01796nas a2200193   4500008004100000022001400041245010200055210006900157260007100226300001400297490000700311520104000318100002201358700002601380700002401406700002201430700002101452856012901473       2007                        eng d  a0024-929700aHydrogen bonding in trivinyl monomers: implications for inclusion complexation and polymerization0 aHydrogen bonding in trivinyl monomers implications for inclusion  a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcMAR  a1824-18300 v403 a<p>Trimethylolpropane trimethacrylate (TMPTMA) formed a 1:2 inclusion complex (IC) with beta-cyclodextrin (beta-CD). Polymerization of the complex resulted in a soluble, linear polymer containing two pendant unsaturations per repeat unit since the methacryloyl groups included in the beta-CD cavity did not react with the growing radical chain. Trimethylolpropane triacrylate (TMPTA) formed a 1:1 complex with beta-CD. Yet a soluble polymer containing two pendant unsaturations per repeat unit was obtained. Computational analysis confirmed that in TMPTA hydrogen bonding between C-H center dot center dot center dot O=C brings two acryloyl groups in close vicinity of one another. As a result, both acryloyl groups were included in the same beta-CD cavity. Trimethylolpropane diacrylate 4-vinylbenzoate (TMPDAVB) once again formed 1:2 IC as a result of disruption of hydrogen bonds between two acryloyl groups. The study highlights that an understanding of the composition of the IC is more important than its stoichiometry alone.</p>1 aSatav, Sunita, S.1 aKarmalkar, Rohini, N.1 aKulkarni, Mohan, G.1 aMulpuri, Nagaraju1 aSastry, Narahari  uhttps://library.ncl.res.in/content/hydrogen-bonding-trivinyl-monomers-implications-inclusion-complexation-and-polymerization01949nas a2200253   4500008004100000022001400041245008100055210006900136260010600205300001400311490000700325520099400332653002101326653002201347653002801369653003701397653003201434100002801466700002301494700002301517700001901540700002101559856011501580       2009                        eng d  a0040-403900aTotal syntheses and absolute stereochemistry of decarestrictines C-1 and C-20 aTotal syntheses and absolute stereochemistry of decarestrictines  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcOCT  a5636-56390 v503 a<p>The total syntheses of decarestrictines C-1 and C-2 have been described. The synthetic strategy involves a practical and flexible approach using esterification and ring-closing metathesis to unite the acid and alcohol fragments. The acid fragments are enantiomers of each other and have been prepared from L-(-)-malic acid via similar transformations; in Sharpless asymmetric epoxidation, (+)-DET has been used for decarestrictine C-1 and (-)-DET for decarestrictine C-2. The alcohol fragment is identical for both decarestrictines C-1 and C-2 and has been accessed from D-(+)-mannitol. Comparison of the H-1 and C-13 NMR data combined with the computational studies predicts the presence of two conformations without and with hydrogen bonding (conformational isomers I and II for decarestrictine C-1), respectively. The H-1 and C-13 NMR data for decarestrictine C-2 completely agreed with the analytical data reported by Kibayashi et al. (C) 2009 Elsevier Ltd. All rights reserved.</p>10aDecarestrictines10aPinnick oxidation10aRing-closing metathesis10aSharpless asymmetric epoxidation10aYamaguchi coupling reaction1 aMohapatra, Debendra, K.1 aSahoo, Gokarneswar1 aRamesh, Dhondi, K.1 aRao, Srinivasa1 aSastry, Narahari  uhttps://library.ncl.res.in/content/total-syntheses-and-absolute-stereochemistry-decarestrictines-c-1-and-c-2-001028nas a2200181   4500008004100000022001400041245006200055210006100117260011000178300001400288490000700302520034600309100002900655700002600684700002100710700002000731856009500751       2011                        eng d  a1359-734500aPropargyl/methyl furanosides as potential glycosyl donors0 aPropargylmethyl furanosides as potential glycosyl donors  aTHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLANDbROYAL SOC CHEMISTRYcAUG  a9906-99080 v473 a<p>Transfuranosylations are not well studied though many similar studies exist for transpyranosylation; herein, we report that propargyl/methyl D-ribf- and D-lyxf- give only 1,2-trans glycosides whereas D-araf- and D-xylf- result in a mixture of 1,2-trans and 1,2-cis glycosides; observed facts are rationalised by computational studies.</p>1 aVidadala, Srinivasa, Rao1 aGayatri, Gaddamannugu1 aSastry, Narahari1 aHotha, Srinivas  uhttps://library.ncl.res.in/content/propargylmethyl-furanosides-potential-glycosyl-donors-007622nas a2201957   4500008004100000022001400041245016800055210006900223260000800292300001100300490000800311520199500319653001102314653001202325653001302337653002102350653001402371653002102385653001502406100001902421700002102440700002302461700001902484700001902503700002002522700002402542700001902566700001802585700002002603700001502623700002702638700003402665700002102699700001702720700001702737700001902754700002402773700003002797700001902827700001802846700002102864700001402885700002502899700001702924700001702941700001202958700002002970700001902990700001703009700002103026700002603047700002603073700001903099700001803118700002403136700003003160700002703190700001703217700002703234700003503261700002503296700002403321700002303345700002803368700002703396700002703423700002103450700002903471700001903500700001603519700001703535700001803552700001603570700001803586700001703604700002003621700001903641700002403660700002103684700001903705700001803724700002203742700002203764700002403786700002603810700002203836700002403858700002803882700002103910700001803931700001703949700002603966700001603992700001704008700001904025700002204044700001804066700001604084700001904100700002704119700002704146700002404173700001704197700002104214700002304235700001804258700002504276700002804301700001904329700002204348700002204370700001604392700001804408700002004426700001704446700001904463700001504482700002704497700003004524700001604554700001204570700001804582700002804600700002204628700002104650700002704671700002004698700001804718700002104736700001804757700002004775700002004795700001704815700001904832700001604851700001904867700001604886700002004902700002204922700002104944700001804965700001904983700002105002700002005023700002005043700002105063700002005084700002205104700003105126700001905157700002705176700003505203700002605238700002405264700002405288700003205312700003905344700002305383700001805406700002405424700002505448700002005473700002305493700001805516856013005534       2022                        eng d  a0010-482500aMachine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys0 aMachine learningbased approach to determine infection status in   cJUL  a1054190 v1463 a<p>Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.</p>
10aBBV15210aCovaxin10aCOVID-1910aEnsemble methods10aInfection10amachine learning10aSARS-CoV-21 aSingh, Prateek1 aUjjainiya, Rajat1 aPrakash, Satyartha1 aNaushin, Salwa1 aSardana, Viren1 aBhatheja, Nitin1 aSingh, Ajay, Pratap1 aBarman, Joydeb1 aKumar, Kartik1 aGayali, Saurabh1 aKhan, Raju1 aRawat, Birendra, Singh1 aTallapaka, Karthik, Bharadwaj1 aAnumalla, Mahesh1 aLahiri, Amit1 aKar, Susanta1 aBhosale, Vivek1 aSrivastava, Mrigank1 aMugale, Madhav, Nilakanth1 aPandey, C., P.1 aKhan, Shaziya1 aKatiyar, Shivani1 aRaj, Desh1 aIshteyaque, Sharmeen1 aKhanka, Sonu1 aRani, Ankita1 aPromila1 aSharma, Jyotsna1 aSeth, Anuradha1 aDutta, Mukul1 aSaurabh, Nishant1 aVeerapandian, Murugan1 aVenkatachalam, Ganesh1 aBansal, Deepak1 aGupta, Dinesh1 aHalami, Prakash, M.1 aPeddha, Muthukumar, Serva1 aVeeranna, Ravindra, P.1 aPal, Anirban1 aSingh, Ranvijay, Kumar1 aAnandasadagopan, Suresh, Kumar1 aKaruppanan, Parimala1 aRahman, Syed, Nasar1 aSelvakumar, Gopika1 aVenkatesan, Subramanian1 aKarmakar, Malay, Kumar1 aSardana, Harish, Kumar1 aKothari, Anamika1 aParihar, Devendra, Singh1 aThakur, Anupma1 aSaifi, Anas1 aGupta, Naman1 aSingh, Yogita1 aReddu, Ritu1 aGautam, Rizul1 aMishra, Anuj1 aMishra, Avinash1 aGogeri, Iranna1 aRayasam, Geethavani1 aPadwad, Yogendra1 aPatial, Vikram1 aHallan, Vipin1 aSingh, Damanpreet1 aTirpude, Narendra1 aChakrabarti, Partha1 aMaity, Sujay, Krishna1 aGanguly, Dipyaman1 aSistla, Ramakrishna1 aBalthu, Narender, Kumar1 aKumar, Kiran, A.1 aRanjith, Siva1 aKumar, Vijay1 aJamwal, Piyush, Singh1 aWali, Anshu1 aAhmed, Sajad1 aChouhan, Rekha1 aGandhi, Sumit, G.1 aSharma, Nancy1 aRai, Garima1 aIrshad, Faisal1 aJamwal, Vijay, Lakshmi1 aPaddar, Masroor, Ahmad1 aKhan, Sameer, Ullah1 aMalik, Fayaz1 aGhosh, Debashish1 aThakkar, Ghanshyam1 aBarik, S., K.1 aTripathi, Prabhanshu1 aSatija, Yatendra, Kumar1 aMohanty, Sneha1 aKhan, Md, Tauseef1 aSubudhi, Umakanta1 aSen, Pradip1 aKumar, Rashmi1 aBhardwaj, Anshu1 aGupta, Pawan1 aSharma, Deepak1 aTuli, Amit1 aChaudhuri, Saumya, Ray1 aKrishnamurthi, Srinivasan1 aPrakash, L.1 aRao, Ch1 aSingh, B., N.1 aChaurasiya, Arvindkumar1 aChaurasiya, Meera1 aBhadange, Mayuri1 aLikhitkar, Bhagyashree1 aMohite, Sharada1 aPatil, Yogita1 aKulkarni, Mahesh1 aJoshi, Rakesh1 aPandya, Vaibhav1 aMahajan, Sachin1 aPatil, Amita1 aSamson, Rachel1 aVare, Tejas1 aDharne, Mahesh1 aGiri, Ashok1 aMahajan, Sachin1 aParanjape, Shilpa1 aSastry, Narahari1 aKalita, Jatin1 aPhukan, Tridip1 aManna, Prasenjit1 aRomi, Wahengbam1 aBharali, Pankaj1 aOzah, Dibyajyoti1 aSahu, RaviKumar1 aDutta, Prachurjya1 aSingh, Moirangthem, Goutam1 aGogoi, Gayatri1 aTapadar, Yasmin, Begam1 aBabu, Elapavalooru, V. S. S. K1 aSukumaran, Rajeev, K.1 aNair, Aishwarya, R.1 aPuthiyamadam, Anoop1 aValappil, Prajeesh, Kooloth1 aPrasannakumari, Adrash, Velayudhan1 aChodankar, Kalpana1 aDamare, Samir1 aAgrawal, Ved, Varun1 aChaudhary, Kumardeep1 aAgrawal, Anurag1 aSengupta, Shantanu1 aDash, Debasis  uhttps://library.ncl.res.in/content/machine-learning-based-approach-determine-infection-status-recipients-bbv152-covaxin-whole