02233nas a2200157   4500008004100000022001400041245009700055210006900152260005700221300001200278490000700290520160500297100002601902700001801928856012901946       2013                        eng d  a0343-865100aMarine actinobacteria showing phosphate-solubilizing efficiency in Chorao Island, Goa, India0 aMarine actinobacteria showing phosphatesolubilizing efficiency i  a233 SPRING ST, NEW YORK, NY 10013 USAbSPRINGERcMAY  a421-4270 v663 a<p>The occurrence and distribution of an actinobacteria group of bacteria capable of dissolving insoluble phosphates were investigated in this study in marine environments, especially in sediments of Chorao Island, Goa Province, India. A total of 200 bacterial isolates of actinobacteria was isolated. All isolates were screened for phosphate-solubilizing activity on Pikovskaya's agar. Thirteen different isolates exhibiting maximum formation of halos (zone of solubilization) around the bacterial colonies were selected for quantitative estimations of P-solubilization. Quantitative estimations for P-solubilization were analyzed for up to 10 days at intervals of 24 h. Maximum solubilization from 89.3 +/- A 3.1 to 164.1 +/- A 4.1 mu g ml(-1) was observed after 6 days of incubation in six of all isolates, while the isolate NII-1020 showed maximum P-solubilization. The increase in solubilization coincided with the drop in pH. Many of these species showed wide range of tolerance to temperature, pH, and salt concentrations. Further, 16S rRNA gene sequence analyses were carried to identify the bacterial groups which are actively solubilized phosphate in vitro. Gene sequencing results reveal that all isolates were clustered into six different actinobacterial genera: Streptomyces, Microbacterium, Angustibacter, Kocuria, Isoptericola, and Agromyces. The presence of phosphate-solubilizing microorganisms and their ability to solubilize phosphate were indicative of the important role played by bacteria in the biogeochemical cycle of phosphorus and the plant growth in coastal ecosystems.</p>1 aDastager, Syed, Gulam1 aDamare, Samir  uhttps://library.ncl.res.in/content/marine-actinobacteria-showing-phosphate-solubilizing-efficiency-chorao-island-goa-india-006307nas a2201765   4500008004100000022001400041245009500055210006900150260000900219300001100228490000700239520113100246100001901377700001901396700002101415700002001436700001701456700002601473700002101499700002301520700001501543700002701558700003401585700002101619700002801640700001701668700001701685700002801702700003001730700002401760700001801784700002301802700001901825700002601844700002601870700003301896700001801929700001801947700002401965700003001989700002702019700002702046700001702073700002602090700002102116700002702137700002402164700003502188700002502223700002802248700002702276700002102303700001802324700002002342700002902362700001602391700001802407700002002425700002002445700001902465700002402484700001902508700002302527700002202550700002402572700001702596700002202613700002102635700002302656700002702679700002202706700001902728700001802747700001802765700001802783700002102801700002002822700002002842700002502862700002102887700001902908700002802927700002502955700001802980700001902998700002203017700002903039700002403068700002603092700002203118700001603140700002403156700002803180700002103208700002203229700002603251700001903277700002703296700001903323700002103342700002303363700002203386700001603408700002703424700001803451700001703469700001503486700001903501700002203520700002003542700002103562700002703583700002303610700002403633700001803657700002103675700002403696700002703720700001803747700002503765700002103790700002103811700003203832700002503864700002203889700002203911700001803933700002503951700001903976700002103995700002004016700002004036700002104056700002204077700002904099700002204128700002304150700003204173700002404205700002904229700002304258700001804281700002104299700002504320700001804345700002004363700001804383700002304401856011704424       2021                        eng d  a2050-084X00aInsights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV20 aInsights from a pan India Seroepidemiological survey PhenomeIndi  cAPR   ae665370 v103 a<p>To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, &lt;0.0001), use of public transport (1.79, 1.43-2.24, &lt;0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, &lt;0.0001), and B blood group (1.36, 1.15-1.61, 0.001).</p>1 aNaushin, Salwa1 aSardana, Viren1 aUjjainiya, Rajat1 aBhatheja, Nitin1 aKutum, Rintu1 aBhaskar, Akash, Kumar1 aPradhan, Shalini1 aPrakash, Satyartha1 aKhan, Raju1 aRawat, Birendra, Singh1 aTallapaka, Karthik, Bharadwaj1 aAnumalla, Mahesh1 aChandak, Giriraj, Ratan1 aLahiri, Amit1 aKar, Susanta1 aMulay, Shrikant, Ramesh1 aMugale, Madhav, Nilakanth1 aSrivastava, Mrigank1 aKhan, Shaziya1 aSrivastava, Anjali1 aTomar, Bhawana1 aVeerapandian, Murugan1 aVenkatachalam, Ganesh1 aVijayakumar, Selvamani, Raja1 aAgarwal, Ajay1 aGupta, Dinesh1 aHalami, Prakash, M.1 aPeddha, Muthukumar, Serva1 aSundaram, Gopinath, M.1 aVeeranna, Ravindra, P.1 aPal, Anirban1 aAgarwal, Vinay, Kumar1 aMaurya, Anil, Ku1 aSingh, Ranvijay, Kumar1 aRaman, Ashok, Kumar1 aAnandasadagopan, Suresh, Kumar1 aKaruppanan, Parimala1 aVenkatesan, Subramanian1 aSardana, Harish, Kumar1 aKothari, Anamika1 aJain, Rishabh1 aThakur, Anupama1 aParihar, Devendra, Singh1 aSaifi, Anas1 aKaur, Jasleen1 aKumar, Virendra1 aMishra, Avinash1 aGogeri, Iranna1 aRayasam, Geethavani1 aSingh, Praveen1 aChakraborty, Rahul1 aChaturvedi, Gaura1 aKarunakar, Pinreddy1 aYadav, Rohit1 aSinghmar, Sunanda1 aSingh, Dayanidhi1 aSarkar, Sharmistha1 aBhattacharya, Purbasha1 aAcharya, Sundaram1 aSingh, Vandana1 aVerma, Shweta1 aSoni, Drishti1 aSeth, Surabhi1 aVashisht, Sakshi1 aThakran, Sarita1 aFatima, Firdaus1 aSingh, Akash, Pratap1 aSharma, Akanksha1 aSharma, Babita1 aSubramanian, Manikandan1 aPadwad, Yogendra, S.1 aHallan, Vipin1 aPatial, Vikram1 aSingh, Damanpreet1 aTripude, Narendra, Vijay1 aChakrabarti, Partha1 aMaity, Sujay, Krishna1 aGanguly, Dipyaman1 aSarkar, Jit1 aRamakrishna, Sistla1 aKumar, Balthu, Narender1 aKumar, Kiran, A.1 aGandhi, Sumit, G.1 aJamwal, Piyush, Singh1 aChouhan, Rekha1 aJamwal, Vijay, Lakshmi1 aKapoor, Nitika1 aGhosh, Debashish1 aThakkar, Ghanshyam1 aSubudhi, Umakanta1 aSen, Pradip1 aChaudhury, Saumya, Ray1 aKumar, Rashmi1 aGupta, Pawan1 aTuli, Amit1 aSharma, Deepak1 aRinge, Rajesh, P.1 aAmarnarayan, D.1 aKulkarni, Mahesh1 aShanmugam, Dhansekaran1 aDharne, Mahesh, S.1 aDastager, Sayed, G.1 aJoshi, Rakesh1 aPatil, Amita, P.1 aMahajan, Sachin, N.1 aKhan, Abujunaid, Habib1 aWagh, Vasudev1 aYadav, Rakesh, Kumar1 aKhilari, Ajinkya1 aBhadange, Mayuri1 aChaurasiya, Arvindkumar, H.1 aKulsange, Shabda, E.1 aKhairnar, Krishna1 aParanjape, Shilpa1 aKalita, Jatin1 aSastry, Narahari, G.1 aPhukan, Tridip1 aManna, Prasenjit1 aRomi, Wahengbam1 aBharali, Pankaj1 aOzah, Dibyajyoti1 aSahu, Ravi, Kumar1 aBabu, Elapavalooru, Vssk1 aSukumaran, Rajeev1 aNair, Aiswarya, R.1 aValappil, Prajeesh, Kooloth1 aPuthiyamadam, Anoop1 aVelayudhanpillai, Adarsh1 aChodankar, Kalpana1 aDamare, Samir1 aMadhavi, Yennapu1 aAggarwal, Ved, Varun1 aDahiya, Sumit1 aAgrawal, Anurag1 aDash, Debasis1 aSengupta, Shantanu  uhttps://library.ncl.res.in/content/insights-pan-india-sero-epidemiological-survey-phenome-india-cohort-sars-cov207622nas a2201957   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      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