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Biol. 1986;88(2):210–12. Google Scholar Suput D. Equinatoxin II, and exocytosis. Ann N Y Acad Sci. 1994;710:30–7.Article PubMed CAS Google Scholar Šuput D, Schwarz W. Equinatoxin II activates calcium-dependent potassium channels in human erythrocytes. In: Gopalakrishnakone P, Tan CH, editors. Recent advances in toxinology. Singapore: Singapore University Press; 1992. Google Scholar Suput D, Rubly N, Meves H. Effects of equinatoxins on single myelinated nerve fibres. In: Gopalakrishnakone P, Tan CH, editors. Progress in venom and toxin research. Singapore: National University of Singapore; 1987. Google Scholar Suput D, Frangez R, Bunc M. Cardiovascular effects of equinatoxin III from the sea anemone Actinia equina (L.). Toxicon. 2001;39(9):1421–7.Article PubMed CAS Google Scholar Tejuca M, Anderluh G, Macek P, Marcet R, Torres D, Sarracent J, Alvarez C, Lanio ME, Dalla Serra M, Menestrina G. Antiparasite activity of sea-anemone cytolysins on Giardia duodenalis and specific targeting with anti-Giardia antibodies. Int J Parasitol. 1999;29(3):489–98.Article PubMed CAS Google Scholar Tejuca M, Diaz I, Figueredo R, Roque L, Pazos F, Martinez D, Iznaga-Escobar N, Perez R, Alvarez C, Lanio ME. Construction of an immunotoxin with the pore forming protein StI and ior C5, a monoclonal antibody against a colon cancer cell line. Int Immunopharmacol. 2004;4(6):731–44.Article PubMed CAS Google Scholar Teng CM, Lee LG, Lee CY, Ferlan I. Platelet aggregation induced by equinatoxin. Thromb Res. 1988;52(5):401–11.Article PubMed CAS Google Scholar Turk T, Macek P, Gubensek F. The role of tryptophan in structural and functional properties of equinatoxin II. Biochim Biophys Acta. 1992;1119(1):1–4.Article PubMed CAS Google Scholar Zorec R, Tester M,

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Hong D, Wang X (2019) BMC Bioinform 20:93 Google Scholar Dominguez C, Boelens R, Bonvin AMJJ (2003) J Am Chem Soc 125:1731CAS PubMed Google Scholar Cole J, Nissink JWM, Taylor R (2005) Protein-ligand and docking virtual screening with GOLD. In: Shoichet B, Alvarez J (eds) Virtual screening in drug discovery. CRC Press, Boca Raton, p 379 Google Scholar Moustakas DT, Lang PT, Pegg S, Pettersen E, Kuntz ID, Brooijmans N, Rizzo RC (2006) J Comput Aided Mol Des 20:601CAS PubMed Google Scholar Yan Y, Zhang D, Zhou P, Li B, Huang SY (2017) Nucleic Acids Res 45:W365CAS PubMed PubMed Central Google Scholar Tuszynska I, Magnus M, Jonak K, Dawson W, Bujnicki JM (2015) Nucleic Acids Res 43:W425CAS PubMed PubMed Central Google Scholar Lin YF, Cheng CW, Shih CS, Hwang JK, Yu CS, Lu CH (2016) J Chem Inf Model 56:2287CAS PubMed Google Scholar Lu CH, Chen CC, Yu CS, Liu YY, Liu JJ, Wei ST, Lin YF (2022) Bioinformatics 38:4428CAS PubMed Google Scholar Ó Conchúir S, Barlow KA, Pache RA, Ollikainen N, Kundert K, O’Meara MJ, Smith CA, Kortemme T (2015) PLoS ONE 10:e0130433PubMed PubMed Central Google Scholar Brancolini G, Kokh DB, Calzolai L, Wade RC, Corni S (2012) ACS Nano 6:9863CAS PubMed Google Scholar Download references

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ReferencesAcademy of Disaster Reduction and Emergency Management (ADREM) (2023) 2022 Global natural disaster assessment report, National Disaster Reduction Centre of China. N, Ahamed F, Rahman A (2024) Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia. Nat Hazards. Google Scholar Ahmed AN, Yafouz A, Birima AH, Kisi O, Huang YF, Sherif M, Sefelnasr A, El-Shafie A (2022) Water level prediction using various machine learning algorithms: a case study of Durian Tunggal River, Malaysia. Eng Appl Computational Fluid Mech 16(1):422–440. Google Scholar Aiyelokun OO, Aiyelokun OD, Agbede OA (2023) Application of random forest (RF) for flood levels prediction in Lower Ogun Basin. Nigeria Nat Hazards 119(3):2179–2195. Google Scholar Ayus I, Natarajan N, Gupta D (2023) Prediction of water level using machine learning and deep learning techniques. Iranian J Sci Technol, Trans Civil Eng 47(4):2437–2447. Google Scholar Belyakova PA, Moreido VM, Tsyplenkov AS, Amerbaev AN, Grechishnikova DA, Kurochkina LS, Filippov VA, Makeev MS (2022) Forecasting water levels in Krasnodar Krai Rivers with the use of machine learning. Water Resour 49(1):10–22. CAS Google Scholar Benke KK, Lowell KE, Hamilton AJ (2008) Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model. Math Comput Model 47(11–12):1134–1149. Google Scholar Bontempi G, Ben Taieb S, Le Borgne YA (2013) Machine learning strategies for time series forecasting. Lecture Notes Bus Inf Proc 138:62–77. Google Scholar Breiman L (2001) Random forests. Mach Learn 45:5–32. Google Scholar Chang CH, Chen H, Guo WD, Yeh SH, Chen WB, Liu CH, Lee SC. Google Keep Chrome Extension l tiện ch tr n tr nh duyệt Google Chrome hỗ trợ người d ng lưu nội dung v o ứng dụng ghi ch Google Keep dễ d ng. C ch vẽ trong Google DocsC ch vẽ trong Google DocsHướng dẫn c c bạn c ch vẽ h nh trong Google Docs (Google T i liệu) chi tiết v dễ d ng.C ch l m như sau:Bướ

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If the desire for greater harmonization in global health R&D are ever to be realised[8].DisclaimerThe views and opinions expressed in this article are those of the authors and do not necessarily reflect those of the organizations they represent. References2008, World Health Assembly resolution WHA61.21, Forum for Health Research: Global Forum for Health Research. 2001–2009, Monitoring financial flows for health research series Google Scholar UK Clinical Research Collaboration (2005) Health Research Classification System: 2005, Research Classification Systems – Current Approaches and Future Recommendations: 2011, European Science Foundation: Policy Brief 43Pereira S: Multi-terminology indexing for the assignment of MeSH descriptors to medical abstracts in French. AMIA Annu Symp Proc. 2009, 521-525. Google Scholar Lane J: Measuring the Results of Science Investments. Science. 2011, 331 (6018): 678-680. 10.1126/science.1201865.Article CAS PubMed Google Scholar Moran M: The G-Finder report 2010. Neglected Disease Research & Development. 2010, Policy Cures: Is the global financial crisis changing R&D?, Google Scholar Report of the Consultative Expert Working Group on Research and Development: Financing and Coordination Research and Development to Meet Health Needs in Developing Countries. 2012, World Health Organization, Geneva: Strengthening Global Financing and Coordination Google Scholar Download referencesAuthor informationAuthors and AffiliationsPublic Health, Innovation and Intellectual Property, World Health Organization, Avenue Appia, Geneva, CH-1211, SwitzerlandRobert F TerryStrategic Planning & Policy Unit, The Wellcome Trust, 215 Euston Road, London, NW1 2BE, United KingdomLiz AllenHealth, UBS Optimus Foundation, Augustinerhof 1, Zürich, CH-8098, SwitzerlandCharles A GardnerPolicy Cures, 36 Gordon Square, London, WC1H 0PD, United KingdomJavier GuzmanPolicy Cures, 46 Market Street, Sydney, NSW,

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Springs. China J Hydrol 625:130079. Google Scholar Gan M, Pan S, Chen Y, Cheng C, Pan H, Zhu X (2021) Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River. J Marine Sci Eng 9(5):496. Google Scholar Gao X, Luo H, Wang Q, Zhao F, Ye L, Zhang Y (2019) A human activity recognition algorithm based on stacking denoising autoencoder and lightGBM. Sensors 19:947. Google Scholar Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42. Google Scholar Guo WD, Chen WB, Yeh SH, Chang CH, Chen H (2021) Prediction of river stage using multistep-ahead machine learning techniques for a tidal river of Taiwan. Water 13(7):920. Google Scholar Guo WD, Chen WB, Chang CH (2023a) Error-correction-based data-driven models for multiple-hour-ahead river stage predictions: a case study of the upstream region of the Cho-Shui River. Taiwan J Hydrol: Reg Stud 47:101378. Google Scholar Guo Y, Peng Y, Hao R, Tang X (2023b) Capturing spatial–temporal correlations with attention based graph convolutional network for network traffic prediction. J Netw Comput Appl 220:103746. Google Scholar Hameed MM, Alomar MK, Khaleel F, Al-Ansari N (2021) An extra tree regression model for discharge coefficient prediction: novel, practical applications in the hydraulic sector and future research directions. Math Probl Eng 2021:7001710. Google Scholar Hanifi S, Cammarono A, Zare-Behtash H (2024) Advanced hyperparameter optimization of deep learning models for wind power prediction. Renewable Energy 221:119700. Google Scholar Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural

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Functions of Internet-connected devices in the right ways, we might achieve breakthroughs in our ability to improve mental health and well-being. ReferencesTwenge, J. M., Martin, G. N. & Spitzberg, B. H. Psychol. Pop. Media Culture 8, 329–345 (2019).Article Google Scholar Orben, A. & Przybylski, A. K. Nature Hum. Behav. 3, 173–182 (2019).Article PubMed Google Scholar Odgers, C. L. & Jensen, M. R. J. Child Psychol. Psychiatry (2020).Article Google Scholar Maccoby, E. E. The Two Sexes: Growing Up Apart, Coming Together Ch. 2 (Harvard Univ. Press, 1999). Google Scholar Nesi, J., Choukas-Bradley, S. & Prinstein, M. J. Clin. Child. Fam. Psychol. Rev. 21, 267–294 (2018).Article PubMed Google Scholar Rosenquist, J. N., Fowler, J. H. & Christakis, N. A. Molec. Psychiatry 16, 273–281 (2011).Article Google Scholar Orben, A. Social Psychiatry Psychiatr. Epidemiol. (2020).Article Google Scholar Mohr, D. C., Zhang, M. & Schueller, S. M. Annu. Rev. Clin. Psychol. 13, 23–47 (2017).Article PubMed Google Scholar Nelson, B. W. & Allen, N. B. Perspect. Psychol. Sci. 13, 718–733 (2018).Article PubMed Google Scholar Nahum-Shani, I. et al. Ann. Behav. Med. 52, 446–462 (2018).Article PubMed Google Scholar Allen, N. B., Nelson, B. W., Brent, D. & Auerbach, R. P. J. Affect. Disord. 250, 163–169 (2019).Article PubMed Google Scholar Download references. Google Keep Chrome Extension l tiện ch tr n tr nh duyệt Google Chrome hỗ trợ người d ng lưu nội dung v o ứng dụng ghi ch Google Keep dễ d ng.

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Deis LN, Verma V, Keedy DA, Hintze BJ, Chen VB, Jain S, Lewis SM, Arendall WB 3rd, Snoeyink J, Adams PD, Lovell SC, Richardson JS, Richardson DC (2018) Protein Sci 27:293CAS PubMed Google Scholar Weichenberger CX, Sippl MJ (2006) Bioinformatics 22:1397CAS PubMed Google Scholar Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) J Biomol NMR 8:477CAS PubMed Google Scholar Wiederstein M, Sippl MJ (2007) Nucleic Acids Res 35:W407PubMed PubMed Central Google Scholar Vriend G (1990) J Mol Graph 8:52CAS PubMed Google Scholar Hooft RWW, Vriend G, Sander C, Abola EE (1996) Nature 381:272CAS PubMed Google Scholar Hawkins PCD, Nicholls A (2012) J Chem Inf Model 52:2919CAS PubMed Google Scholar Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Hénin J, Jiang W, McGreevy R, Melo MCR, Radak BK, Skeel RD, Singharoy A, Wang Y, Roux B, Aksimentiev A, Luthey-Schulten Z, Kalé LV, Schulten K, Chipot C, Tajkhorshid E (2020) J Chem Phys 153:044130CAS PubMed PubMed Central Google Scholar Kawabata T (2019) Biophys Physicobiol 16:391CAS PubMed PubMed Central Google Scholar Halgren TA (2009) J Chem Inf Model 49:377CAS PubMed Google Scholar Ngan CH, Bohnuud T, Mottarella SE, Beglov D, Villar EA, Hall DR, Kozakov D, Vajda S (2012) Nucleic Acids Res 40:W271CAS PubMed PubMed Central Google Scholar Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE, Luo L, Xia B, Beglov D, Vajda S (2015) Nat Protoc 10:733CAS PubMed PubMed Central Google Scholar Yang J, Roy A, Zhang Y (2013) Bioinformatics 29:2588CAS PubMed PubMed Central Google Scholar Laskowski RA (1995) J Mol Graph 13:323CAS PubMed Google Scholar Le Guilloux V, Schmidtke P, Tuffery P (2009) BMC Bioinform 10:168 Google Scholar Schmidtke P, Le Guilloux V, Maupetit J, Tuffery P (2010) Nucleic Acids Res 38:W582CAS PubMed PubMed Central Google Scholar Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS (2013) Nucleic Acids Res 41:W314PubMed PubMed Central Google Scholar Ravindranath PA, Sanner MF (2016) Bioinformatics 32:3142CAS PubMed PubMed Central Google Scholar Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, De Fabritiis G (2017) Bioinformatics 33:3036PubMed Google Scholar Cui Y, Dong Q,