Messages.BadGetterArity);var i=this.parsePropertyMethod(r);return this.context.allowYield=n,this.finalize(e,new a.FunctionExpression(null,r.params,i,t))},e.prototype.parseSetterMethod=function(){var e=this.createNode(),t=!1,n=this.context.allowYield;this.context.allowYield=!t;var r=this.parseFormalParameters();1!==r.params.length?this.tolerateError(o.Messages.BadSetterArity):r.params[0]instanceof a.RestElement&&this.tolerateError(o.Messages.BadSetterRestParameter);var i=this.parsePropertyMethod(r);return this.context.allowYield=n,this.finalize(e,new a.FunctionExpression(null,r.params,i,t))},e.prototype.parseGeneratorMethod=function(){var e=this.createNode(),t=!0,n=this.context.allowYield;this.context.allowYield=!0;var r=this.parseFormalParameters();this.context.allowYield=!1;var i=this.parsePropertyMethod(r);return this.context.allowYield=n,this.finalize(e,new 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![\"445](\"/SARS-Cov2/assets/plots/445_immunity.png\")
.![\"493_imm_freq\"](\"/SARS-Cov2/assets/plots/493_imm_freq.png\")
variant Spike_Q493R has high frequency and high immunity against too until Nov22. While calculating the expected immunnity against the variant, the case when x=y=Spike_Q493R will have the heighest value because p1 will be higher than p2, frequency of Spike_Q493R is higher anyways, so the expected immunity against Spike_Q493R is high![\"DSSP_output\"](\"/SARS-Cov2/assets/Pics/DSSP_output.png\")
![\"getarea_output\"](\"/SARS-Cov2/assets/Pics/getarea_output.png\")
![\"output\"](\"/SARS-Cov2/assets/Pics/NetsurfP3_0.png\")
![\"The](\"/SARS-Cov2/assets/Pics/clustal_w_alignment.png\")
The sequences align fairly well.![\"Trends](\"/SARS-Cov2/assets/plots/Country_trend_plots.png\")
![\"mapping](\"/SARS-Cov2/assets/Pics/Mapping_lineage_idea.png\")
![\"pangolin_assignment\"](\"/SARS-Cov2/assets/Pics/pangolin_assignment_analysis.png\")
![\"pangolin_assignemt_details\"](\"/SARS-Cov2/assets/Pics/pangolin_assignment.png\")
![\"frequency](\"/SARS-Cov2/assets/Pics/Frequency_interpolation.png\")
![\"Pressure](\"/SARS-Cov2/assets/Pics/Pressure_computed_india.png\")
![\"Methods](\"/SARS-Cov2/assets/Pics/methods_comparison.png\")
![\"plateau](\"/SARS-Cov2/assets/plots/Pressure_rep_curve_plateau.png\")
\n![\"first](\"/SARS-Cov2/assets/protein_locations/first_grp.png\")
![\"Sharp](\"/SARS-Cov2/assets/plots/Sharp_increase_decrease.png\")
\n![\"second](\"/SARS-Cov2/assets/protein_locations/second_grp.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_493.png\")
\n![\"493\"](\"/SARS-Cov2/assets/protein_locations/pos_493.png\")
![\"small](\"/SARS-Cov2/assets/plots/small_peak_big_curve.png\")
\n![\"446_346\"](\"/SARS-Cov2/assets/protein_locations/446_346.png\")
![\"small](\"/SARS-Cov2/assets/plots/small_peak_big_curve_decrease.png\")
\n![\"157_452\"](\"/SARS-Cov2/assets/protein_locations/452_157.png\")
![\"small](\"/SARS-Cov2/assets/plots/small_peak_instability_end.png\")
\n![\"156_156\"](\"/SARS-Cov2/assets/protein_locations/156_158.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_144.png\")
![\"sharp](\"/SARS-Cov2/assets/plots/fluctuating_peaks.png\")
\n![\"this](\"/SARS-Cov2/assets/protein_locations/245_153_450_18.png\")
![\"increase](\"/SARS-Cov2/assets/plots/increase_end.png\")
\n![\"end](\"/SARS-Cov2/assets/protein_locations/end_increase.png\")
![\"end](\"/SARS-Cov2/assets/plots/bump_end.png\")
\n![\"end](\"/SARS-Cov2/assets/protein_locations/end_bump.png\")
![\"end](\"/SARS-Cov2/assets/plots/end_small_rise.png\")
\n![\"253_521\"](\"/SARS-Cov2/assets/protein_locations/253_521.png\")
![\"sharp](\"/SARS-Cov2/assets/plots/end_sharp_increase.png\")
\n![\"455_456\"](\"/SARS-Cov2/assets/protein_locations/455_456.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_256.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_344.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_494.png\")
![\"pos](\"/SARS-Cov2/assets/plots/pp_248.png\")
![\"India](\"/SARS-Cov2/assets/plots/india_promising.png\")
This seems like at the time when the pressure trend of spike_G339O peaks - high vulnerability to be mutated the pressure trend of spike_G339H starts picking up, this corroborates the hypothesis that when the pressure increases there are higher chances for that position to be mutated.![\"Australia](\"/SARS-Cov2/assets/plots/australia_promising.png\")
In this case the mutant Spike_F157del has exsisted along side of Spike_F157L. But the pressure on the former increases only after the pressure on the later peaks. This can be interpretted as the former got more favorable conditions to spread more and hence the pressure is going higher.![\"plot1\"](\"/SARS-Cov2/assets/plots/plot_doubt1.png\")
![\"plot2\"](\"/SARS-Cov2/assets/plots/plot_doubt2.png\")
![\"plot3\"](\"/SARS-Cov2/assets/plots/plot_doubt3.png\")
![\"MAD\"](\"/SARS-Cov2/assets/Pics/MAD.png\")
![\"Flowchart](\"/SARS-Cov2/assets/Pics/mapping_lineages.png\")
\n![\"spike](\"/SARS-Cov2/assets/Pics/uniprot_spikeRBD.png\")