An Algorithm for Computing Nucleic Acid BasePairing Probabilities Including Pseudoknots


 Hillary Rose
 1 years ago
 Views:
Transcription
1 An Alorthm for Computn Nuclec Acd BaseParn Proaltes Includn Pseudoknots ROBERT M. DIRKS, 1 NILES A. PIERCE 2 1 Department of Chemstry, Calforna Insttute of Technoloy, Pasadena, Calforna Departments of Appled & Computatonal Mathematcs and Boenneern, Calforna Insttute of Technoloy, Mal Code , Pasadena, Calforna Receved 21 January 2004; Accepted 19 March 2004 DOI /jcc Pulshed onlne n Wley InterScence (www.nterscence.wley.com). Astract: Gven a nuclec acd sequence, a recent alorthm allows the calculaton of the partton functon over secondary structure space ncludn a class of physcally relevant pseudoknots. Here, we present a method for computn aseparn proaltes startn from the output of ths partton functon alorthm. The approach reles on the calculaton of recurson proaltes that are computed y acktrackn throuh the partton functon alorthm, applyn a partcular transformaton at each step. Ths transformaton s applcale to any partton functon alorthm that follows the same asc dynamc prorammn paradm. Baseparn proaltes are useful for analyzn the equlrum ensemle propertes of natural and enneered nuclec acds, as demonstrated for a human telomerase RNA and a synthetc DNA nanostructure Wley Perodcals, Inc. J Comput Chem 25: , 2004 Key words: DNA; RNA; aseparn proaltes; partton functon; pseudoknots Introducton Thermodynamc models ased on nuclec acd secondary structure and nearestnehor denttes 1 5 underly dynamc prorammn alorthms for predctn the mnmum enery secondary structure 6 10 and calculatn the partton functon over secondary structure space In ther ornal forms, these alorthms eclude the posslty of pseudoknots, a olocally relevant class of secondary structures 13 that also arses n DNA nanotechnoloy applcatons. 14,15 Pseudoknots result when two ase pars j and d e, wth d, fal to satsfy the nestn property d e j (see, e.., F. 1). Recent etensons of the structure predcton and partton functon 18 alorthms allow the ncluson of certan pseudoknots. For an ensemle of secondary structures s, the partton functon Q e Gs/RT s may e used to compute the proalty ps* 1 Q egs*/rt (1) that secondary structure s* s sampled at thermodynamc equlrum. The ensemle equlrum can also e characterzed y the matr of aseparn proaltes wth entres p, j correspondn to the proalty that ase s pared wth ase j n. McCaskll s ornal artcle 11 defnes eleant dynamc prorams to compute the partton functon and aseparn proaltes over the ensemle of unpseudoknotted secondary structures. The partton functon alorthm ulds up recursvely from short susequences to the full strand, and then the par proaltes are computed y workn ackwards to short susequences usn ntermedate results from the partton functon calculaton. In the asence of pseudoknots, the partton functon alorthm s suffcently succnct that McCaskll s ale to determne the form of the par proalty acktrack alorthm smply y consdern the few possle forms of enclosn secondary structure for any ven ase par. Althouh ths approach s smple and effcent, t s not easly Correspondence to: Nles A. Perce; emal: Contract/rant sponsor: NSF raduate research fellowshp (R.M.D.). Contract/rant sponsor: Defense Advanced Research Projects Aency (DARPA) and Ar Force Research Laoratory under F (N.A.P.). Contract/rant sponsor: Ralph M. Parsons Foundaton (N.A.P.). Contract/rant sponsor: Charles Lee Powell Foundaton (N.A.P.) Wley Perodcals, Inc.
2 1296 Drks and Perce Vol. 25, No. 10 Journal of Computatonal Chemstry descrpton n the same notaton). [The complety may e reduced to O(N 3 ) y eplotn the formulaton of the nearestnehor enery model for lon nteror loops. 18,21 ] Partton functon recursons are nonredundant n the sense that every secondary structure n the ensemle s vsted eactly once usn a unque sequence of recursons. The alorthm computes the partton functon Q, j for each susequence [, j] norn all ases eteror to [, j], startn from susequences of lenth l 1 and uldn up ncrementally to l N. The recursons that defne Q, j rely on addtonal restrcted partton functons Q, j and Q m, j. Q, j represents the partton functon for susequence [, j] ven that and j are ase pared and Q m, j s used to calculate multloop contrutons. At the end of the recursve process, the full partton functon Q s ven y Q 1,N and the values of Q, j, Q, j, Q m, j are stored n matrces for 1, j N. These ntermedate results wll play a crtcal role n the new alorthm descred elow. Recurson Proaltes Fure 1. Secondary structures of competn pseudoknot and harpn constructs n human telomerase RNA. The wldtype sequence s shown. For the twopont mutant mplcated n dyskeratoss conenta, GC s replaced y AG n the shaded oes, dsruptn two ase pars n the pseudoknot construct. For the epermental studes of the harpn structure, 20 the 18 nucleotdes at the 3 end are ecluded to prevent formaton of the pseudoknot. eneralzale to alorthmc etensons, such as the ncluson of pseudoknots. Here, we descre a eneral method for mechancally transformn the new pseudoknot partton functon alorthm 18 to compute recurson proaltes, whch can e used n turn to compute aseparn proaltes. The transformaton approach s eneralzale to any future partton functon etensons that follow the same dynamc prorammn paradm. Baseparn proaltes assst n the analyss of olocally relevant pseudoknots. Here, we eamne human telomerase RNA, whch ests at equlrum n oth harpn and pseudoknotted forms. 19 A twopont mutaton, mplcated n the dsease dyskeratoss conenta, alters the thermodynamc alance etween these competn structures. 20 Ths shft n equlrum s clearly dentfale when the aseparn proaltes for the two sequences are compared. Baseparn proaltes that permt pseudoknots are also useful n analyzn synthetc DNA nanostructures. 14,15 Follown the eecuton of the partton functon calculaton, a second alorthm can e mplemented to calculate proalty matrces, P, P, P m, correspondn to the Q, Q, Q m matrces. The values stored n these Ptype matrces wll e termed recurson proaltes. Recurson proaltes can e ntutvely descred as follows. Consder sampln the ensemle of secondary structures s where the proalty of selectn structure s* s ven y the Boltzmann proalty (1). For each secondary structure s*, the contruton to Q s computed y a unque recurson sequence nvolvn specfc Q, j, Q, j, and Q m, j ntermedates. Assocatn these ntermedates wth structure s*, the recurson proalty P, j, P, j or P m, j corresponds to the proalty that the sampled structure s* requres the use of the correspondn ntermedate Q, j, Q, j or Q m, j to calculate the partton functon contruton. Recent work y Dn and Lawrence 22 eplots quanttes related to recurson proaltes to statstcally sample the dstruton of unpseudoknotted secondary structures for a ven sequence. Here, we develop a eneral approach for computn Ptype ma Alorthm For clarty, we en y consdern the class of secondary structures ecludn pseudoknots and then address the addtonal complety that arses when pseudoknots are ntroduced. Partton Functon Recursons For a strand of lenth N, the partton functon may e computed over all unpseudoknotted secondary structures n O(N 4 ) usn the alorthm 10,11 summarzed n Fure 2 (see ref. 18 for a detaled Fure 2. O(N 4 ) partton functon alorthm that ecludes pseudoknots.
3 Alorthm for Computn Nuclec Acd BaseParn Proaltes 1297 Fure 3. Recurson daram correspondn to recursve update (2), depctn the addton to Q, j of partton functon contrutons for those structures wth rhtmost ase par d e. See ref. 18 for a thorouh descrpton of the partton functon alorthm (wth or wthout pseudoknots) n terms of recurson darams. trces ven a set of Qtype matrces and correspondn partton functon recursons. An alorthm for computn recurson proaltes can e formulated n a mechancal way startn from a set of partton functon recursons. The cru of ths formulaton s the repeated applcaton of a snle transformaton to the partton functon code. In partcular, updates of the form Q, j Q,d1 Q d,e (equvalent to the recurson daram of F. 3) are converted to the follown seres of statements (2) Startn from the partton functon alorthm of Fure 2, the recurson proalty alorthm s otaned y performn three modfcatons: (1) the two outermost loops are altered so that the alorthm starts wth the full strand of lenth l N and decrements down to susequences of lenth l 1; (2) all recursve updates are transformed as for (3) aove; (3) the order of the recurson locks (Q, [Q, Q m ]) s reversed ([P, P m ], P ). Ths last modfcaton s necessary ecause the recurson order n the partton functon alorthm ensures that f one quantty (e.., Q, j ) recurses to another quantty of the same lenth (e.., Q, j ) then the lower level quantty (.e., Q, j ) s calculated frst. The reverse ordern s needed for the recurson proalty alorthm, ecause P, j cannot e used untl t has een fully computed n the P, j loop. The pseudocode n Fure 4 detals the outcome of these transformatons for the unpseudoknotted case. Ths modfed alorthm reverses the flow of the partton functon calculaton and ncrementally determnes all recurson proaltes (frequences of famles of structures), ased on the proaltes of all superstructures that drectly contan them. Once recurson proaltes are computed for all and j, the aseparn proalty p, j s smply P, j, ecause Q, j s assocated wth every structure s n whch j appears, and j s assocated wth eactly one Q, j. By startn from a more complcated O(N 3 ) partton functon alorthm, 18,21 the computatonal complety of the recurson proalty alorthm can also e reduced to O(N 3 ) as descred n the Append. Pseudoknots condtonal proalty p P, j Q,d1 Q d,e /Q, j P,d1 p The procedure outlned aove for otann recurson proalty alorthms s equally applcale to a new partton functon alorthm that ncludes pseudoknots (see the pseudocode of F. 21 n ref. 18). For the unpseudoknotted alorthm, all ase pars stem P d,e p (3) Specfcally, the rhthand sde (RHS) of each recursve update s dvded y the lefthand sde (LHS), and the P term correspondn to the new denomnator s multpled y ths quotent. The resultn proaltes, temporarly stored as p, are susequently added to every Ptype value correspondn to the Qtype terms on the RHS of the ornal statement (2). To understand ths transformaton, recall that Q, j, Q m, j and Q, j are partton functons for structural suclasses of the full sequence. In recursve updates such as (2), the rato of the RHS to the fully computed LHS corresponds to the proalty that a structure drawn from an equlrum ensemle defned y the LHS partton functon s n the suensemle defned y the RHS partton functon. As an eample, transformaton (3) states that for any, d, e, j, the structures represented y Q, j partally consst of sustructures represented y Q,d1 and Q d,e. Consequently, once the proalty P, j s determned, t can e used to aument P,d1 and P d,e ecause the frequences of the correspondn sustructures wthn the Q, j ensemle can e derved from Q,d1 and Q d,e. By acktrackn throuh the partton functon alorthm and transformn all recursve updates analaously to (3), proaltes can e calculated for each recurson. Fure 4. O(N 4 ) recurson proalty alorthm that ecludes pseudoknots. For smplcty, we omt detals such as checkn for updates wth zero n the denomnator (n whch case the numerator wll also evaluate to zero and the epresson should e skpped).
4 1298 Drks and Perce Vol. 25, No. 10 Journal of Computatonal Chemstry from Q recursons, so the values stored n P are precsely the desred proaltes (.e., p, j P, j ). For the pseudoknotted case, P, j only ves the proalty that and j form a nested par. The full aseparn proalty must also take nto consderaton those ase pars that are nonnested and lead to pseudoknotted structures (termed apspannn pars n ref. 18). For these apspannn pars, there s no snle recurson proalty that represents the contruton to p, j. However, ths contruton may e succnctly represented n terms of Qtype and Ptype matrces for the full pseudoknotted alorthm. A new set of quanttes, P, j, wll e used to store the ase parn proaltes of j apspannn pars n pseudoknots. The most pertnent recurson proalty, P,d,e, j, stores the proalty of a ap structure wth outer apspannn par j and nner apspannn par d e correspondn to the partton functon recurson Q,d,e, j (see F. 19 n ref. 18). Due to the structure of the Q,d,e, j recurson, the sum of P,d,e, j over all values of d, e precsely ves the proalty of an outer par j P, j However, the sum of P,d,e, j dej P,d,e, j. (4) over all values of, j does not ve the proalty of an nner par d e, ecause the same nner par may e present n multple recursons requred to defne the same secondary structure. To correctly determne the proaltes of nner apspannn pars, only the porton of P that corresponds to calln Q drectly from Q l should e ncluded P d,f defj l P,e,f, j z Q,d,f, j Q d1,e l ep 2 /RT/Q,e,f, j. (5) Here, Q l and Q z are partton functon recursons used to defne the nteror structure of a pseudoknot, and 2 s a pseudoknot enery parameter (see Fs. 18 and 12 n ref. 18). Allown pseudoknots, the total proalty of a ase par j s then p, j P, j P, j. Pseudocode detaln the alorthm for computn recurson proaltes n the pseudoknotted case s provded n Fure 5, where the calculaton of P, j usn (4) and (5) has een emedded at lttle addtonal cost. [Note that (4) and (5) use dfferent ndces for P to mantan consstency wth the pseudocode.] In the Append, we descre how to reduce the complety of the pseudoknotted alorthm from O(N 6 ) to O(N 5 ). Methods The standard enery model 4 and pseudoknot etenson 18 are mplemented as descred prevously, 18 ncludn danle eneres and penaltes for helces not termnated y a G C par. These terms do not chane the structure of the recursons descred n the pseudocode and are omtted for clarty. Coaal stackn contrutons are not ncluded n the physcal model, as t s unclear how to treat dfferent stackns assocated wth the same secondary structure n the contet of the partton functon. To mantan consstency wth a recent desn study, 23 danle eneres are treated analoously to the d2 opton n the Venna packae. 10 Follown ths approach, danle eneres are ncluded even f two helces are separated y one or zero ases, provdn some compensaton for the nelect of coaal stackn onuses. Applcatons The recurson proalty alorthm provdes a smple, eneral method for calculatn the frequency of varous sustructures n the ensemle of states for a ven nuclec acd. Baseparn proaltes derved from the recurson proaltes are partcularly useful for analyzn secondary structure va dot plot analyses. 11 A tradtonal dot plot depcts the proaltes of formn all possle j ase pars. In the case of pseudoknots, the dot plot can e decomposed nto two dot plots one for nested pars and one for nonnested apspannn pars. To see the utlty of ths decomposton, calculatons were run on wldtype and mutant sequences of a pseudoknot construct derved from human telomerase RNA. 20 Epermental evdence suests that ths pseudoknot ests n equlrum wth an alternatve, harpn structure, and that ths equlrum functons as a olocal swtch. 19 A twopont mutant, found n a small percentae of people, shfts the equlrum towards the harpn structure, leadn to a dsease known as dyskeratoss conenta. 19 Feon and coworkers 20 eamne ths shft n equlrum for sements of the wldtype and mutant sequences descred n Fure 1, revealn that the pseudoknot conformaton domnates the harpn for the wldtype sequence (95% to 5%) ut competes rouhly equally n the mutant sequence (50% to 50%). Usn prelmnary pseudoknot parameters, 18 eneres were computed for oth the wldtype sequence and the twopont mutant on the pseudoknotted and harpn structures. The predcted eneres n Tale 1 match reasonaly well wth epermental values. 20 For the wldtype sequence, the dsparty etween the pseudoknot and harpn eneres suests an equlrum that favors the more stale pseudoknot. In contrast, the eneres for the doule mutant sequence suest a more alanced equlrum. Fures 6 and 7 llustrate that the harpn conformaton has a snfcant mpact on the par proaltes for the mutant, ut not for the wldtype sequence. Baseparn proaltes can also e used to construct metrcs for evaluatn nuclec acd desns. The secondary structure s may e descred y a symmetrc N N matr S wth entres S, j 1 f s contans ase par j and S, j 0 otherwse. We aument ths matr y an addtonal column wth entres S,N1 1 f ase s unpared and S,N1 0 otherwse. Hence, each row sum s one. For a ven sequence of lenth N, the metrc 23 ns* N 1N 1jN1 p, j S*, j represents the averae numer of nucleotdes that dffer from the taret secondary structure s* at thermodynamc equlrum. Ths
5 Alorthm for Computn Nuclec Acd BaseParn Proaltes 1299 Fure 5. O(N 6 ) recurson proalty alorthm that ncludes a class of pseudoknots. Modfcatons requred to produce an O(N 5 ) verson of the alorthm are noted n comments. See the Append for detals.
6 1300 Drks and Perce Vol. 25, No. 10 Journal of Computatonal Chemstry Tale 1. Enery Comparsons for Human Telomerase RNA Constructs. Eneres (kcal/mol) RNA Conformaton G ep G calc Wldtype Pseudoknot Harpn 9.8 a 11.5 c Mutant Pseudoknot Harpn 10.5 a 11.5 c a Eperments were performed on partal sequences that ecluded the 18 nucleotdes on the 3 end to prevent the formaton of pseudoknots. 20 Ths truncaton does not affect the correspondn G calc. A related pseudoknot structure that s otherwse dentcal ut omts the three consecutve A U pars n the stem wth the ule loop s predcted to e 0.5 kcal/mol more stale. c The secondary structure enery calculaton nores the four consecutve noncanoncal ase pars that are oserved to close the nteror loop n the harpn stem. 20 s a less strnent metrc than p(s*), the proalty that the sequence eactly adopts structure s*; even f p(s*) s not close to unty, n(s*) can stll e small f the equlrum ensemle s domnated y structures that dffer only slhtly from s*. It s llustratve to compare the two metrcs on a real desn prolem nvolvn pseudoknots. For eample, Wnfree et al. 14 desned and constructed DNA doulecrossover molecules 24 that nteract to form a twodmensonal lattce wth a pseudoknotted unt cell. These sequence desns were performed usn sequence symmetry mnmzaton 25 to ensure that ncorrectly pared susequences of lenth s would always contan at least one msmatch and most ncorrectly pared susequences of lenth fve would also contan a msmatch. 14 Lackn DNA pseudoknot parameters, we eamne an RNA analo of ther sequence for the porton of the pseudoknotted unt cell depcted n Fure 8a. The proalty of adoptn the taret structure s p(s*) 0.1 and the averae numer of ncorrect nucleotdes s n(s*) 4.0. The low value of Fure 6. Dot plots for wldtype human telomerase RNA. (a) Pseudoknot (ottom left) and harpn (top rht) constructs. For () and (c), lare dots ndcate a p, j 0.5 and small dots ndcate 0.5 p, j () Baseparn proaltes ncludn pseudoknots (ottom left) and ecludn pseudoknots (top rht). (c) A decomposton of the full aseparn proaltes nto apspannn pars (ottom left) and nested pars (top rht). Note that there are no nested pars wth snfcant proalty, ndcatn that pseudoknot conformatons are domnatn the equlrum. Fure 7. Dot plots for doule mutant human telomerase RNA. The plots are analoous to those of Fure 6. The key dfference s oserved n (c), where the harpn stem appears as oth apspannn pars and nested pars, ndcatn the ncreased snfcance of harpn conformatons. p(s*) mht possly ndcate a cause for concern, ut for a structure wth 90 nucleotdes and helces of lenth eht, the averae numer of ncorrect nucleotdes s relatvely small. Hence, t s not surprsn that the sequence ehaves well epermentally, demonstratn the correct aseparn topoloy despte slht predcted varatons at the ends of helces. The dot plot n Fure 8 llustrates the smlarty etween the averae structure and the desred taret. Interestnly, desn methods descred n prevous work 23 can e used, n conjuncton wth the pseudoknot partton functon alorthm, to fnd sequences that acheve p(s*) 0.98 and n(s*) 1. It s unclear whether these sequences would provde any epermental eneft for ths system (even ven a perfect enery model), ecause the dfference etween n(s*) 4 and n(s*) 1 may e lost n epermental nose. By contrast, f a sequence produced p(s*) 0.1 wth n(s*) 4, then the equlrum ensemle could nclude mportant structures dffern snfcantly from the taret structure. Conclusons A eneral transformaton rule etends nuclec acd partton functon alorthms to calculate recurson proaltes, whch n turn, can e used to compute aseparn proaltes. We use ths approach to derve an alorthm for computn aseparn proaltes startn from a partton functon alorthm that ncludes a class of pseudoknots. The same stratey wll apply to future partton functon etensons that follow the same dynamc prorammn paradm. To demonstrate the utlty of aseparn proaltes, calculatons were performed on a pseudoknot/harpn construct thouht to represent an mportant olocal swtch. In areement wth epermental evdence, the computatonal results ndcate that the pseudoknot domnates the harpn for the wldtype sequence, ut not for the doule mutant. Baseparn proaltes were also used to eamne the ensemle propertes of a synthetc nuclec acd sequence desned to assemle nto a pseudoknotted doulecrossover molecule. The averae numer of ncorrect nucleotdes was found to e small, suestn that the relatvely low computed proalty of adoptn the
7 Alorthm for Computn Nuclec Acd BaseParn Proaltes 1301 Acknowledments We wsh to thank C. Ueda for dscussons on human telomerase RNA and E. Wnfree for dscussons on the DNA lattce. Append: Reducn Computatonal Complety Fure 8. Computatonal eamnaton of a pseudoknotted DNA nanostructure. (a) Secondary structure for a doulecrossover molecule that forms a porton of the unt cell n a twodmensonal lattce. 14 For our computatonal study, we jon the lue and orane strands (arrows denote 3) nto a snle strand usn aulary nucleotdes (reen) to facltate the use of the snlestranded partton functon alorthm. 18 In the asence of DNA pseudoknot parameters, we consder the RNA analo 5CCAACUCCUAGCGAUUUUUCGCUAGGUUUACCA GAUCCACAAGCCGACGUUACAUUUUGGAUCUGGUAAG UUGGUGUAACGUCGGCUUGU3, where the nteror hyphens denote the oundares of the aulary lnker sement. () Dot plot analyss of the desned sequence. The ottom left depcts the asepars n the taret structure, and the upper rht depcts the aseparn proaltes. Lare dots ndcate a p, j 0.5 and small dots ndcate 0.5 p, j The crcles ndcate the major dfferences etween the taret structure and the calculated par proaltes. taret secondary structure should not snfcantly affect the epermental performance of the molecule. Software Download The alorthms descred n ths artcle are avalale for download at as part of the NUPACK software sute. The alorthms presented n the man tet provde an neffcent treatment of nteror loops. By eplotn the form of the nteror loop potental functon, the computatonal complety of the partton functon alorthms ecludn and ncludn pseudoknots can e reduced y a factor of N, where N s the sequence lenth. 18,21 A detaled descrpton of the fastloops treatment s provded n ref. 18 and the correspondn Supplementary Materal. The fastloops modfcaton detracts from the smplcty of the presentaton ecause the necessary recursons do not conform to the same structure as the other terms n the alorthm. Here, we descre the etenson of ths approach to recurson proalty alorthms. In the unpseudoknotted case, pseudocode for an O(N 3 ) partton functon alorthm s provded n Fure 11 of ref. 18, whch employs the fastloops functon of Supplementary Materal Fure S2. To ths pont, we have assumed that all Qtype values are accessle at the end of the partton functon calculaton. For the fastloops methods, the values Q, Q 1 and Q 2 are computed on the fly and dscarded to save memory. Hence, for the recurson proalty alorthm, t s necessary to recompute the Q type terms at the same tme that the correspondn P type terms are calculated. An O(N 3 ) recurson proalty alorthm that ecludes pseudoknots s descred n Fure A1, whch references the functon fastloopsn3 of Fure A2. If pseudoknots are ncluded, the computatonal complety of the recurson proalty alorthm n Fure 5 s reduced to O(N 5 ) usn fastloopsn5 descred n Fure A3. A few aspects of the fastloopsn3 and fastloopsn5 routnes deserve menton. It s advsale to revew the relevant sectons of ref. 18 and the correspondn Supplementary Materal efore proceedn. An nteror loop wth closn par j and nteror par d e has enery G nteror,d,e, j, sdes of lenths L 1 d 1, L 2 j e 1, (6) and sze L 1 L 2. Loops wth oth L 1 4 and L 2 4 are termed etensle and ther contrutons to the partton functon alorthm are calculated usn Q. Furthermore, Q also contans nformaton aout possle etensle loops for whch the defntons of L 1, L 2 are the same ut and j are not requred to asepar. The partton functon alorthm eamnes susequences of lenth l j 1, startn wth l 1 and endn wth l N. Q s effcently calculated usn the etenson dentty [see eq. (15) of ref. 18], Q 1,s2 s 2 L 14 L 1L 2s2 s 2 L 24 L 1L 2s2 Q,s ep 1 s 2 1 s/rt (7)
8 1302 Drks and Perce Vol. 25, No. 10 Journal of Computatonal Chemstry whch relates Q,s (for susequences of lenth l ) to Q 1,s2 (for susequences of lenth l 2). The frst lne seeds Q wth cases that are oth etensle (L 1 4 and L 2 4) and at an end of the strand ( 1 or j N). For mplementaton purposes, the second lne of (8) s calculated durn the l, loop and temporarly stored 2 n Q 1,s2. The frst lne of (8) s added to ths contruton n the l 2, 1 loop. We retan the conventon that L 1 and L 2 are defned wth respect to the loop nde n whch they are calculated (.e., l, for the second lne and l 2, 1 for the frst lne). Dervaton of the alorthm to compute P requres careful consderaton. The quanttes Q and Q 2 contan ncomplete partton functon nformaton for possle etensle loops, ut they do not represent susequence partton functons n the manner of other Qtype matrces. In a normal recurson relaton, Fure A1. O(N 3 ) recurson proalty alorthm that ecludes pseudoknots. The alorthm proceeds from loner susequences to shorter ones, so n contrast to the analoous partton functon alorthm (see F. 11 of ref. 18), Q 1 and Q 2 refer to susequences whose lenths are shorter (y 1 and 2, respectvely) than the current susequence of lenth l. whch relates Q,s (for susequences of lenth l ) to Q 1,s2 (for susequences of lenth l 2). The frst lne seeds Q wth cases at an etenson order (L 1 4 or L 2 4) for susequent etenson to loner susequences. For concseness, we have ntroduced the defnton s ep 1 s 2 L 1 L 2 3 e, d, e 1, d 1/RTQ d,e, where d and e are defned mplctly n terms of L 1 and L 2. For mplementaton purposes, the second lne of (7) s calculated 2 durn the l, loop and temporarly stored n Q 1,s2. The frst lne of (7) s added to ths contruton n the l 2, 1 loop. As a result of ths two step procedure, we adopt the conventon that L 1 and L 2 are defned wth respect to the loop nde n whch they are calculated (.e., l, for the second lne and l 2, 1 for the frst lne). Ths conventon facltates the comparson of the etenson dentty wth pseudocode. The recurson proalty alorthm eamnes susequences of lenth l startn wth l N and endn wth l 1. To recompute Q n ths contet, we use the contracton dentty Q 1,s2 1 L 14,L 24 L 1L 2s2 s 2 jn L 14,L 24 L 1L 2s2 s 2 Q,s s L 14 s L 24 L 1L 2s L 1L 2s ep 1 s 2 1 s/rt (8) Fure A2. Pseudocode for computn nteror loop contrutons to P n O(N 3 ) as an alternatve to the O(N 4 ) nteror loop recurson of Fure 4.
9 Alorthm for Computn Nuclec Acd BaseParn Proaltes 1303 calculaton of P requres nformaton aout whch Q quanttes ultmately contrute to secondary structures n the ensemle. As a result, the etenson dentty (7) cannot smply e transformed usn the standard recurson proalty approach, whch assumes that oth sdes of the equaton represent susequence partton functons that are assured of contrutn to the equlrum ensemle. Ths realzaton suests computn P,s y addn the proaltes of all nternal loops that rely on Q,s to ncorporate nformaton n the partton functon. To calculate P,s (for a fed l ), note that Q,s wll e nvoked for all nteror loops (, d, e, j) wth nteror par d e and closn par j such that j j 0, L 1 4, L 2 4, L 1 L 2 s, (9) where L 1, L 2 and s are defned wth respect to and j. Hence, a partcular loop (, d, e, j) s dentfed wth a set of Q,s terms that are related y the etenson dentty (7). Alternatvely, a partcular Q,s term s dentfed wth all of the nteror loops (, d, e, j) to whch t ultmately contrutes va the etenson dentty. Consequently, from the noton of recurson proaltes ntroduced earler, P,s (for a fed l ) should e the sum of the proaltes of all nteror loops (, d, e, j) that satsfy the propertes (9). For the case where 1 N j (the case 1 N j yelds analoous results), t follows that P,s 1 p, d, e, j, (10) L 14,L 24 L 1L 2s where p(, d, e, j) s the proalty of the (, d, e, j) nteror loop n the equlrum ensemle of secondary structures. Because 2 P 1,s2 s defned smlarly, wth l and s decremented y 2, t follows that Fure A3. Pseudocode for computn nteror loop contrutons to P n O(N 5 ) as an alternatve to the O(N 6 ) nteror loop recurson of Fure 5. Qtype matrces on the rhthand sde are susequence partton functons descrn a local structural motf that contrutes to the larer susequence partton functon on the lefthand sde. Q,s contans nformaton aout possle etensle loops that may not actually est (f and j are not complementary). The etenson dentty (7) passes ths potentally useful nformaton on to 2 Q 1,s2. Consder, for eample, a chan of Q values related y the etenson dentty n a case where no complementary j ase par s encountered whle ncrementn l y 2 untl an end of the strand s reached. In ths scenaro, the values of Q computed n ths chan should not contrute to the correspondn recurson proaltes P ecause the values of Q are not dentfed wth any secondary structure n the equlrum ensemle. Hence, the 2 P 1,s2 1 1 p, d, e, j, (11) L 15,L 25 L 1L 2s where L 1 and L 2 are temporarly defned wth respect to and j to retan the sze constrant L 1 L 2 s. Comparn (10) and (11), we then dentfy the relatonshp 2 P 1,s2 P,s L 15,L 25 L 1L 2s 1 p, d, e, j 1, jj1 p, d, e, j L14,L24 L 1L 2s 1 p, d, e, j L15,L24, L 1L 2s where L 1 and L 2 contnue to e defned wth respect to and j. Fnally, we shft the ndces n the frst lne so that L 1 and L 2 are defned wth respect to 1 and j 1
10 1304 Drks and Perce Vol. 25, No. 10 Journal of Computatonal Chemstry 2 P 1,s2 P,s L 14,L 24 L 1L 2s2 1 p, d, e, j 1, jj1 p, d, e, j L14,L24 L 1L 2s 1 p, d, e, j L15,L24. L 1L 2s (12) Ths dentty relates P,s (for susequences of lenth l ) to P 1,s2 (for susequences of lenth l 2). For mplementaton purposes, the second lne s calculated durn the l, loop and 2 temporarly stored n Q 1,s2. Each of the sums of form 1 operates on a snle term, whch s a suset of the terms n the defnton of P,s (10). Hence, the sums of form 1 n (12) may e evaluated mplctly as P,s tmes a quotent wth Q,s n the denomnator and the correspondn suset of Q,s n the numerator. The frst lne s added to ths contruton n the l 2, 1 loop. There, the summaton corresponds to eactly those loops treated y Q 1,s2 n the case where 1 and j 1 ase par. As usual, L 1 and L 2 are defned wth respect to the loop nde n whch they are calculated (.e., l, for the second lne and l 2, 1 for the frst lne). References 1. Tnoco, I., Jr.; Uhlenec, O.; Levne, M. Nature 1971, 230, Turner, D. H.; Sumoto, N.; Freer, S. Annu Rev Bophys Bophys Chem 1988, 17, SantaLuca, J., Jr. Proc Natl Acad Sc USA 1998, 95, Mathews, D.; Sana, J.; Zuker, M.; Turner, D. J Mol Bol 1999, 288, Zuker, M. Curr Opn Struct Bol 2000, 10, Waterman, M. In Studes n Foundatons and Comnatorcs: Advances n Mathematcs Supplemental Studes; Academc Press: New York, 1978, 1, Waterman, M.; Smth, T. Math Bosc 1978, 42, Nussnov, R.; Peczenk, J.; Grs, J.; Kletman, D. SIAM J Appl Math 1978, 35, Zuker, M.; Steler, P. Nuclec Acds Res 1981, 9, Hofacker, I.; Fontana, W.; Stadler, P.; Bonhoeffer, L.; Tacker, M.; Schuster, P. Chem Monthly 1994, 125, McCaskll, J. Bopolymers 1990, 29, Bonhoeffer, S.; McCaskll, J.; Stadler, P.; Schuster, P. Eur Bophys J 1993, 22, van Batenur, F.; Gultyaev, A.; Plej, C.; N, J. Nuclec Acds Res 2000, 28, Wnfree, E.; Lu, F.; Wenzler, L.; Seeman, N. C. Nature 1998, 394, Yan, H.; LaBean, T.; Fen, L.; Ref, J. Proc Natl Acad Sc USA 2003, 100, Rvas, E.; Eddy, S. J Mol Bol 1999, 285, Akutsu, T. Dscrete Appl Math 2000, 104, Drks, R.; Perce, N. A. J Comput Chem 2003, 24, Comoll, L.; Smrnov, I.; Xu, L.; Blackurn, E.; James, T. Proc Natl Acad Sc USA 2002, 99, Themer, C.; Fner, L.; Trantrek, L.; Feon, J. Proc Natl Acad Sc USA 2003, 100, Lynso, R.; Zuker, M.; Pedersen, C. Bonformatcs 1999, 15, Dn, Y.; Lawrence, C. Nuclec Acds Res 2003, 31, Drks, R.; Ln, M.; Wnfree, E.; Perce, N. A. Nuclec Acds Res 2004, 32, Fu, T.J.; Seeman, N. C. Bochemstry 1993, 32, Seeman, N. C. J Theor Bol 1982, 99, 237.
An Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationBERNSTEIN POLYNOMIALS
OnLne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationThe Mathematical Derivation of Least Squares
Pscholog 885 Prof. Federco The Mathematcal Dervaton of Least Squares Back when the powers that e forced ou to learn matr algera and calculus, I et ou all asked ourself the ageold queston: When the hell
More informationInequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationImplementation of Deutsch's Algorithm Using Mathcad
Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages  n "Machnes, Logc and Quantum Physcs"
More informationEconomics Letters 59 (1998) 385 390. Jonathan Morduch* Received 12 January 1998; accepted 9 March 1998
Economcs Letters 59 (998) 385 390 Poverty, economc rowth, and averae ext tme Jonathan Morduch* Hoover Insttuton L27, Stanford Unversty, Stanford, CA 94305600, USA Receved 2 January 998; accepted 9 March
More informationA Secure PasswordAuthenticated Key Agreement Using Smart Cards
A Secure PasswordAuthentcated Key Agreement Usng Smart Cards Ka Chan 1, WenChung Kuo 2 and JnChou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 /  Communcaton Networks II (Görg) SS20  www.comnets.unbremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationReporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide
Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationMAPP. MERIS level 3 cloud and water vapour products. Issue: 1. Revision: 0. Date: 9.12.1998. Function Name Organisation Signature Date
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPPATBDClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationGas Deliverability Model with Different Vertical Wells Properties
PROC. ITB En. Scence Vol. 35 B, No., 003, 538 5 Gas Delverablty Model wth Dfferent Vertcal Wells Proertes L. Mucharam, P. Sukarno, S. Srear,3, Z. Syhab, E. Soewono,3, M. Ar 3 & F. Iral 3 Deartment of
More information8 Algorithm for Binary Searching in Trees
8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the
More informationQ3.8: A person trying to throw a ball as far as possible will run forward during the throw. Explain why this increases the distance of the throw.
Problem Set 3 Due: 09/3/, Tuesda Chapter 3: Vectors and Moton n Two Dmensons Questons: 7, 8,, 4, 0 Eercses & Problems:, 7, 8, 33, 37, 44, 46, 65, 73 Q3.7: An athlete performn the lon jump tres to acheve
More informationCausal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes causeandeffect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationBrigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
More informationA Probabilistic Theory of Coherence
A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want
More information8.4. Annuities: Future Value. INVESTIGATE the Math. 504 8.4 Annuities: Future Value
8. Annutes: Future Value YOU WILL NEED graphng calculator spreadsheet software GOAL Determne the future value of an annuty earnng compound nterest. INVESTIGATE the Math Chrstne decdes to nvest $000 at
More informationOn the Optimal Control of a Cascade of HydroElectric Power Stations
On the Optmal Control of a Cascade of HydroElectrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationThe Current Employment Statistics (CES) survey,
Busness Brths and Deaths Impact of busness brths and deaths n the payroll survey The CES probabltybased sample redesgn accounts for most busness brth employment through the mputaton of busness deaths,
More informationQUANTUM MECHANICS, BRAS AND KETS
PH575 SPRING QUANTUM MECHANICS, BRAS AND KETS The followng summares the man relatons and defntons from quantum mechancs that we wll be usng. State of a phscal sstem: The state of a phscal sstem s represented
More informationMethods for Calculating Life Insurance Rates
World Appled Scences Journal 5 (4): 653663, 03 ISSN 88495 IDOSI Pulcatons, 03 DOI: 0.589/dos.wasj.03.5.04.338 Methods for Calculatng Lfe Insurance Rates Madna Movsarovna Magomadova Chechen State Unversty,
More informationPhysics 110 Spring 2006 2D Motion Problems: Projectile Motion Their Solutions
Physcs 110 Sprn 006 D Moton Problems: Projectle Moton Ther Solutons 1. A placekcker must kck a football from a pont 36 m (about 40 yards) from the oal, and half the crowd hopes the ball wll clear the
More informationTuition Fee Loan application notes
Tuton Fee Loan applcaton notes for new parttme EU students 2012/13 About these notes These notes should be read along wth your Tuton Fee Loan applcaton form. The notes are splt nto three parts: Part 1
More informationDamage detection in composite laminates using cointap method
Damage detecton n composte lamnates usng contap method S.J. Km Korea Aerospace Research Insttute, 45 EoeunDong, YouseongGu, 35333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The contap test has the
More informationNMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582
NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!
More informationSmall pots lump sum payment instruction
For customers Small pots lump sum payment nstructon Please read these notes before completng ths nstructon About ths nstructon Use ths nstructon f you re an ndvdual wth Aegon Retrement Choces Self Invested
More information+ + +   This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
More informationNUMERICAL SIMULATION OF FORCED CONVECTION IN OIL SANDS USING LATTICE BOLTZMANN METHOD
Internatonal Journal of Mechancal Enneern and Technoloy (IJMET) Volume 7, Issue, JanFeb, pp. 7889, Artcle ID: IJMET_7 9 Avalable onlne at http://www.aeme.com/ijmet/ssues.asp?jtype=ijmet&vtype=7&itype=
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationPowerofTwo Policies for Single Warehouse MultiRetailer Inventory Systems with Order Frequency Discounts
Powerofwo Polces for Sngle Warehouse MultRetaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More informationIDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS
IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,
More informationNordea G10 Alpha Carry Index
Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and
More informationOptimal Service Selection Based on Business for Cloud Computing
2013 Internatonal Conference on Cloud and Servce Computn Optmal Servce Selecton Based on Busness for Cloud Computn Xaochen Lu, Chunhe Xa, Zhao We, Xaonn Sun Ben Key Laboratory of Networ Technoloy, School
More informationFREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES
FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES Zuzanna BRO EKMUCHA, Grzegorz ZADORA, 2 Insttute of Forensc Research, Cracow, Poland 2 Faculty of Chemstry, Jagellonan
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationEfficient Project Portfolio as a tool for Enterprise Risk Management
Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse
More informationCommunication Networks II Contents
8 / 1  Communcaton Networs II (Görg)  www.comnets.unbremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP
More information21 Vectors: The Cross Product & Torque
21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rghthand rule for the cross product of two vectors dscussed n ths chapter or the rghthand rule for somethng curl
More informationSCALAR A physical quantity that is completely characterized by a real number (or by its numerical value) is called a scalar. In other words, a scalar
SCALAR A phscal quantt that s completel charactered b a real number (or b ts numercal value) s called a scalar. In other words, a scalar possesses onl a magntude. Mass, denst, volume, temperature, tme,
More informationTo manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.
Corporate Polces & Procedures Human Resources  Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationArtificial Intelligence Approach to Evaluate Students Answerscripts Based on the Similarity Measure between Vague Sets
Wan, H.Y., & Chen, S. M. (007). rtfcal Intellence pproach to Evaluate Students nswerscrpts ased on the Smlarty Measure between Vaue Sets. Educatonal Technoloy & Socety, 0 (), . rtfcal Intellence pproach
More informationJoe Pimbley, unpublished, 2005. Yield Curve Calculations
Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward
More informationTime Value of Money Module
Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationMean Molecular Weight
Mean Molecular Weght The thermodynamc relatons between P, ρ, and T, as well as the calculaton of stellar opacty requres knowledge of the system s mean molecular weght defned as the mass per unt mole of
More information1E6 Electrical Engineering AC Circuit Analysis and Power Lecture 12: Parallel Resonant Circuits
E6 Electrcal Engneerng A rcut Analyss and Power ecture : Parallel esonant rcuts. Introducton There are equvalent crcuts to the seres combnatons examned whch exst n parallel confguratons. The ssues surroundng
More informationLinear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
More informationLecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCullochPtts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
More informationx f(x) 1 0.25 1 0.75 x 1 0 1 1 0.04 0.01 0.20 1 0.12 0.03 0.60
BIVARIATE DISTRIBUTIONS Let be a varable that assumes the values { 1,,..., n }. Then, a functon that epresses the relatve frequenc of these values s called a unvarate frequenc functon. It must be true
More informationSTATISTICAL DATA ANALYSIS IN EXCEL
Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 1401013 petr.nazarov@crpsante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for
More informationThe covariance is the two variable analog to the variance. The formula for the covariance between two variables is
Regresson Lectures So far we have talked only about statstcs that descrbe one varable. What we are gong to be dscussng for much of the remander of the course s relatonshps between two or more varables.
More informationIMPACT ANALYSIS OF A CELLULAR PHONE
4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng
More informationEfficient Striping Techniques for Variable Bit Rate Continuous Media File Servers æ
Effcent Strpng Technques for Varable Bt Rate Contnuous Meda Fle Servers æ Prashant J. Shenoy Harrck M. Vn Department of Computer Scence, Department of Computer Scences, Unversty of Massachusetts at Amherst
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 MultpleChoce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multplechoce questons. For each queston, only one of the answers s correct.
More informationHollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA )
February 17, 2011 Andrew J. Hatnay ahatnay@kmlaw.ca Dear Sr/Madam: Re: Re: Hollnger Canadan Publshng Holdngs Co. ( HCPH ) proceedng under the Companes Credtors Arrangement Act ( CCAA ) Update on CCAA Proceedngs
More informationTexas Instruments 30Xa Calculator
Teas Instruments 30Xa Calculator Keystrokes for the TI30Xa are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the tet, check
More informationIT09  Identity Management Policy
IT09  Identty Management Polcy Introducton 1 The Unersty needs to manage dentty accounts for all users of the Unersty s electronc systems and ensure that users hae an approprate leel of access to these
More informationAddendum to: Importing SkillBiased Technology
Addendum to: Importng SkllBased Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our
More informationEquilibrium in competitive insurance markets with ex ante adverse selection and ex post moral hazard
Journal of Pulc Economcs 84 (2002) 251 278 www.elsever.com/ locate/ econase Equlrum n compettve nsurance markets wth ex ante adverse selecton and ex post moral hazard Wllam Jack* Department of Economcs,
More informationPassive Filters. References: Barbow (pp 265275), Hayes & Horowitz (pp 3260), Rizzoni (Chap. 6)
Passve Flters eferences: Barbow (pp 6575), Hayes & Horowtz (pp 360), zzon (Chap. 6) Frequencyselectve or flter crcuts pass to the output only those nput sgnals that are n a desred range of frequences (called
More informationSimple Interest Loans (Section 5.1) :
Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part
More informationStaff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall
SP 200502 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 148537801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent
More information1.1 The University may award Higher Doctorate degrees as specified from timetotime in UPR AS11 1.
HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher
More informationPOLYSA: A Polynomial Algorithm for Nonbinary Constraint Satisfaction Problems with and
POLYSA: A Polynomal Algorthm for Nonbnary Constrant Satsfacton Problems wth and Mguel A. Saldo, Federco Barber Dpto. Sstemas Informátcos y Computacón Unversdad Poltécnca de Valenca, Camno de Vera s/n
More informationCLUSTER SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR 1
amplng Theory MODULE IX LECTURE  30 CLUTER AMPLIG DR HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR It s one of the asc assumptons n any samplng procedure that the populaton
More informationInterleaved Power Factor Correction (IPFC)
Interleaved Power Factor Correcton (IPFC) 2009 Mcrochp Technology Incorporated. All Rghts Reserved. Interleaved Power Factor Correcton Slde 1 Welcome to the Interleaved Power Factor Correcton Reference
More informationThe Magnetic Field. Concepts and Principles. Moving Charges. Permanent Magnets
. The Magnetc Feld Concepts and Prncples Movng Charges All charged partcles create electrc felds, and these felds can be detected by other charged partcles resultng n electrc force. However, a completely
More informationShallow water dynamics and dispersion
Lecture Shallow water dynamcs and dsperson 7. Shallow water equatons (Quck reference) The shallow water equatons descrbe the dynamcs of a hydrostatc, homoenous flud layer: t u + u x u + v y u fv + x η
More informationCHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationTexas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
More informationMismatch in Law School by Jesse Rothstein, Princeton University and NBER Albert Yoon, Northwestern University CEPS Working Paper No.
Msmatch n Law School y Jesse Rothsten Prnceton Unversty and NBER Alert Yoon Northwestern Unversty CEPS Workng Paper No. 123 Feruary 2006 Astract: An mportant crtcsm of affrmatve acton polces n admssons
More informationProductForm Stationary Distributions for Deficiency Zero Chemical Reaction Networks
Bulletn of Mathematcal Bology (21 DOI 1.17/s11538195174 ORIGINAL ARTICLE ProductForm Statonary Dstrbutons for Defcency Zero Chemcal Reacton Networks Davd F. Anderson, Gheorghe Cracun, Thomas G. Kurtz
More informationFormula of Total Probability, Bayes Rule, and Applications
1 Formula of Total Probablty, Bayes Rule, and Applcatons Recall that for any event A, the par of events A and A has an ntersecton that s empty, whereas the unon A A represents the total populaton of nterest.
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationHeterogeneous Paths Through College: Detailed Patterns and Relationships with Graduation and Earnings
Heterogeneous Paths Through College: Detaled Patterns and Relatonshps wth Graduaton and Earnngs Rodney J. Andrews The Unversty of Texas at Dallas and the Texas Schools Project Jng L The Unversty of Tulsa
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 6105194390,
More informationA Performance Analysis of View Maintenance Techniques for Data Warehouses
A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao
More informationMARKET SHARE CONSTRAINTS AND THE LOSS FUNCTION IN CHOICE BASED CONJOINT ANALYSIS
MARKET SHARE CONSTRAINTS AND THE LOSS FUNCTION IN CHOICE BASED CONJOINT ANALYSIS Tmothy J. Glbrde Assstant Professor of Marketng 315 Mendoza College of Busness Unversty of Notre Dame Notre Dame, IN 46556
More informationFINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals
FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant
More informationVembu StoreGrid Windows Client Installation Guide
Ser v cepr ov dered t on Cl enti nst al l at ongu de W ndows Vembu StoreGrd Wndows Clent Installaton Gude Download the Wndows nstaller, VembuStoreGrd_4_2_0_SP_Clent_Only.exe To nstall StoreGrd clent on
More informationEliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors
Elmnatng Condtonally Independent Sets n Factor Graphs: A Unfyng Perspectve ased on Smart Factors Luca Carlone, Zsolt Kra, Chrs Beall, Vadm Indelman, and Frank Dellaert Astract Factor graphs are a general
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationELE427  Testing Linear Sensors. Linear Regression, Accuracy, and Resolution.
ELE47  Testng Lnear Sensors Lnear Regresson, Accurac, and Resoluton. Introducton: In the frst three la eperents we wll e concerned wth the characterstcs of lnear sensors. The asc functon of these sensors
More informationGibbs Free Energy and Chemical Equilibrium (or how to predict chemical reactions without doing experiments)
Gbbs Free Energy and Chemcal Equlbrum (or how to predct chemcal reactons wthout dong experments) OCN 623 Chemcal Oceanography Readng: Frst half of Chapter 3, Snoeynk and Jenkns (1980) Introducton We want
More information