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3. Qualitative Analysis of RLC Circuit Described by Hilfer Derivative with Numerical Treatment Using the Lagrange Polynomial Method

Author

Naveen S., Parthiban V., and Mohamed I. Abbas

Subjects
fixed point theorem, fractional order integro-differential RLC circuit, Hilfer fractional derivative, non-local boundary conditions, Thermodynamics, QC310.15-319, Mathematics, QA1-939, Analysis, QA299.6-433
Abstract

This paper delves into an examination of the existence, uniqueness, and stability properties of a non-local integro-differential equation featuring the Hilfer fractional derivative with order ω∈(1,2) for the RLC model. Based on Schaefer’s fixed point theorem and Banach’s contraction principle, the existence and uniqueness results are established. Furthermore, Ulam–Hyers and Ulam–Hyers–Rassias stability results for the boundary value problem of the RLC model are discussed. To showcase the practicality and efficacy of our theoretical findings, a two-step Lagrange polynomial interpolation method is applied to solve some numerical examples.

Published
2023
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4. First Report of Clonostachys rosea as a Mycoparasite on Sclerotinia sclerotiorum Causing Head Rot of Cabbage in India

Author

Ruppavalli M. Venkatesan, Karthikeyan Muthusamy, Johnson Iruthayasamy, Balakrishnan Prithiviraj, Parthiban V. Kumaresan, Pugalendhi Lakshmanan, and Irene Vethamoni Perianadar

Subjects
biological control, mycoparasitic fungus, sclerotia, SEM, sporodochia, Botany, QK1-989
Abstract

Clonostachys rosea, an ascomycetous, omnipresent, cellulose-decaying soil fungus, has been reported to be a well-known mycoparasitic biological control agent. In this study, we isolated C. rosea, a mycoparasitic fungus for the first time in India from sclerotia of the notorious plant pathogen Sclerotinia sclerotiorum, causing head rot disease in cabbage. A total of five mycoparasitic fungi were isolated from the sclerotial bodies of S. sclerotiorum (TNAU-CR 01, 02, 03, 04 and 05). All the isolates were tested under morpho-molecular characterization. Among them, TNAU-CR 02 showed the greatest mycelial inhibition of 79.63% over the control. Similarly, the SEM imaging of effective C. rosea isolates indicated the presence of numerous conidia destroying the outer cortex layers of sclerotia. Metabolite fingerprinting of C. rosea TNAU-CR 02 identified 18 chemical compounds using GC-MS analysis. The crude antibiotics of C. rosea TNAU-CR 02 were verified for their antifungal activity against S. sclerotiorum and the results revealed 97.17% mycelial inhibition compared with the control. Similarly, foliar application of TNAU-CR 02 at 5 mL/litre on 30, 45 and 60 days after transplanting showed the lowest disease incidence of 15.1 PDI compared to the control. This discovery expands our understanding of the biology and the dissemination of C. rosea, providing a way for the exploitation of C. rosea against cabbage head rot pathogens.

Published
2023
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11. Existence and Stability Results of Non-local Integro-Differential Boundary Value Problems with Hilfer Derivative

Author

Naveen S and Parthiban V

Abstract

The existence and stability results of nonlocal integro-differential boundary value problemswith Hilfer derivative of order ω ∈ (1, 2) have been investigated. A new definition of thefractional derivative was introduced by R. Hilfer. He named it as the left-sided (right-sided)generalized Riemann-Liouville (R-L) derivative of order ω ∈ (0, 1) and a type τ ∈ [0, 1]. Manyauthors call it the Hilfer derivative. Such derivative interpolates between the R-L and Caputoderivative. The fixed point theorems of Banach and Schaefer are two commonly used fixedpoint theorems that are applied to our results. To demonstrate the effectiveness and applicabilityof our theoretical conclusions and two-step Lagrange polynomial interpolation were usedto solve three numerical results and their application is used in the last part.

Published
2022

13. Analysis On the Progression of COVID with the Impact of Climatic Conditions

Author

Swetha, G., Parthiban, V., Chelliah, D. Shanti, and Subramanian, D. Padma

Subjects
Machine Learning algorithms, COVID
Abstract

Globally since December 2019 Coronavirus disease (COVID- 19) has been identified as a potentially severe contagious disease and WHO has declared the spread of COVID-19 as pandemic. Hence, Study of COVID-19 is most essential for prevention and proper treatment of the disease. COVID is a contagious disease in mammals and birds. In humans, coronavirus causes respiratory track infections that can be mild, such as some cases of the common cold, and the others can be lethal, such as SARS, MERS and COVID-19. Machine learning is a powerful technique which is used to train computer programs involving big data to take automated decisions. It is proposed to develop an efficient machine learning algorithm for analysis of progression of COVID-19 with the impact of climatic conditions such as temperature rainfall and humidity. Feature selection has several objectives such as enhancing model performance by avoiding overfitting in the case of supervised classification. Thus EDA is performed for a large set of data that is fed for different algorithms in order to arrive at efficient and lesser RMSE values.

Published
2021

14. Large-scale discovery of novel genetic causes of developmental disorders

Author

Fitzgerald, T. W., Gerety, S. S., Jones, W. D., van Kogelenberg, M., King, D. A., McRae, J., Morley, K. I., Parthiban, V., Al-Turki, S., Ambridge, K., Barrett, D. M., Bayzetinova, T., Clayton, S., Coomber, E. L., Gribble, S., Jones, P., Krishnappa, N., Mason, L. E., Middleton, A., Miller, R., Prigmore, E., Rajan, D., Sifrim, A., Tivey, A. R., Ahmed, M., Akawi, N., Andrews, R., Anjum, U., Archer, H., Armstrong, R., Balasubramanian, M., Banerjee, R., Baralle, D., Batstone, P., Baty, D., Bennett, C., Berg, J., Bernhard, B., Bevan, A. P., Blair, E., Blyth, M., Bohanna, D., Bourdon, L., Bourn, D., Brady, A., Bragin, E., Brewer, C., Brueton, L., Brunstrom, K., Bumpstead, S. J., Bunyan, D. J., Burn, J., Burton, J., Canham, N., Castle, B., Chandler, K., Clasper, S., Clayton-Smith, J., Cole, T., Collins, A., Collinson, M. N., Connell, F., Cooper, N., Cox, H., Cresswell, L., Cross, G., Crow, Y., DʼAlessandro, M., Dabir, T., Davidson, R., Davies, S., Dean, J., Deshpande, C., Devlin, G., Dixit, A., Dominiczak, A., Donnelly, C., Donnelly, D., Douglas, A., Duncan, A., Eason, J., Edkins, S., Ellard, S., Ellis, P., Elmslie, F., Evans, K., Everest, S., Fendick, T., Fisher, R., Flinter, F., Foulds, N., Fryer, A., Fu, B., Gardiner, C., Gaunt, L., Ghali, N., Gibbons, R., Pereira, Gomes S. L., Goodship, J., Goudie, D., Gray, E., Greene, P., Greenhalgh, L., Harrison, L., Hawkins, R., Hellens, S., Henderson, A., Hobson, E., Holden, S., Holder, S., Hollingsworth, G., Homfray, T., Humphreys, M., Hurst, J., Ingram, S., Irving, M., Jarvis, J., Jenkins, L., Johnson, D., Jones, D., Jones, E., Josifova, D., Joss, S., Kaemba, B., Kazembe, S., Kerr, B., Kini, U., Kinning, E., Kirby, G., Kirk, C., Kivuva, E., Kraus, A., Kumar, D., Lachlan, K., Lam, W., Lampe, A., Langman, C., Lees, M., Lim, D., Lowther, G., Lynch, S. A., Magee, A., Maher, E., Mansour, S., Marks, K., Martin, K., Maye, U., McCann, E., McConnell, V., McEntagart, M., McGowan, R., McKay, K., McKee, S., McMullan, D. J., McNerlan, S., Mehta, S., Metcalfe, K., Miles, E., Mohammed, S., Montgomery, T., Moore, D., Morgan, S., Morris, A., Morton, J., Mugalaasi, H., Murday, V., Nevitt, L., Newbury-Ecob, R., Norman, A., OʼShea, R., Ogilvie, C., Park, S., Parker, M. J., Patel, C., Paterson, J., Payne, S., Phipps, J., Pilz, D. T., Porteous, D., Pratt, N., Prescott, K., Price, S., Pridham, A., Procter, A., Purnell, H., Ragge, N., Rankin, J., Raymond, L., Rice, D., Robert, L., Roberts, E., Roberts, G., Roberts, J., Roberts, P., Ross, A., Rosser, E., Saggar, A., Samant, S., Sandford, R., Sarkar, A., Schweiger, S., Scott, C., Scott, R., Selby, A., Seller, A., Sequeira, C., Shannon, N., Sharif, S., Shaw-Smith, C., Shearing, E., Shears, D., Simonic, I., Simpkin, D., Singzon, R., Skitt, Z., Smith, A., Smith, B., Smith, K., Smithson, S., Sneddon, L., Splitt, M., Squires, M., Stewart, F., Stewart, H., Suri, M., Sutton, V., Swaminathan, G. J., Sweeney, E., Tatton-Brown, K., Taylor, C., Taylor, R., Tein, M., Temple, I. K., Thomson, J., Tolmie, J., Torokwa, A., Treacy, B., Turner, C., Turnpenny, P., Tysoe, C., Vandersteen, A., Vasudevan, P., Vogt, J., Wakeling, E., Walker, D., Waters, J., Weber, A., Wellesley, D., Whiteford, M., Widaa, S., Wilcox, S., Williams, D., Williams, N., Woods, G., Wragg, C., Wright, M., Yang, F., Yau, M., Carter, N. P., Parker, M., Firth, H. V., FitzPatrick, D. R., Wright, C. F., Barrett, J. C., and Hurles, M. E.

Published
2015
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15. Brick Manufacturing Using Foundry Sand as Partial Replacement for Fly-ash

Author

Parthiban, V., Gowtham, K., Pramoth Keerthan, M., Santhosh Kumar, K., Parthiban, V., Gowtham, K., Pramoth Keerthan, M., and Santhosh Kumar, K.

Abstract

The foundry is an industrial sector where various iron, scrap steel, and ferroalloys are melted down in arc furnaces or cupolas, shaped in sand, ceramic, or metal moulds, and the cast, steel, nodular, and tempered foundry products needed in industry are produced as raw or processed materials. Especially in establishments such as factories and workshops that produce parts of the automotive, construction, and machine and in steel industry, foundry sand is used to mould foundry products (iron-steel industry and aluminium- and copper-based alloys).Foundry sand is used to prepare metal foundry moulds. For 1 ton of production, 4-5 tons of sand is required. This ratio may be changed based on the type of the metal that needs to be casted, part size, and moulding technique. Sands that contain more than 90% of silica and 7–15% clay (bentonite or kaolinite clay) and have a sintering temperature of over 1500°C are defined as foundry sands. Foundry sand disposal is a herculean task for the industrial sector in today’s scenario. In order to overcome this problem to some extent, it is required to convert it into some useful products. Hence, this project gains its importance for the effective utilization of foundry sand into foundry sand bricks.

Published
2021

16. Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data

Author

Aitken, Stuart, primary, Firth, Helen V., additional, McRae, Jeremy, additional, Halachev, Mihail, additional, Kini, Usha, additional, Parker, Michael J., additional, Lees, Melissa M., additional, Lachlan, Katherine, additional, Sarkar, Ajoy, additional, Joss, Shelagh, additional, Splitt, Miranda, additional, McKee, Shane, additional, Németh, Andrea H., additional, Scott, Richard H., additional, Wright, Caroline F., additional, Marsh, Joseph A., additional, Hurles, Matthew E., additional, FitzPatrick, David R., additional, Fitzgerald, T.W., additional, Gerety, S.S., additional, Jones, W.D., additional, van Kogelenberg, M., additional, King, D.A., additional, McRae, J., additional, Morley, K.I., additional, Parthiban, V., additional, Al-Turki, S., additional, Ambridge, K., additional, Barrett, D.M., additional, Bayzetinova, T., additional, Clayton, S., additional, Coomber, E.L., additional, Gribble, S., additional, Jones, P., additional, Krishnappa, N., additional, Mason, L.E., additional, Middleton, A., additional, Miller, R., additional, Prigmore, E., additional, Rajan, D., additional, Sifrim, A., additional, Tivey, A.R., additional, Ahmed, M., additional, Akawi, N., additional, Andrews, R., additional, Anjum, U., additional, Archer, H., additional, Armstrong, R., additional, Balasubramanian, M., additional, Banerjee, R., additional, Barelle, D., additional, Batstone, P., additional, Baty, D., additional, Bennett, C., additional, Berg, J., additional, Bernhard, B., additional, Bevan, A.P., additional, Blair, E., additional, Blyth, M., additional, Bohanna, D., additional, Bourdon, L., additional, Bourn, D., additional, Brady, A., additional, Bragin, E., additional, Brewer, C., additional, Brueton, L., additional, Brunstrom, K., additional, Bumpstead, S.J., additional, Bunyan, D.J., additional, Burn, J., additional, Burton, J., additional, Canham, N., additional, Castle, B., additional, Chandler, K., additional, Clasper, S., additional, Clayton-Smith, J., additional, Cole, T., additional, Collins, A., additional, Collinson, M.N., additional, Connell, F., additional, Cooper, N., additional, Cox, H., additional, Cresswell, L., additional, Cross, G., additional, Crow, Y., additional, D’Alessandro, P.M., additional, Dabir, T., additional, Davidson, R., additional, Davies, S., additional, Dean, J., additional, Deshpande, C., additional, Devlin, G., additional, Dixit, A., additional, Dominiczak, A., additional, Donnelly, C., additional, Donnelly, D., additional, Douglas, A., additional, Duncan, A., additional, Eason, J., additional, Edkins, S., additional, Ellard, S., additional, Ellis, P., additional, Elmslie, F., additional, Evans, K., additional, Everest, S., additional, Fendick, T., additional, Fisher, R., additional, Flinter, F., additional, Foulds, N., additional, Fryer, A., additional, Fu, B., additional, Gardiner, C., additional, Gaunt, L., additional, Ghali, N., additional, Gibbons, R., additional, Pereira, S.L. Gomes, additional, Goodship, J., additional, Goudie, D., additional, Gray, E., additional, Greene, P., additional, Greenhalgh, L., additional, Harrison, L., additional, Hawkins, R., additional, Hellens, S., additional, Henderson, A., additional, Hobson, E., additional, Holden, S., additional, Holder, S., additional, Hollingsworth, G., additional, Homfray, T., additional, Humphreys, M., additional, Hurst, J., additional, Ingram, S., additional, Irving, M., additional, Jarvis, J., additional, Jenkins, L., additional, Johnson, D., additional, Jones, D., additional, Jones, E., additional, Josifova, D., additional, Joss, S., additional, Kaemba, B., additional, Kazembe, S., additional, Kerr, B., additional, Kini, U., additional, Kinning, E., additional, Kirby, G., additional, Kirk, C., additional, Kivuva, E., additional, Kraus, A., additional, Kumar, D., additional, Lachlan, K., additional, Lam, W., additional, Lampe, A., additional, Langman, C., additional, Lees, M., additional, Lim, D., additional, Lowther, G., additional, Lynch, S.A., additional, Magee, A., additional, Maher, E., additional, Mansour, S., additional, Marks, K., additional, Martin, K., additional, Maye, U., additional, McCann, E., additional, McConnell, V., additional, McEntagart, M., additional, McGowan, R., additional, McKay, K., additional, McKee, S., additional, McMullan, D.J., additional, McNerlan, S., additional, Mehta, S., additional, Metcalfe, K., additional, Miles, E., additional, Mohammed, S., additional, Montgomery, T., additional, Moore, D., additional, Morgan, S., additional, Morris, A., additional, Morton, J., additional, Mugalaasi, H., additional, Murday, V., additional, Nevitt, L., additional, Newbury-Ecob, R., additional, Norman, A., additional, O’Shea, R., additional, Ogilvie, C., additional, Park, S., additional, Parker, M.J., additional, Patel, C., additional, Paterson, J., additional, Payne, S., additional, Phipps, J., additional, Pilz, D.T., additional, Porteous, D., additional, Pratt, N., additional, Prescott, K., additional, Price, S., additional, Pridham, A., additional, Proctor, A., additional, Purnell, H., additional, Ragge, N., additional, Rankin, J., additional, Raymond, L., additional, Rice, D., additional, Robert, L., additional, Roberts, E., additional, Roberts, G., additional, Roberts, J., additional, Roberts, P., additional, Ross, A., additional, Rosser, E., additional, Saggar, A., additional, Samant, S., additional, Sandford, R., additional, Sarkar, A., additional, Schweiger, S., additional, Scott, C., additional, Scott, R., additional, Selby, A., additional, Seller, A., additional, Sequeira, C., additional, Shannon, N., additional, Sharif, S., additional, Shaw-Smith, C., additional, Shearing, E., additional, Shears, D., additional, Simonic, I., additional, Simpkin, D., additional, Singzon, R., additional, Skitt, Z., additional, Smith, A., additional, Smith, B., additional, Smith, K., additional, Smithson, S., additional, Sneddon, L., additional, Splitt, M., additional, Squires, M., additional, Stewart, F., additional, Stewart, H., additional, Suri, M., additional, Sutton, V., additional, Swaminathan, G.J., additional, Sweeney, E., additional, Tatton-Brown, K., additional, Taylor, C., additional, Taylor, R., additional, Tein, M., additional, Temple, I.K., additional, Thomson, J., additional, Tolmie, J., additional, Torokwa, A., additional, Treacy, B., additional, Turner, C., additional, Turnpenny, P., additional, Tysoe, C., additional, Vandersteen, A., additional, Vasudevan, P., additional, Vogt, J., additional, Wakeling, E., additional, Walker, D., additional, Waters, J., additional, Weber, A., additional, Wellesley, D., additional, Whiteford, M., additional, Widaa, S., additional, Wilcox, S., additional, Williams, D., additional, Williams, N., additional, Woods, G., additional, Wragg, C., additional, Wright, M., additional, Yang, F., additional, Yau, M., additional, Carter, N.P., additional, Parker, M., additional, Firth, H.V., additional, FitzPatrick, D.R., additional, Wright, C.F., additional, Barrett, J.C., additional, and Hurles, M.E., additional

Published
2019
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19. Large-scale discovery of novel genetic causes of developmental disorders

Author

Fitzgerald, TW, Gerety, SS, Jones, WD, van Kogelenberg, M, King, DA, McRae, J, Morley, KI, Parthiban, V, Al-Turki, S, Ambridge, K, Barrett, DM, Bayzetinova, T, Clayton, S, Coomber, EL, Gribble, S, Jones, P, Krishnappa, N, Mason, LE, Middleton, A, Miller, R, Prigmore, E, Rajan, D, Sifrim, A, Tivey, AR, Ahmed, M, Akawi, N, Andrews, R, Anjum, U, Archer, H, Armstrong, R, Balasubramanian, M, Banerjee, R, Baralle, D, Batstone, P, Baty, D, Bennett, C, Berg, J, Bernhard, B, Bevan, AP, Blair, E, Blyth, M, Bohanna, D, Bourdon, L, Bourn, D, Brady, A, Bragin, E, Brewer, C, Brueton, L, Brunstrom, K, Bumpstead, SJ, Bunyan, DJ, Burn, J, Burton, J, Canham, N, Castle, B, Chandler, K, Clasper, S, Clayton-Smith, J, Cole, T, Collins, A, Collinson, MN, Connell, F, Cooper, N, Cox, H, Cresswell, L, Cross, G, Crow, Y, D'Alessandro, M, Dabir, T, Davidson, R, Davies, S, Dean, J, Deshpande, C, Devlin, G, Dixit, A, Dominiczak, A, Donnelly, C, Donnelly, D, Douglas, A, Duncan, A, Eason, J, Edkins, S, Ellard, S, Ellis, P, Elmslie, F, Evans, K, Everest, S, Fendick, T, Fisher, R, Flinter, F, Foulds, N, Fryer, A, Fu, B, Gardiner, C, Gaunt, L, Ghali, N, Gibbons, R, Pereira, SLG, Goodship, J, Goudie, D, Gray, E, Greene, P, Greenhalgh, L, Harrison, L, Hawkins, R, Hellens, S, Henderson, A, Hobson, E, Holden, S, Holder, S, Hollingsworth, G, Homfray, T, Humphreys, M, Hurst, J, Ingram, S, Irving, M, Jarvis, J, Jenkins, L, Johnson, D, Jones, D, Jones, E, Josifova, D, Joss, S, Kaemba, B, Kazembe, S, Kerr, B, Kini, U, Kinning, E, Kirby, G, Kirk, C, Kivuva, E, Kraus, A, Kumar, D, Lachlan, K, Lam, W, Lampe, A, Langman, C, Lees, M, Lim, D, Lowther, G, Lynch, SA, Magee, A, Maher, E, Mansour, S, Marks, K, Martin, K, Maye, U, McCann, E, McConnell, V, McEntagart, M, McGowan, R, McKay, K, McKee, S, McMullan, DJ, McNerlan, S, Mehta, S, Metcalfe, K, Miles, E, Mohammed, S, Montgomery, T, Moore, D, Morgan, S, Morris, A, Morton, J, Mugalaasi, H, Murday, V, Nevitt, L, Newbury-Ecob, R, Norman, A, O'Shea, R, Ogilvie, C, Park, S, Parker, MJ, Patel, C, Paterson, J, Payne, S, Phipps, J, Pilz, DT, Porteous, D, Pratt, N, Prescott, K, Price, S, Pridham, A, Procter, A, Purnell, H, Ragge, N, Rankin, J, Raymond, L, Rice, D, Robert, L, Roberts, E, Roberts, G, Roberts, J, Roberts, P, Ross, A, Rosser, E, Saggar, A, Samant, S, Sandford, R, Sarkar, A, Schweier, S, Scott, C, Scott, R, Selby, A, Seller, A, Sequeira, C, Shannon, N, Shanrif, S, Shaw-Smith, C, Shearing, E, Shears, D, Simonic, I, Simpkin, D, Singzon, R, Skitt, Z, Smith, A, Smith, B, Smith, K, Smithson, S, Sneddon, L, Splitt, M, Squires, M, Stewart, F, Stewart, H, Suri, M, Sutton, V, Swaminathan, GJ, Sweeney, E, Tatton-Brown, K, Taylor, C, Taylor, R, Tein, M, Temple, IK, Thomson, J, Tolmie, J, Torokwa, A, Treacy, B, Turner, C, Turnpenny, P, Tysoe, C, Vandersteen, A, Vasudevan, P, Vogt, J, Wakeling, E, Walker, D, Waters, J, Weber, A, Wellesley, D, Whiteford, M, Widaa, S, Wilcox, S, Williams, D, Williams, N, Woods, G, Wragg, C, Wright, M, Yang, F, Yau, M, Carter, NP, Parker, M, Firth, HV, FitzPatrick, DR, Wright, CF, Barrett, JC, Hurles, ME, Fitzgerald, TW, Gerety, SS, Jones, WD, van Kogelenberg, M, King, DA, McRae, J, Morley, KI, Parthiban, V, Al-Turki, S, Ambridge, K, Barrett, DM, Bayzetinova, T, Clayton, S, Coomber, EL, Gribble, S, Jones, P, Krishnappa, N, Mason, LE, Middleton, A, Miller, R, Prigmore, E, Rajan, D, Sifrim, A, Tivey, AR, Ahmed, M, Akawi, N, Andrews, R, Anjum, U, Archer, H, Armstrong, R, Balasubramanian, M, Banerjee, R, Baralle, D, Batstone, P, Baty, D, Bennett, C, Berg, J, Bernhard, B, Bevan, AP, Blair, E, Blyth, M, Bohanna, D, Bourdon, L, Bourn, D, Brady, A, Bragin, E, Brewer, C, Brueton, L, Brunstrom, K, Bumpstead, SJ, Bunyan, DJ, Burn, J, Burton, J, Canham, N, Castle, B, Chandler, K, Clasper, S, Clayton-Smith, J, Cole, T, Collins, A, Collinson, MN, Connell, F, Cooper, N, Cox, H, Cresswell, L, Cross, G, Crow, Y, D'Alessandro, M, Dabir, T, Davidson, R, Davies, S, Dean, J, Deshpande, C, Devlin, G, Dixit, A, Dominiczak, A, Donnelly, C, Donnelly, D, Douglas, A, Duncan, A, Eason, J, Edkins, S, Ellard, S, Ellis, P, Elmslie, F, Evans, K, Everest, S, Fendick, T, Fisher, R, Flinter, F, Foulds, N, Fryer, A, Fu, B, Gardiner, C, Gaunt, L, Ghali, N, Gibbons, R, Pereira, SLG, Goodship, J, Goudie, D, Gray, E, Greene, P, Greenhalgh, L, Harrison, L, Hawkins, R, Hellens, S, Henderson, A, Hobson, E, Holden, S, Holder, S, Hollingsworth, G, Homfray, T, Humphreys, M, Hurst, J, Ingram, S, Irving, M, Jarvis, J, Jenkins, L, Johnson, D, Jones, D, Jones, E, Josifova, D, Joss, S, Kaemba, B, Kazembe, S, Kerr, B, Kini, U, Kinning, E, Kirby, G, Kirk, C, Kivuva, E, Kraus, A, Kumar, D, Lachlan, K, Lam, W, Lampe, A, Langman, C, Lees, M, Lim, D, Lowther, G, Lynch, SA, Magee, A, Maher, E, Mansour, S, Marks, K, Martin, K, Maye, U, McCann, E, McConnell, V, McEntagart, M, McGowan, R, McKay, K, McKee, S, McMullan, DJ, McNerlan, S, Mehta, S, Metcalfe, K, Miles, E, Mohammed, S, Montgomery, T, Moore, D, Morgan, S, Morris, A, Morton, J, Mugalaasi, H, Murday, V, Nevitt, L, Newbury-Ecob, R, Norman, A, O'Shea, R, Ogilvie, C, Park, S, Parker, MJ, Patel, C, Paterson, J, Payne, S, Phipps, J, Pilz, DT, Porteous, D, Pratt, N, Prescott, K, Price, S, Pridham, A, Procter, A, Purnell, H, Ragge, N, Rankin, J, Raymond, L, Rice, D, Robert, L, Roberts, E, Roberts, G, Roberts, J, Roberts, P, Ross, A, Rosser, E, Saggar, A, Samant, S, Sandford, R, Sarkar, A, Schweier, S, Scott, C, Scott, R, Selby, A, Seller, A, Sequeira, C, Shannon, N, Shanrif, S, Shaw-Smith, C, Shearing, E, Shears, D, Simonic, I, Simpkin, D, Singzon, R, Skitt, Z, Smith, A, Smith, B, Smith, K, Smithson, S, Sneddon, L, Splitt, M, Squires, M, Stewart, F, Stewart, H, Suri, M, Sutton, V, Swaminathan, GJ, Sweeney, E, Tatton-Brown, K, Taylor, C, Taylor, R, Tein, M, Temple, IK, Thomson, J, Tolmie, J, Torokwa, A, Treacy, B, Turner, C, Turnpenny, P, Tysoe, C, Vandersteen, A, Vasudevan, P, Vogt, J, Wakeling, E, Walker, D, Waters, J, Weber, A, Wellesley, D, Whiteford, M, Widaa, S, Wilcox, S, Williams, D, Williams, N, Woods, G, Wragg, C, Wright, M, Yang, F, Yau, M, Carter, NP, Parker, M, Firth, HV, FitzPatrick, DR, Wright, CF, Barrett, JC, and Hurles, ME

Abstract

Despite three decades of successful, predominantly phenotype-driven discovery of the genetic causes of monogenic disorders, up to half of children with severe developmental disorders of probable genetic origin remain without a genetic diagnosis. Particularly challenging are those disorders rare enough to have eluded recognition as a discrete clinical entity, those with highly variable clinical manifestations, and those that are difficult to distinguish from other, very similar, disorders. Here we demonstrate the power of using an unbiased genotype-driven approach to identify subsets of patients with similar disorders. By studying 1,133 children with severe, undiagnosed developmental disorders, and their parents, using a combination of exome sequencing and array-based detection of chromosomal rearrangements, we discovered 12 novel genes associated with developmental disorders. These newly implicated genes increase by 10% (from 28% to 31%) the proportion of children that could be diagnosed. Clustering of missense mutations in six of these newly implicated genes suggests that normal development is being perturbed by an activating or dominant-negative mechanism. Our findings demonstrate the value of adopting a comprehensive strategy, both genome-wide and nationwide, to elucidate the underlying causes of rare genetic disorders.

Published
2015

23. Solutions of System of Fractional Partial Differential Equations.

Author

Parthiban, V. and Balachandran, K.

Subjects
*PARTIAL differential equations, *MATHEMATICAL decomposition, *DIFFERENTIAL equations, *STOCHASTIC convergence, *CALCULUS
Abstract

In this paper, system of fractional partial differential equation which has numerous applications in many fields of science is considered. Adomian decomposition method, a novel method is used to solve these type of equations. The solutions are derived in convergent series form which shows the effectiveness of the method for solving wide variety of fractional differential equations. [ABSTRACT FROM AUTHOR]

Published
2013

26. Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development.

Author

Parthiban V, Gromiha MM, Abhinandan M, and Schomburg D

Subjects
Algorithms, Normal Distribution, Protein Conformation, Protein Folding, Protein Structure, Secondary, Reproducibility of Results, Computational Biology, Models, Molecular, Point Mutation, Proteins chemistry, Proteins genetics
Abstract

Background: Understanding and predicting protein stability upon point mutations has wide-spread importance in molecular biology. Several prediction models have been developed in the past with various algorithms. Statistical potentials are one of the widely used algorithms for the prediction of changes in stability upon point mutations. Although the methods provide flexibility and the capability to develop an accurate and reliable prediction model, it can be achieved only by the right selection of the structural factors and optimization of their parameters for the statistical potentials. In this work, we have selected five atom classification systems and compared their efficiency for the development of amino acid atom potentials. Additionally, torsion angle potentials have been optimized to include the orientation of amino acids in such a way that altered backbone conformation in different secondary structural regions can be included for the prediction model. This study also elaborates the importance of classifying the mutations according to their solvent accessibility and secondary structure specificity. The prediction efficiency has been calculated individually for the mutations in different secondary structural regions and compared., Results: Results show that, in addition to using an advanced atom description, stepwise regression and selection of atoms are necessary to avoid the redundancy in atom distribution and improve the reliability of the prediction model validation. Comparing to other atom classification models, Melo-Feytmans model shows better prediction efficiency by giving a high correlation of 0.85 between experimental and theoretical Delta Delta G with 84.06% of the mutations correctly predicted out of 1538 mutations. The theoretical Delta Delta G values for the mutations in partially buried beta-strands generated by the structural training dataset from PISCES gave a correlation of 0.84 without performing the Gaussian apodization of the torsion angle distribution. After the Gaussian apodization, the correlation increased to 0.92 and prediction accuracy increased from 80% to 88.89% respectively., Conclusion: These findings were useful for the optimization of the Melo-Feytmans atom classification system and implementing them to develop the statistical potentials. It was also significant that the prediction efficiency of mutations in the partially buried beta-strands improves with the help of Gaussian apodization of the torsion angle distribution. All these comparisons and optimization techniques demonstrate their advantages as well as the restrictions for the development of the prediction model. These findings will be quite helpful not only for the protein stability prediction, but also for various structure solutions in future.

Published
2007
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