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4. Copy number architectures define treatment-mediated selection of lethal prostate cancer clones

Author

Hasan, A, Cremaschi, P, Wetterskog, D, Jayaram, A, Wong, S, Williams, S, Pasam, A, Trigos, A, Trujillo, B, Grist, E, Friedrich, S, Vainauskas, O, Parry, M, Ismail, M, Devlies, W, Wingate, A, Linch, M, Naceur-Lombardelli, C, Zaccaria, S, Hessey, S, Shiu, K, Bridgewater, J, Hochhauser, D, Forster, M, Lee, S, Ahmad, T, Papadatos-Pastos, D, Janes, S, Van Loo, P, Enfield, K, Mcgranahan, N, Huebner, A, Quezada, S, Beck, S, Parker, P, Enver, T, Hynds, R, Pearce, D, Falzon, M, Proctor, I, Sinclair, R, Lok, C, Rhodes, Z, Moore, D, Marafioti, T, Mitchison, M, Ellery, P, Sivakumar, M, Brandner, S, Rowan, A, Hiley, C, Veeriah, S, Shaw, H, Toncheva, A, Prymas, P, Watkins, T, Bailey, C, Martinez Ruiz, C, Litchfield, K, Al-Bakir, M, Kanu, N, Ward, S, Lim, E, Reading, J, Chain, B, Akay, M, Flanagan, A, Biswas, D, Pich, O, Dietzen, M, Puttick, C, Colliver, E, Magness, A, Angelova, M, Black, J, Lucas, O, Hill, W, Liu, W, Frankell, A, Magno, N, Athanasopoulou, F, Salgado, R, Lee, C, Grigoriadis, K, Al-Sawaf, O, Karasaki, T, Bunkum, A, Noorani, I, Benafif, S, Barbe, V, Bola, S, Leone, G, Alifrangis, C, Mcgovern, U, Thol, K, Gamble, S, Ung, S, Sahwangarrom, T, Marin, C, Pawlik, P, Lam, J, Richard, C, Vendramin, R, Dijkstra, K, Rane, J, Nicod, J, Dwornik, A, Bowles, K, Zaidi, R, Gishen, F, Stone, P, Stirling, C, Turajlic, S, Larkin, J, Pickering, L, Furness, A, Young, K, Drake, W, Edmonds, K, Hunter, N, Mangwende, M, Pearce, K, Grostate, L, Au, L, Spain, L, Shepherd, S, Yan, H, Shum, B, Tippu, Z, Hanley, B, Spencer, C, Emmerich, M, Gerard, C, Schmitt, A, Del Rosario, L, Carlyle, E, Lewis, C, Holt, L, Lucanas, A, O'Flaherty, M, Hazell, S, Mudhar, H, Messiou, C, Latifoltojar, A, Fendler, A, Byrne, F, Pallikonda, H, Lobon, I, Coulton, A, Cattin, A, Deng, D, Feng, H, Yousaf, N, Popat, S, Curtis, O, Milner-Watts, C, Stamp, G, Nye, E, Murra, A, Korteweg, J, Kelly, D, Terry, L, Biano, J, Peat, K, Kelly, K, Grieco, C, Le, M, D'Arienzo, P, Turay, E, Hill, P, Josephs, D, Irshad, S, Spicer, J, Mahadeva, U, Green, A, Stewart, R, Wright, N, Pulman, G, Mitu, R, Phillips-Boyd, S, Enting, D, Rudman, S, Ghosh, S, Karapanagiotou, E, Pintus, E, Tutt, A, Howlett, S, Brenton, J, Caldas, C, Fitzgerald, R, Jimenez-Linan, M, Provenzano, E, Cluroe, A, Paterson, A, Aitken, S, Allinson, K, Stewart, G, Mcdermott, U, Beddowes, E, Maughan, T, Ansorge, O, Campbell, P, Roxburgh, P, Fraser, S, Blyth, K, Le Quesne, J, Krebs, M, Blackhall, F, Summers, Y, Oliveira, P, Ortega-Franco, A, Dive, C, Gomes, F, Carter, M, Dransfield, J, Thomas, A, Fennell, D, Shaw, J, Wilson, C, Marrone, D, Naidu, B, Baijal, S, Tanchel, B, Langman, G, Robinson, A, Collard, M, Cockcroft, P, Ferris, C, Bancroft, H, Kerr, A, Middleton, G, Webb, J, Kadiri, S, Colloby, P, Olisemeke, B, Wilson, R, Shackleford, H, Osman, A, Tomlinson, I, Jogai, S, Holden, S, Fernandes, T, Mcneish, I, Hampton, B, Mckenzie, M, Hackshaw, A, Sharp, A, Chan, K, Farrelly, L, Bridger, H, Leslie, R, Tookman, A, Swanton, C, Jamal-Hanjani, M, Lise, S, Sandhu, S, Attard, G, Hasan A. M. M., Cremaschi P., Wetterskog D., Jayaram A., Wong S. Q., Williams S., Pasam A., Trigos A., Trujillo B., Grist E., Friedrich S., Vainauskas O., Parry M., Ismail M., Devlies W., Wingate A., Linch M., Naceur-Lombardelli C., Zaccaria S., Hessey S., Shiu K. -K., Bridgewater J., Hochhauser D., Forster M., Lee S. -M., Ahmad T., Papadatos-Pastos D., Janes S., Van Loo P., Enfield K., McGranahan N., Huebner A., Quezada S., Beck S., Parker P., Enver T., Hynds R. E., Pearce D. R., Falzon M., Proctor I., Sinclair R., Lok C. -W., Rhodes Z., Moore D., Marafioti T., Mitchison M., Ellery P., Sivakumar M., Brandner S., Rowan A., Hiley C., Veeriah S., Shaw H., Toncheva A., Prymas P., Watkins T. B. K., Bailey C., Martinez Ruiz C., Litchfield K., Al-Bakir M., Kanu N., Ward S., Lim E., Reading J., Chain B., Watkins T., Akay M., Flanagan A., Biswas D., Pich O., Dietzen M., Puttick C., Colliver E., Magness A., Angelova M., Black J., Lucas O., Hill W., Liu W. -K., Frankell A., Magno N., Athanasopoulou F., Salgado R., Lee C., Grigoriadis K., Al-Sawaf O., Karasaki T., Bunkum A., Noorani I., Benafif S., Barbe V., Bola S. K., Leone G., Alifrangis C., McGovern U., Thol K., Gamble S., Ung S. K., Sahwangarrom T., Marin C. P., Wong S., Pawlik P., Lam J. M., Richard C., Vendramin R., Dijkstra K., Rane J., Nicod J., Dwornik A., Bowles K., Zaidi R., Gishen F., Stone P., Stirling C., Turajlic S., Larkin J., Pickering L., Furness A., Young K., Drake W., Edmonds K., Hunter N., Mangwende M., Pearce K., Grostate L., Au L., Spain L., Shepherd S., Yan H., Shum B., Tippu Z., Hanley B., Spencer C., Emmerich M., Gerard C., Schmitt A. M., Del Rosario L., Carlyle E., Lewis C., Holt L., Lucanas A., O'Flaherty M., Hazell S., Mudhar H., Messiou C., Latifoltojar A., Fendler A., Byrne F., Pallikonda H., Lobon I., Coulton A., Cattin A. -L., Deng D., Feng H., Yousaf N., Popat S., Curtis O., Milner-Watts C., Stamp G., Nye E., Murra A., Korteweg J., Kelly D., Terry L., Biano J., Peat K., Kelly K., Grieco C., Le M. L., D'Arienzo P. D., Turay E., Hill P., Josephs D., Irshad S., Spicer J., Mahadeva U., Green A., Stewart R., Wright N., Pulman G., Mitu R., Phillips-Boyd S., Enting D., Rudman S., Ghosh S., Karapanagiotou E., Pintus E., Tutt A., Howlett S., Brenton J., Caldas C., Fitzgerald R., Jimenez-Linan M., Provenzano E., Cluroe A., Paterson A., Aitken S., Allinson K., Stewart G., McDermott U., Beddowes E., Maughan T., Ansorge O., Campbell P., Roxburgh P., Fraser S., Blyth K., Le Quesne J., Krebs M., Blackhall F., Summers Y., Oliveira P., Ortega-Franco A., Dive C., Gomes F., Carter M., Dransfield J., Thomas A., Fennell D., Shaw J., Wilson C., Marrone D., Naidu B., Baijal S., Tanchel B., Langman G., Robinson A., Collard M., Cockcroft P., Ferris C., Bancroft H., Kerr A., Middleton G., Webb J., Kadiri S., Colloby P., Olisemeke B., Wilson R., Shackleford H., Osman A., Tomlinson I., Jogai S., Holden S., Fernandes T., McNeish I., Hampton B., McKenzie M., Hackshaw A., Sharp A., Chan K., Farrelly L., Bridger H., Leslie R., Tookman A., Swanton C., Jamal-Hanjani M., Lise S., Sandhu S., Attard G., Hasan, A, Cremaschi, P, Wetterskog, D, Jayaram, A, Wong, S, Williams, S, Pasam, A, Trigos, A, Trujillo, B, Grist, E, Friedrich, S, Vainauskas, O, Parry, M, Ismail, M, Devlies, W, Wingate, A, Linch, M, Naceur-Lombardelli, C, Zaccaria, S, Hessey, S, Shiu, K, Bridgewater, J, Hochhauser, D, Forster, M, Lee, S, Ahmad, T, Papadatos-Pastos, D, Janes, S, Van Loo, P, Enfield, K, Mcgranahan, N, Huebner, A, Quezada, S, Beck, S, Parker, P, Enver, T, Hynds, R, Pearce, D, Falzon, M, Proctor, I, Sinclair, R, Lok, C, Rhodes, Z, Moore, D, Marafioti, T, Mitchison, M, Ellery, P, Sivakumar, M, Brandner, S, Rowan, A, Hiley, C, Veeriah, S, Shaw, H, Toncheva, A, Prymas, P, Watkins, T, Bailey, C, Martinez Ruiz, C, Litchfield, K, Al-Bakir, M, Kanu, N, Ward, S, Lim, E, Reading, J, Chain, B, Akay, M, Flanagan, A, Biswas, D, Pich, O, Dietzen, M, Puttick, C, Colliver, E, Magness, A, Angelova, M, Black, J, Lucas, O, Hill, W, Liu, W, Frankell, A, Magno, N, Athanasopoulou, F, Salgado, R, Lee, C, Grigoriadis, K, Al-Sawaf, O, Karasaki, T, Bunkum, A, Noorani, I, Benafif, S, Barbe, V, Bola, S, Leone, G, Alifrangis, C, Mcgovern, U, Thol, K, Gamble, S, Ung, S, Sahwangarrom, T, Marin, C, Pawlik, P, Lam, J, Richard, C, Vendramin, R, Dijkstra, K, Rane, J, Nicod, J, Dwornik, A, Bowles, K, Zaidi, R, Gishen, F, Stone, P, Stirling, C, Turajlic, S, Larkin, J, Pickering, L, Furness, A, Young, K, Drake, W, Edmonds, K, Hunter, N, Mangwende, M, Pearce, K, Grostate, L, Au, L, Spain, L, Shepherd, S, Yan, H, Shum, B, Tippu, Z, Hanley, B, Spencer, C, Emmerich, M, Gerard, C, Schmitt, A, Del Rosario, L, Carlyle, E, Lewis, C, Holt, L, Lucanas, A, O'Flaherty, M, Hazell, S, Mudhar, H, Messiou, C, Latifoltojar, A, Fendler, A, Byrne, F, Pallikonda, H, Lobon, I, Coulton, A, Cattin, A, Deng, D, Feng, H, Yousaf, N, Popat, S, Curtis, O, Milner-Watts, C, Stamp, G, Nye, E, Murra, A, Korteweg, J, Kelly, D, Terry, L, Biano, J, Peat, K, Kelly, K, Grieco, C, Le, M, D'Arienzo, P, Turay, E, Hill, P, Josephs, D, Irshad, S, Spicer, J, Mahadeva, U, Green, A, Stewart, R, Wright, N, Pulman, G, Mitu, R, Phillips-Boyd, S, Enting, D, Rudman, S, Ghosh, S, Karapanagiotou, E, Pintus, E, Tutt, A, Howlett, S, Brenton, J, Caldas, C, Fitzgerald, R, Jimenez-Linan, M, Provenzano, E, Cluroe, A, Paterson, A, Aitken, S, Allinson, K, Stewart, G, Mcdermott, U, Beddowes, E, Maughan, T, Ansorge, O, Campbell, P, Roxburgh, P, Fraser, S, Blyth, K, Le Quesne, J, Krebs, M, Blackhall, F, Summers, Y, Oliveira, P, Ortega-Franco, A, Dive, C, Gomes, F, Carter, M, Dransfield, J, Thomas, A, Fennell, D, Shaw, J, Wilson, C, Marrone, D, Naidu, B, Baijal, S, Tanchel, B, Langman, G, Robinson, A, Collard, M, Cockcroft, P, Ferris, C, Bancroft, H, Kerr, A, Middleton, G, Webb, J, Kadiri, S, Colloby, P, Olisemeke, B, Wilson, R, Shackleford, H, Osman, A, Tomlinson, I, Jogai, S, Holden, S, Fernandes, T, Mcneish, I, Hampton, B, Mckenzie, M, Hackshaw, A, Sharp, A, Chan, K, Farrelly, L, Bridger, H, Leslie, R, Tookman, A, Swanton, C, Jamal-Hanjani, M, Lise, S, Sandhu, S, Attard, G, Hasan A. M. M., Cremaschi P., Wetterskog D., Jayaram A., Wong S. Q., Williams S., Pasam A., Trigos A., Trujillo B., Grist E., Friedrich S., Vainauskas O., Parry M., Ismail M., Devlies W., Wingate A., Linch M., Naceur-Lombardelli C., Zaccaria S., Hessey S., Shiu K. -K., Bridgewater J., Hochhauser D., Forster M., Lee S. -M., Ahmad T., Papadatos-Pastos D., Janes S., Van Loo P., Enfield K., McGranahan N., Huebner A., Quezada S., Beck S., Parker P., Enver T., Hynds R. E., Pearce D. R., Falzon M., Proctor I., Sinclair R., Lok C. -W., Rhodes Z., Moore D., Marafioti T., Mitchison M., Ellery P., Sivakumar M., Brandner S., Rowan A., Hiley C., Veeriah S., Shaw H., Toncheva A., Prymas P., Watkins T. B. K., Bailey C., Martinez Ruiz C., Litchfield K., Al-Bakir M., Kanu N., Ward S., Lim E., Reading J., Chain B., Watkins T., Akay M., Flanagan A., Biswas D., Pich O., Dietzen M., Puttick C., Colliver E., Magness A., Angelova M., Black J., Lucas O., Hill W., Liu W. -K., Frankell A., Magno N., Athanasopoulou F., Salgado R., Lee C., Grigoriadis K., Al-Sawaf O., Karasaki T., Bunkum A., Noorani I., Benafif S., Barbe V., Bola S. K., Leone G., Alifrangis C., McGovern U., Thol K., Gamble S., Ung S. K., Sahwangarrom T., Marin C. P., Wong S., Pawlik P., Lam J. M., Richard C., Vendramin R., Dijkstra K., Rane J., Nicod J., Dwornik A., Bowles K., Zaidi R., Gishen F., Stone P., Stirling C., Turajlic S., Larkin J., Pickering L., Furness A., Young K., Drake W., Edmonds K., Hunter N., Mangwende M., Pearce K., Grostate L., Au L., Spain L., Shepherd S., Yan H., Shum B., Tippu Z., Hanley B., Spencer C., Emmerich M., Gerard C., Schmitt A. M., Del Rosario L., Carlyle E., Lewis C., Holt L., Lucanas A., O'Flaherty M., Hazell S., Mudhar H., Messiou C., Latifoltojar A., Fendler A., Byrne F., Pallikonda H., Lobon I., Coulton A., Cattin A. -L., Deng D., Feng H., Yousaf N., Popat S., Curtis O., Milner-Watts C., Stamp G., Nye E., Murra A., Korteweg J., Kelly D., Terry L., Biano J., Peat K., Kelly K., Grieco C., Le M. L., D'Arienzo P. D., Turay E., Hill P., Josephs D., Irshad S., Spicer J., Mahadeva U., Green A., Stewart R., Wright N., Pulman G., Mitu R., Phillips-Boyd S., Enting D., Rudman S., Ghosh S., Karapanagiotou E., Pintus E., Tutt A., Howlett S., Brenton J., Caldas C., Fitzgerald R., Jimenez-Linan M., Provenzano E., Cluroe A., Paterson A., Aitken S., Allinson K., Stewart G., McDermott U., Beddowes E., Maughan T., Ansorge O., Campbell P., Roxburgh P., Fraser S., Blyth K., Le Quesne J., Krebs M., Blackhall F., Summers Y., Oliveira P., Ortega-Franco A., Dive C., Gomes F., Carter M., Dransfield J., Thomas A., Fennell D., Shaw J., Wilson C., Marrone D., Naidu B., Baijal S., Tanchel B., Langman G., Robinson A., Collard M., Cockcroft P., Ferris C., Bancroft H., Kerr A., Middleton G., Webb J., Kadiri S., Colloby P., Olisemeke B., Wilson R., Shackleford H., Osman A., Tomlinson I., Jogai S., Holden S., Fernandes T., McNeish I., Hampton B., McKenzie M., Hackshaw A., Sharp A., Chan K., Farrelly L., Bridger H., Leslie R., Tookman A., Swanton C., Jamal-Hanjani M., Lise S., Sandhu S., and Attard G.

Abstract

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient evolutionary relationships of metastases that evade treatment, we perform genome-wide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 167 metastatic regions from 11 organs harvested post-mortem from 10 men who died from prostate cancer. We identify diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number changes, we confirm a common clone of origin across metastases and diagnostic biopsies, and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. These autosome-defined clusters are characterized by cluster-specific AR gene architectures, and in two index cases are topologically more congruent than by chance (p-values 3.07 × 10−8 and 6.4 × 10−4). Integration with anatomical sites suggests patterns of spread and points of genomic divergence. Here, we show that copy number boundaries identify treatment-selected clones with putatively distinct lethal trajectories.

Published
2023

6. Bitcoin price prediction using LSTM, GRU and hybrid LSTM-GRU with bayesian optimization, random search, and grid search for the next days.

Author

Kervanci, I. Sibel, Akay, M. Fatih, and Özceylan, Eren

Subjects
PRICES, STANDARD deviations, BITCOIN, PRICE fluctuations, INVESTORS
Abstract

Bitcoin has high price fluctuations, which involve high risks and high return rates for investors. These high earnings have attracted the attention of investors. This paper proposes a new model for Bitcoin price prediction that effectively reduces prediction error. Hyperparameter optimization methods such as Bayesian optimization (BO), random search and grid search with Long Short-Term Memory (LSTM), Gated Repetitive Unit (GRU), and hybrid LSTM-GRU utilised. Models with BO achieved better results than others. To improve each model's results with BO; Gradient Incremental Regression Trees (GBRT), Gaussian Process (GP), Random Forest (RF) and Extra Trees (ET) were applied to optimizers and corresponding surrogate functions. Evaluating the effects of hyper-parameter values on the problem for each method contributes to the parameter selection process for similar prediction problems. To increase comparability in the literature, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Square Error (MSE) were used. There is a least one hyper-parameter combination, which produces a result close to the best value for each model when the results obtained from the experiments are interpreted. BO with hybrid LSTM-GRU outperformed all methods in this paper and the examined literature for the value of RMSE, MSE, and MAE. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Determination of the various extraction solvent effects on polyphenolic profile and antioxidant activities of selected tea samples by chemometric approach

Author

Vural, Nilüfer, Algan Cavuldak, Özge, Akay, M. Abdülkadir, and Anlı, R. Ertan

Abstract

In this study, the effect of solvent type and solvent concentration on the ultrasound assisted extraction (UAE) of polyphenols from popular tea samples (black, green, mate, blackberry and black mulberry) which have high antioxidant activities were investigated by chemometric approaches. For this purpose, green extraction method was preferred and hydroalcoholic solvents for applied this system in industry. Water, MeOH 100%, MeOH 75%, MeOH 50%, EtOH 100%, EtOH 75%, EtOH 50% were used as extraction solvent. Multivariate calibration analytical technique preferred for quantification of individual phenolic compounds in tea samples and the relationship between total phenolic content (TPC), total flavonoid content (TFC) and total antioxidant activity (TAA) and individual phenolics was determined by chemometric approaches. The results of the study suggest that only spectroscopic comparisons based on TPC, TFC and TAA correlations are insufficient or even incorrect, and this is due to the fact that in tea samples different molecules besides different flavonoid structures are sensitive to spectroscopic techniques. The determination of the appropriate type and the concentration of solvent would contribute the usage of the herbal plants as a source of natural antioxidants in foods and pharmacology in large-scale industrial applications.

Published
2024
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10. Deep Learning Based Financial Forecasting Models

Author

Balat, Eray, Ekinci, Cem Taylan, Arlı, H. Şebnem, Ulus, Ceren, and Akay, M. Fatih

Subjects
Financial planning, Financial forecasting, Deep learning
Abstract

Financial planning involves systematical forecasting and calculation of cash and financial flows into and out of the company. Financial planning is the reconciliation of cash inflows and outflows, both in terms of amount and time by forecasting all types of cash inflows and outflows that will occur during the company's operations. It allows to quickly determine the solution process, make analysis, forecasts and strategic decisions. This study aims to develop financial forecasting models using univariate deep learning methods. For this purpose Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Long Short Term Memory (ConvLSTM) have been used. The performance of the developed models has been evaluated using Mean Absolute Percentage Error (MAPE). The dataset includes 464 rows and total inbound and total outbound invoice amount data from June 22nd, 2020 to March 31st, 2022. Forecast models have been developed for 2 different weeks (28.02.2022 – 04.03.2022 and 21.03.2022 – 25.03.2022) and 2 different months (January 2022 and March 2022) randomly selected from the dataset. When the forecast models developed for inbound invoice amount and outbound invoice amount are examined, it is found that satisfactory results have not been obtained for the monthly forecasts. For the weekly forecasts, MAPE’s of the forecast models were found to be less than 20% in general., {"references":["W. Dai, \"Application of improved convolution neural network in financial forecasting.\" Journal of Organizational and End User Computing (JOEUC), vol. 34, no. 3, pp. 1-16, 2022.","H. Weytjens, E. Lohmann, M. Kleinsteuber, \"Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet.\" Electronic Commerce Research, vol. 21, no. 2, pp. 371-391, 2021.","H. Wasserbacher, M. Spindler, \"Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls\", Digital Finance, pp. 1-26, 2021.","H. Lei, H. Cailan, \"Comparison of multiple machine learning models based on enterprise revenue forecasting.\" Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), pp. 354-359, January, 2021.","M. Çuhadar, \"A comparative study on modelling and forecasting tourism revenues: The case of Turkey.\" Advances in Hospitality and Tourism Research (AHTR), vol. 8, no. 2, pp. 235-255, 2020.","Z. Zhang, K. Zhao, K. Huang, Q. Jia, Y. Fang, Q. Yu, \"Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs.\" 2020","C. Xinyue,X. Zhaoyu, Z. Yue, \"Using Machine Learning to Forecast Future Earnings.\" Atlantic Economic Journal, vol. 48, no. 4, pp. 543-545, 2020.","A. Papadimitriou, U. Patel, L. Kim, G. Bang, A. Nematzadeh, X. Liu, \"A multi-faceted approach to large scale financial forecasting.\" In Proceedings of the First ACM International Conference on AI in Finance, pp. 1-8, October, 2020.","T. Lorenz, C. Homburg, \"Determinants of analysts' revenue forecast accuracy.\" Review of Quantitative Finance and Accounting, vol. 51, no. 2, pp. 389-431, 2018.","I. Leifer, L. Leifer,2016, \"Small business valuation with use of cash flow stochastic modeling.\" In 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO), pp. 511-516, February, 2016.","A. Manowska, \"Using the LSTM Network to Forecast the Demand for Electricity in Poland.\" Applied Sciences, vol. 10, no. 23, pp. 8455, 2020.","B. Liu, C. Song, Q. Wang, Y. Wang, \"Forecasting of China's solar PV industry installed capacity and analyzing of employment effect: based on GRA-Bi-LSTM model.\" Environmental Science and Pollution Research, vol. 29, no. 3, pp. 4557- 4573, 2022.","S. Siami-Namini, N. Tavakoli, A. S. Namin, \"A comparison of ARIMA and LSTM in forecasting time series.\" In 2018 17th IEEE international conference on machine learning and applications (ICMLA), pp. 1394-1401, December, 2018."]}

Published
2022
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12. Delivery Time Prediction Using Support Vector Machine Combined with Look-back Approach

Author

Erkmen,Oğuz Erdi, Nigiz,Ece, Sarı,Z. Sude, Arlı,H. Şebnem, and Akay,M. Fatih

Subjects
Support Vector Machine, Machine learning, Delivery time prediction, Look-back approach
Abstract

Delivery time refers to the time elapsed from the moment the order is created to the delivery of the product to the final consumer. It is extremely important to predict the delivery time in order to ensure customer satisfaction and smooth logistics processes. The aim of this study is to predict the delivery time using Support Vector Machine (SVM) with and without consecutive look-back and periodic look-back approaches. A sample dataset obtained from Kaggle was used. Mean Absolute Error (MAE) was utilized to evaluate the performance of the prediction models. According to the results, the average MAE obtained with the look-back approach (3.81) was 59.12% lower than that obtained without the look-back approach (9.32)., {"references":["N. Salari, S. Liu, Z. J. M. Shen., \"Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?\", Manufacturing & Service Operations Management Sep., 2022","M. Lochbrunner, H. F. Witschel. \"Combining Machine Learning with Human Knowledge for Delivery Time Estimations.\", FHNW - University of Applied Sciences Northwestern Switzerland, Olten, 2022","A. C. de Araujo, A. Etemad, \"End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications\", IEEE Internet of Things Journal, vol. 8, no. 23, pp. 17043-17056, Dec., 2021","J. Khiari, C. Olaverri-Monreal, \"Boosting Algorithms for Delivery Time Prediction in Transportation Logistics,\" in 2020 International Conference on Data Mining Workshops ICDMW, pp. 251-258, Sorrento 2020","M. Abebe, Y. Shin, Y. Noh, S. Lee, I. Lee, \"Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping\". Applied Sciences vol. 10, no.7, pp. 2325, March, 2020","L. Barua, B. Zou, Y. Zhou, \"Machine learning for international freight transportation management: A comprehensive review\", Research in Transportation Business & Management, vol. 34, pp. 100453, 2020","A. Viellechner, S. Spinler, \"Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms\", Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020","N. Servos, X. Liu, M. Teucke, M. Freitag, \"Travel time prediction in a multimodal freight transport relation using machine learning algorithms\", Logistics vol. 4, no.1, pp. 1, Dec., 2019","B. Jia, N. G. M. Rytter, L. B. Reinhardt, G. Haulot, M. B Billesø, \"Estimating Discharge Time of Cargo Units– A Case of Ro-Ro Shipping\" in International Conference on Computational Logistics pp. 122-135, Barranquilla, 2019","F. Wu, L. Wu. \"DeepETA: a spatial-temporal sequential neural network model for estimating time of arrival in package delivery system.\" in Proceedings of the AAAI Conference on Artificial Intelligence, Hawaii 2019","A. C. Jonquais, F. Krempl. \"Predicting Shipping Time with Machine Learning.\" 2019."]}

Published
2022
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13. Inhibition of Knee Sensory Receptors Alters Quadriceps Muscle Coordination in the Rat

Author

Torricelli, D, Akay, M, Pons, JL, Alessandro, C, C Tresch, M, Cristiano Alessandro, Matthew C Tresch, Torricelli, D, Akay, M, Pons, JL, Alessandro, C, C Tresch, M, Cristiano Alessandro, and Matthew C Tresch

Abstract

Internal joint structures such as ligaments are rich with sensory receptors. How the central nervous system (CNS) uses the information provided by these receptors to modify motor commands or guide rehabilitation is still poorly understood. Here, we provide evidence that the sensory afferents originating from the receptors within the knee capsule affect the coordination of the quadriceps muscles in the rat. This result suggests the existence of control loops driven by these receptors. Lesions to internal joint structures should therefore not solely be considered as musculoskeletal injuries, but more broadly as neuromuscular injuries.

Published
2022

14. Wireless eye-tracking technology application and self-report measures to explore users’ approach to Smart Home Systems (SHS)

Author

Torricelli, D, Akay, M, Pons, JL, Angioletti, Laura, Cassioli, Federico, Balconi, Michela, Angioletti Laura (ORCID:0000-0002-3027-2272), Cassioli Federico, Balconi Michela (ORCID:0000-0002-8634-1951), Torricelli, D, Akay, M, Pons, JL, Angioletti, Laura, Cassioli, Federico, Balconi, Michela, Angioletti Laura (ORCID:0000-0002-3027-2272), Cassioli Federico, and Balconi Michela (ORCID:0000-0002-8634-1951)

Abstract

The relation between individual differences in some psychological traits and eye-gaze behavior in participants exploring a Smart Home System (SHS) was considered. Real-time eye-gaze metrics were recorded while subjects were engaged with 5 tech-interaction (TI) areas with different levels of complexity in the SHS. Results suggested easiness to visually process a simpler TI compared to complex ones, in terms of higher number of fixations and of slower Time To First Fixation (TTFF). In two of the TIs (Living Room and Bathroom) we found a negative correlation: between the external Locus of Control (LoC) and the number of fixations, which might indicate a possible tendency for individuals with low external LoC to explore more actively complex TIs. A negative correlation was found between Behavioural Activation System Reward Responsiveness (BAS-RR) and fixation duration, suggesting a tendency for subjects with high BAS-RR to scan the TIs with more quick and dynamic visual patterns.

Published
2022

15. Wearable neurotechnologies for neurocognitive empowerment in applied contexts

Author

Torricelli, D, Akay, M, Pons, JL, Crivelli, Davide, Fronda, Giulia, Angioletti, Laura, Spinosa, C., Balconi, Michela, Crivelli Davide (ORCID:0000-0003-2221-2349), Fronda Giulia (ORCID:0000-0003-0535-0846), Angioletti Laura (ORCID:0000-0002-3027-2272), Balconi Michela (ORCID:0000-0002-8634-1951), Torricelli, D, Akay, M, Pons, JL, Crivelli, Davide, Fronda, Giulia, Angioletti, Laura, Spinosa, C., Balconi, Michela, Crivelli Davide (ORCID:0000-0003-2221-2349), Fronda Giulia (ORCID:0000-0003-0535-0846), Angioletti Laura (ORCID:0000-0002-3027-2272), and Balconi Michela (ORCID:0000-0002-8634-1951)

Abstract

Recently, the use of wearable devices and neurotechnologies for well-being and neurocognitive empowerment has increased, allowing users to achieve better awareness and control of their mindset and mood states. We present an overview of main results from a research line on the effectiveness of an intensive training protocol supported by wearable neurofeedback devices in the field of neurocognitive empowerment and stress management. Specifically, the potential of such neurotechnology-supported training was tested by collecting behavioural, psychometric, neuropsychological, and physiological outcomes and comparing experimental and active control groups. The protocol was tested in different experimental and applied contexts—including sports, workplace, and healthy aging. Based on the multi-level assessment of training outcomes, results highlighted the effectiveness of using intensive neurotechnology-mediated protocols to enhance focusing and attention regulation skills and to reduce anxiety/stress levels while increasing mental vigour, hinting at the potential of such new technologies for neurocognitive empowerment.

Published
2022

16. Wearable neurofeedback training for boosting attention regulation at the wheel

Author

Torricelli, D, Akay, M, Pons, JL, Crivelli, Davide, Angioletti, Laura, Balconi, Michela, Crivelli Davide (ORCID:0000-0003-2221-2349), Angioletti Laura (ORCID:0000-0002-3027-2272), Balconi Michela (ORCID:0000-0002-8634-1951), Torricelli, D, Akay, M, Pons, JL, Crivelli, Davide, Angioletti, Laura, Balconi, Michela, Crivelli Davide (ORCID:0000-0003-2221-2349), Angioletti Laura (ORCID:0000-0002-3027-2272), and Balconi Michela (ORCID:0000-0002-8634-1951)

Abstract

We tested the effects of a three-week neurofeedback (NF) empowerment protocol on drivers’ cognitive performance by looking at both behavioural and electrophysiological (Event-Related Potentials, ERPs) markers of attention regulation skills. Results highlighted higher task-related ERP marking attention orientation and cognitive control processes (the N2 ERP component) in the experimental group receiving NF training compared to control subjects. Present findings suggest that intensive NF-based training might be a valuable way to improve drivers focused and sustained attention mechanisms, with practical implications for the development of proactive prevention protocols for driving performance.

Published
2022

17. LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction.

Author

Kervancı, I. Sibel and Akay, M. Fatih

Subjects
PRICES, MACHINE learning, DEEP learning, BITCOIN, ALGORITHMS, FORECASTING
Abstract

Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they are not specific to all problems. This paper used Long Short-Term Memory (LSTM) and eight different hyperparameters (go-backward, epoch, batch size, dropout, activation function, optimizer, learning rate, and the number of layers) to examine daily and hourly Bitcoin datasets. The effects of each parameter on the daily dataset on the results were evaluated and explained. These parameters were examined with the hparam properties of Tensorboard. As a result, it was seen that examining all combinations of parameters with hparam produced the best test Mean Square Error (MSE) values with hourly dataset 0.000043633 and daily dataset 0.00061806. Both datasets produced better results with the tanh activation function. Finally, when the results are interpreted, the daily dataset produces better results with a small learning rate and dropout values. In contrast, the hourly dataset produces better results with a large learning rate and dropout values. [ABSTRACT FROM AUTHOR]

Published
2023
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30. Feasibility of the use of a Stereoscopic Camera In the Final Inclusion (SCI-FI) of Donor Organ Assessment Using Virtual Reality Technology

Author

Salas de Armas, Ismael A., Marcano, Juan, Patel, Jayeshkumar A., Akay, M. Hakan, Patel, Manish K., Akkanti, Bindu, Jumean, Marwan, Kumar, Sachin, Radovancevic, Rajko, Nathan, Sriram S., Kar, Biswajit, and Gregoric, Igor D.

Subjects
FOS: Clinical medicine, FOS: Medical and health sciences
Abstract

Objective: Donor organ assessment is challenging, particularly when physicians and surgeons have to make decisions remotely. To improve communication and preparation for the transplant process, we proposed that a versatile, secure, and transportable video presence would enable a detailed assessment of donor organs. Methods: A virtual reality camera with a 360-degree visualization range was used with a smartphone receiver to enable integral assessment of the donor's heart in the operating room, with visualization of the organs in real time. Prospective, observational data was collected in two cases in this proof-of-concept study. An assessment physician evaluated the donor heart remotely, and the assessment physician’s conclusions were compared to the transplanting surgeon. Results: The assessment physician was able to adjust the view, angle, contrast, and zoom of the images independently and deemed the organs suitable for transplant, which correlated in a blinded fashion with the procurement surgeon’s independent assessment. Discussion: Usage of 360-degree high definition cameras and real-time, three-dimensional mobile receivers may be a feasible approach to apply to organ procurement, allowing physicians located remotely from the procurement center to participate and assist in the final intraoperative assessment for organ acceptance., Salas de Armas, et al. Feasibility of the use of a Stereoscopic Camera In the Final Inclusion (SCI-FI) of Donor Organ Assessment Using Virtual Reality Technology. The VAD Journal. 2021; 7(1):e2021715. https://doi.org/10.11589/vad/e2021715

Published
2021
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31. Royal academy of medicine in Ireland section of bioengineering: Proceedings of meeting held in dundalk, co. louth, in february 1998.

Author

McAlinden, M. G., Magowan, J., Wilson, D. J., Insley, G., Ferris, P., Prendergast, P. J., Rice, J., Blayney, A. W., Dalstra, M., Walsh, M., McGloughlin, T., Grace, P., Colgan, D., Bray, D., McCormack, B. A. O., Reilly, R., Tancred, D., Carr, A. J., McCormack, B. A. O., Leyland, N. S., Meenan, J., Boyd, A., Akay, M., O’Dwyer, B., McCormack, B. A. O., Dunne, N. J., Ryan, K., Orr, J. F., Stungo, B., Brennan, E. G., O’Hare, N. J., Walsh, M. J., Jordan, M. F., Rasheed, A. M., Kelly, C., Kay, E., Bouchier-Hayes, D. J., Leahy, A., Maher, S. A., O’Reilly, D., Foley, J., Gillan, M. A., Cole, J. S., Raghunathan, S., O’Reilly, M. J. G., Kenny, T., Foley, J., Hourigan, T. F., Lyons, G. M., Cox, S. L., Kernohan, W. G., Fitzpatrick, C., Kernohan, W. G., Dempsey, G. J., Millar, I., Kelly, S., Charlwood, A. P., O’Brien, S., Beverland, D. E., Kavanagh, A., McGloughlin, T., Neligan, M. F., McKenna, J., Laracy, P., Moran, D., O’Beirne, J., Charlwood, A. P., Kelly, S., Nixon, J. R., Beverland, D. E., Kenny, P., Maher, S. A., Murphy, L. A., Prendergast, P. J., O’Rourke, S. K., O’Donoghue, D., Gilchrist, M. D., Caulfield, B., O’Brien, B., Simms, C., Lyons, C. G., Brady, C. L., Badran, S., Clifford, P. M., Burden, D. J., Orr, J. F., Taylor, D., Hill, R., Griffin, S., De Barra, E., Brook, I., Reytil, P., Blades, M., O’Reilly, J. P., Masterson, B. F., Macauley, D., Toner, M., Walker, J., Gillan, J., Boyd, A., Meenan, J., Akay, M., Leyland, N. S., Murphy, H., McNamara, P., Jones, E., Kelly, P., Rajah, L., Dhaif, B., Colville, J., Waide, D. V., Waide, D. V., Lawlor, G., McCormack, A., Carr, A. J., McCartney, W., McNamara, B. P., Connolly, P., Devitt, A., McElwaine, J., O’Reilly, P., Maher, S. A., Eames, M. H. A., Cosgrove, A. P., Baker, R. J., Condron, J., Coyle, E., Nugent, D., Webb, J., Black, N. D., Mclntyre, M., Lowery, M., O’Malley, M., Vaughan, L., Sweeney, P. C., Lyons, G. M., McGiven, R., Collins, A. D., Gibson, M. J., Lyons, G. M., Clernon, G. F., Wilcox, D. J., Shanahan, A., Buckley, P. J., Hanna, S., McGrellis, N., Orr, J. F., Fennel, B., Hill, R., Akinmade, A., Mitchell, A., Pintado, M. R., Douglas, W. H., Ryan, E. E., Savage, E. J., Orr, J. F., Mitchell, E., Silbermann, M., Mullett, H., Ranjith, P., Burke, T., Hill, R., Dorreil, P., Watters, E. P., Spedding, P. L., Grimshaw, J., M Bowler, D. J., Felle, P., Allen, D., McCormack, B. A. O., Moran, R., Lennon, A. B., McCormack, B. A. O., Prendergast, P. J., Thompson, N. S., Cosgrove, A. P., Baker, R. J., Saunders, J. L., Taylor, T., Grimson, J., Grimson, W., Azuaje, F., Black, N. D., Adamson, K., Lopes, P., Dubitzky, W., Wu, X., White, J., Murtagh, F., Campbell, J. G., Adamson, K., O’Tiarnaigh, R. I., Cormack, W. A., Hume, A., Starck, J. L., Lardillier, P., Kernohan, W. G., Mao, W. E., Bell, D., Chambers, M. G. A., McCammon, C., Leane, G. E., Lyons, G. M., Lyons, D. J., Lacrox, D., Murphy, L. A., Prendergast, P. J., FitzPatrick, D. P., McClorey, M., Meenen, J., O’Brien, F. J., Lee, T. C., Pellegrini, F., Dickson, G. R., and Taylor, D.

Published
1998
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35. Performance evaluation of directory protocols on an optical broadcast-based distributed shared memory multiprocessor

Author

AbasikeleA, A[degrees]Pek and Akay, M. Fatih

Subjects
Multiprocessing -- Directories, Multiprocessing -- Analysis, Fiber optics -- Equipment and supplies, Fiber optics -- Analysis, Fiber optics -- Directories, Multiprocessing, Computers, Electronics, Engineering and manufacturing industries
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2009.06.003 Byline: A[degrees]pek AbasikeleA, M. Fatih Akay Keywords: Directory protocols; Distributed shared memory; Multiprocessors; Performance evaluation; Optical interconnects; Simulation Abstract: Recent advances in the development of optical technologies suggest the possible emergence of broadcast-based optical interconnects within cache-coherent distributed shared memory (DSM) multiprocessor architectures. It is well known that the cache-coherence protocol is a critical issue in designing such architectures because it directly affects memory latencies. In this paper, we evaluate via simulation the performance of three directory-based cache-coherence protocols; strict request-response, intervention forwarding and reply forwarding on the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus), which is a low-latency and high-bandwidth broadcast-based fiber-optic interconnection network supporting DSM. The simulated system contains 64 nodes, each of which has a processor, a cache controller, a directory controller and an output channel. Simulations have been conducted for each protocol to measure average processor utilization, average network latency and average number of packets transferred over the network for varying values of the important DSM parameters such as the ratio of the mean channel service time to mean thread run time (T/R), probability of a cache block being in modified state {P(M)}, the fraction of write misses {P(W)} and home node contention rate. The results reveal that for all cases, except for low values of P(M), intervention forwarding gives the worst performance (lowest processor utilization and highest latency). The performance of strict request-response and reply forwarding is comparable for several values of the DSM parameters and contention rate. For a contention rate of 0%, the increase of P(M) makes reply forwarding perform better than strict request-response. The performance of all protocols decreases with the increase of P(W) and contention rate. However, the performance of strict request-response is the least affected among other protocols due to the negative impact of the increase of P(W) and contention rate. Therefore, for the full contention case (i.e. contention rate of 100%); for low values of P(M), or for mid values of P(M) and high values of P(W), strict request-response performs better than reply forwarding. These results are significant in the sense that they provide an insight to multiprocessor architecture designers for comparing the performance of different directory-based cache-coherence protocols on a broadcast-based interconnection network for different values of the DSM parameters and varying rates of contention. Author Affiliation: Computer Engineering Department, Aukurova University, 01330 Adana, Turkey Article History: Received 24 January 2009; Revised 7 June 2009; Accepted 25 June 2009

Published
2010

36. Traumatic respiratory failure and veno-venous extracorporeal membrane oxygenation support.

Author

Salas De Armas, Ismael A, Akkanti, Bindu, Doshi, Pratik B, Patel, Manish, Kumar, Sachin, Akay, M Hakan, Hussain, Rahat, Dinh, Kha, Baloch, Muhammad Yasir, Ahmed, Mahmoud Samy, Banjac, Igor, Jumean, Marwan F, McGinness, Kelly, Janowiak, Lisa M, Mittal Agrawal, Kriti, Nathan, Sriram, Zaki, John, Nieto, Luis, Klein, Kimberly, and Taub, Ethan

Subjects
RESPIRATORY insufficiency, EXTRACORPOREAL membrane oxygenation
Abstract

Background: Respiratory failure (RF) is a common cause of death and morbid complication in trauma patients. Extracorporeal membrane oxygenation (ECMO) is increasingly used in adults with RF refractory to invasive mechanical ventilation. However, use of ECMO remains limited for this patient population as they often have contraindications for anticoagulation. Study design: Medical records were retroactively searched for all adult patients who were admitted to the trauma service and received veno-venous ECMO (VV ECMO) support between June 2015 and August 2018. Survival to discharge and ECMO-related complications were collected and analyzed. Results: Fifteen patients from a large Level I trauma center met the criteria. The median PaO2/FiO2 ratio was 53.0 (IQR, 27.0–76.0), median injury severity score was 34.0 (IQR, 27.0–43.0), and the median duration of ECMO support was 11 days (IQR, 7.5–20.0). For this cohort, the survival-to-discharge rate was 87% (13/15). The incidence of neurologic complications was 13%, and deep vein thrombosis was reported in two cases (13%). Conclusions: Survival rates of trauma patients in this study are equivalent to, or may exceed, those of non-trauma patients who receive ECMO support for other types of RF. With the employment of a multidisciplinary team assessment and proper patient selection, early cannulation, traumatic RF may be safely supported with VV ECMO in experienced centers. [ABSTRACT FROM AUTHOR]

Published
2022
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37. Traumatic respiratory failure and veno-venous extracorporeal membrane oxygenation support

Author

Salas De Armas, Ismael A, primary, Akkanti, Bindu, additional, Doshi, Pratik B, additional, Patel, Manish, additional, Kumar, Sachin, additional, Akay, M Hakan, additional, Hussain, Rahat, additional, Dinh, Kha, additional, Baloch, Muhammad Yasir, additional, Ahmed, Mahmoud Samy, additional, Banjac, Igor, additional, Jumean, Marwan F, additional, McGinness, Kelly, additional, Janowiak, Lisa M, additional, Mittal Agrawal, Kriti, additional, Nathan, Sriram, additional, Zaki, John, additional, Nieto, Luis, additional, Klein, Kimberly, additional, Taub, Ethan, additional, Tint, Hlaing, additional, Patel, Jayeshkumar A, additional, Nascimbene, Angelo, additional, Kar, Biswajit, additional, and Gregoric, Igor D, additional

Published
2021
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42. Feasibility of the use of a Stereoscopic Camera In the Final Inclusion (SCI-FI) of Donor Organ Assessment Using Virtual Reality Technology

Author

Salas de Armas, Ismael, primary, Marcano, Juan, additional, Patel, Jayeshkumar, additional, Akay, M., additional, Patel, Manish, additional, Akkant, Bindu, additional, Jumean, Marwan, additional, Kumar, Sachin, additional, Radovancevic, Rajko, additional, Nathan, Sriram, additional, Kar, Biswajit, additional, and Gregoric, Igor, additional

Published
2021
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48. Left Ventricular Rupture After Impella® Placement During High-Risk Percutaneous Coronary Intervention

Author

Salas de Armas, Ismael A., primary, Kumar, Sachin, additional, Almustafa, Ahmed, additional, Akkanti, Bindu, additional, Akay, M. Hakan, additional, Patel, Manish K., additional, Patel, Jayeshkumar, additional, Rajagopal, Keshava, additional, Marcano, Juan, additional, Goswami, Rajiv, additional, Gregoric, Igor D., additional, and Kar, Biswajit, additional

Published
2020
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49. Axillary Access TAVR: Entrapment of a Transcatheter Aortic Valve in the Innominate Artery With Aortic Dissection

Author

Salas de Armas, Ismael A., primary, Basra, Sukhdeep S., additional, Patel, Manish K., additional, Akay, M. Hakan, additional, Kumar, Sachin, additional, Carrazo, Wendy, additional, Marcano, Juan, additional, Akkanti, Bindu, additional, Patel, Jayeshkumar, additional, Mittal, Kriti, additional, de Filippis, Alejandro, additional, Zaki, John, additional, Gregoric, Igor D., additional, and Kar, Biswajit, additional

Published
2020
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