Academic literature on the topic 'Cover song detection'

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.

Lanthanide-doped nanoparticles have been considered as one of the most promising luminescent materials due to their excellent properties such as high photochemical stability, long-lived (μs-ms) luminescence, narrow emission band, and low toxicity.Moreover, benefiting from a unique electronic structure (4fn5s25p6 , n = 0-14), lanthanides have discrete energy levels and exhibit practical wavelength conversion via downshifting and upconversion processes. Hence, their emissions cover the spectral regions from ultraviolet (UV) to near-infrared (NIR).[1,2] Here, my talk is mainly devoted to our recent developments, including (1) recently, we present a new composition of Er3+-based upconversion nanoparticles with color-switchable output under irradiation with 980, 808, or 1535 nm light for information security. The variation of excitation wavelengths changes the intensity ratio of visible (Vis)/near-infrared 1535 nm (NIR-II) emissions. Taking advantage of the Vis/NIR-II multi-modal emissions of upconversion nanoparticles and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue.[3] (2) we construct heterostructured nanocomposites based on upconversion nanoparticles and EuSe semiconductors by using cation exchange method. It is generally considered that epitaxial growth is difficult when the lattice mismatch is large between two materials. In this case, the cation exchange of Eu3+ ions and other rare-earth ions could promote the formation of buffer layers to reduce the lattice mismatch and promote the heterogeneous epitaxial growth of EuSe on the upconversion nanoparticles. The heterostructured nanocomposites can emit tunable multicolor fluorescence under excitation of UV, continuous NIR, and pulsed NIR light. Based on the advantage of multiple tunable luminescence, the nanocomposites are designed as optical modules to load optical information. This work enables multi-dimensional storage of information and provides new insights into the design and fabrication of next-generation storage materials. References [1] L. N. Sun, R. Wei, J. Feng, and H. J. Zhang, Tailored lanthanide-doped upconversion nanoparticles and their promising bioapplication prospects, Coordination Chemistry Reviews, 2018, 364, 10-32. [2] G. Sun, Y. Xie, L. N. Sun, and H. J. Zhang, Lanthanide Upconversion and Downshifting Luminescence for Biomolecules Detection, Nanoscale Horizons, 2021, 6(10), 766 – 780. [3] Y. Song, M. Lu, G. A. Mandl, Y. Xie, G. Sun,J. Chen, X. Liu,J. A. Capobianco, and L. N. Sun, “Energy Migration Control of Multimodal Emissions in an Er3+-Doped Nanostructure for Information Encryption and Deep-Learning Decoding”, Angewandte Chemie International Edition , 2021, 60(44), 23790–23796.

Music is a medium for emotional artistic expression. Different people have different understandings of music. Music emotion recognition (MER) has thus become a novel branch in computer music. The goal of this essay is to investigate in the performance of established CNN architectures, such as AlexNet and VGG16, to recognize emotions contained in a song. CAL500 dataset is used as it covers a variety of genres. The dataset is transformed to spectrograms, which can be understood by computers through image recognition. The result of this investigation turned out to be that previous architectures would lead to overfitting within the training of a few batches. Possible explanations for this are that the parameters used in the model are too large for a simple regression task. This research provides some understanding of how CNN works as a network initially designed for image classification. Understanding emotions using spectrograms might require less complex CNN models or new models that are specialized in such tasks.

Audio processing, including speech enhancement system, improves speech intelligibility and quality in real-time communication (RTC) such as online meetings and online education. However, such processing, primarily noise suppression and automatic gain control, is harmful to music quality when the captured signal is music instead of speech. A music detector can solve the issue above by switching off the speech processing when the music is detected. In RTC scenarios, the music detector should be low-complexity and cover various situations, including different types of music, background noises, and other acoustical environments. In this paper, a real-time music detection method with low-computation complexity is proposed, based on a convolutional neural network (CNN) using Mel-spectrogram and spectral flux as input features. The proposed method achieves overall 90.63% accuracy under different music types (classical music, instruments solos, singing-songs, etc.), speech languages (English and Mandarin), and noise types. The proposed method is constructed on a lightweight CNN model with a small feature size, which guarantees real-time processing.

The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM) with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009) it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Climate change induced sea-level rise (SLR) is on its increase globally. Regionally the lowlands of China, Vietnam, Bangladesh, and islands of the Malaysian, Indonesian and Philippine archipelagos are among the world’s most threatened regions. Sea-level rise has major impacts on the ecosystems and society. It threatens coastal populations, economic activities, and fragile ecosystems as mangroves, coastal salt-marches and wetlands. This paper provides a summary of the current state of knowledge of sea level-rise and its effects on both human and natural ecosystems. The focus is on coastal urban areas and low lying deltas in South-East Asia and Vietnam, as one of the most threatened areas in the world. About 3 mm per year reflects the growing consensus on the average SLR worldwide. The trend speeds up during recent decades. The figures are subject to local, temporal and methodological variation. In Vietnam the average values of 3.3 mm per year during the 1993-2014 period are above the worldwide average. Although a basic conceptual understanding exists that the increasing global frequency of the strongest tropical cyclones is related with the increasing temperature and SLR, this relationship is insufficiently understood. Moreover the precise, complex environmental, economic, social, and health impacts are currently unclear. SLR, storms and changing precipitation patterns increase flood risks, in particular in urban areas. Part of the current scientific debate is on how urban agglomeration can be made more resilient to flood risks. Where originally mainly technical interventions dominated this discussion, it becomes increasingly clear that proactive special planning, flood defense, flood risk mitigation, flood preparation, and flood recovery are important, but costly instruments. Next to the main focus on SLR and its effects on resilience, the paper reviews main SLR associated impacts: Floods and inundation, salinization, shoreline change, and effects on mangroves and wetlands. The hazards of SLR related floods increase fastest in urban areas. This is related with both the increasing surface major cities are expected to occupy during the decades to come and the increasing coastal population. In particular Asia and its megacities in the southern part of the continent are increasingly at risk. The discussion points to complexity, inter-disciplinarity, and the related uncertainty, as core characteristics. An integrated combination of mitigation, adaptation and resilience measures is currently considered as the most indicated way to resist SLR today and in the near future.References Aerts J.C.J.H., Hassan A., Savenije H.H.G., Khan M.F., 2000. Using GIS tools and rapid assessment techniques for determining salt intrusion: Stream a river basin management instrument. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25, 265-273. Doi: 10.1016/S1464-1909(00)00014-9. Alongi D.M., 2002. Present state and future of the world’s mangrove forests. Environmental Conservation, 29, 331-349. Doi: 10.1017/S0376892902000231 Alongi D.M., 2015. The impact of climate change on mangrove forests. Curr. Clim. Change Rep., 1, 30-39. Doi: 10.1007/s404641-015-0002-x. Anderson F., Al-Thani N., 2016. Effect of sea level rise and groundwater withdrawal on seawater intrusion in the Gulf Coast aquifer: Implications for agriculture. Journal of Geoscience and Environment Protection, 4, 116-124. Doi: 10.4236/gep.2016.44015. Anguelovski I., Chu E., Carmin J., 2014. Variations in approaches to urban climate adaptation: Experiences and experimentation from the global South. Global Environmental Change, 27, 156-167. Doi: 10.1016/j.gloenvcha.2014.05.010. Arustienè J., Kriukaitè J., Satkunas J., Gregorauskas M., 2013. Climate change and groundwater - From modelling to some adaptation means in example of Klaipèda region, Lithuania. In: Climate change adaptation in practice. P. Schmidt-Thomé, J. Klein Eds. John Wiley and Sons Ltd., Chichester, UK., 157-169. Bamber J.L., Aspinall W.P., Cooke R.M., 2016. A commentary on “how to interpret expert judgement assessments of twenty-first century sea-level rise” by Hylke de Vries and Roderik S.W. Van de Wal. Climatic Change, 137, 321-328. Doi: 10.1007/s10584-016-1672-7. Barnes C., 2014. Coastal population vulnerability to sea level rise and tropical cyclone intensification under global warming. BSc-thesis. Department of Geography, University of Lethbridge, Alberta Canada. Be T.T., Sinh B.T., Miller F., 2007. Challenges to sustainable development in the Mekong Delta: Regional and national policy issues and research needs. The Sustainable Mekong Research Network, Bangkok, Thailand, 1-210. Bellard C., Leclerc C., Courchamp F., 2014. Impact of sea level rise on 10 insular biodiversity hotspots. Global Ecology and Biogeography, 23, 203-212. Doi: 10.1111/geb.12093. Berg H., Söderholm A.E., Sönderström A.S., Nguyen Thanh Tam, 2017. Recognizing wetland ecosystem services for sustainable rice farming in the Mekong delta, Vietnam. Sustainability Science, 12, 137-154. Doi: 10.1007/s11625-016-0409-x. Bilskie M.V., Hagen S.C., Medeiros S.C., Passeri D.L., 2014. Dynamics of sea level rise and coastal flooding on a changing landscape. Geophysical Research Letters, 41, 927-934. Doi: 10.1002/2013GL058759. Binh T.N.K.D., Vromant N., Hung N.T., Hens L., Boon E.K., 2005. Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau penisula, Vietnam. Environment, Development and Sustainability, 7, 519-536. Doi: 10.1007/s10668-004-6001-z. Blankespoor B., Dasgupta S., Laplante B., 2014. Sea-level rise and coastal wetlands. Ambio, 43, 996- 005.Doi: 10.1007/s13280-014-0500-4. Brockway R., Bowers D., Hoguane A., Dove V., Vassele V., 2006. A note on salt intrusion in funnel shaped estuaries: Application to the Incomati estuary, Mozambique.Estuarine, Coastal and Shelf Science, 66, 1-5. Doi: 10.1016/j.ecss.2005.07.014. Cannaby H., Palmer M.D., Howard T., Bricheno L., Calvert D., Krijnen J., Wood R., Tinker J., Bunney C., Harle J., Saulter A., O’Neill C., Bellingham C., Lowe J., 2015. Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore. Ocean Sci. Discuss, 12, 2955-3001. Doi: 10.5194/osd-12-2955-2015. Carraro C., Favero A., Massetti E., 2012. Investment in public finance in a green, low carbon economy. Energy Economics, 34, S15-S18. Castan-Broto V., Bulkeley H., 2013. A survey ofurban climate change experiments in 100 cities. Global Environmental Change, 23, 92-102. Doi: 10.1016/j.gloenvcha.2012.07.005. Cazenave A., Le Cozannet G., 2014. Sea level rise and its coastal impacts. GeoHealth, 2, 15-34. Doi: 10.1002/2013EF000188. Chu M.L., Guzman J.A., Munoz-Carpena R., Kiker G.A., Linkov I., 2014. A simplified approach for simulating changes in beach habitat due to the combined effects of long-term sea level rise, storm erosion and nourishment. Environmental modelling and software, 52, 111-120. Doi.org/10.1016/j.envcsoft.2013.10.020. Church J.A. et al., 2013. Sea level change. In: Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of Intergovernmental Panel on Climate Change. Eds: Stocker T.F., Qin D., Plattner G.-K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex V., Midgley P.M., Cambridge University Press, Cambridge, UK. Connell J., 2016. Last days of the Carteret Islands? Climate change, livelihoods and migration on coral atolls. Asia Pacific Viewpoint, 57, 3-15. Doi: 10.1111/apv.12118. Dasgupta S., Laplante B., Meisner C., Wheeler, Yan J., 2009. The impact of sea level rise on developing countries: A comparative analysis. Climatic Change, 93, 379-388. Doi: 10.1007/s 10584-008-9499-5. Delbeke J., Vis P., 2015. EU climate policy explained, 136p. Routledge, Oxon, UK. DiGeorgio M., 2015. Bargaining with disaster: Flooding, climate change, and urban growth ambitions in QuyNhon, Vietnam. Public Affairs, 88, 577-597. Doi: 10.5509/2015883577. Do Minh Duc, Yasuhara K., Nguyen Manh Hieu, 2015. Enhancement of coastal protection under the context of climate change: A case study of Hai Hau coast, Vietnam. Proceedings of the 10th Asian Regional Conference of IAEG, 1-8. Do Minh Duc, Yasuhara K., Nguyen Manh Hieu, Lan Nguyen Chau, 2017. Climate change impacts on a large-scale erosion coast of Hai Hau district, Vietnam and the adaptation. Journal of Coastal Conservation, 21, 47-62. Donner S.D., Webber S., 2014. Obstacles to climate change adaptation decisions: A case study of sea level rise; and coastal protection measures in Kiribati. Sustainability Science, 9, 331-345. Doi: 10.1007/s11625-014-0242-z. Driessen P.P.J., Hegger D.L.T., Bakker M.H.N., Van Renswick H.F.M.W., Kundzewicz Z.W., 2016. Toward more resilient flood risk governance. Ecology and Society, 21, 53-61. Doi: 10.5751/ES-08921-210453. Duangyiwa C., Yu D., Wilby R., Aobpaet A., 2015. Coastal flood risks in the Bangkok Metropolitan region, Thailand: Combined impacts on land subsidence, sea level rise and storm surge. American Geophysical Union, Fall meeting 2015, abstract#NH33C-1927. Duarte C.M., Losada I.J., Hendriks I.E., Mazarrasa I., Marba N., 2013. The role of coastal plant communities for climate change mitigation and adaptation. Nature Climate Change, 3, 961-968. Doi: 10.1038/nclimate1970. Erban L.E., Gorelick S.M., Zebker H.A., 2014. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. Environmental Research Letters, 9, 1-20. Doi: 10.1088/1748-9326/9/8/084010. FAO - Food and Agriculture Organisation, 2007.The world’s mangroves 1980-2005. FAO Forestry Paper, 153, Rome, Italy. Farbotko C., 2010. Wishful sinking: Disappearing islands, climate refugees and cosmopolitan experimentation. Asia Pacific Viewpoint, 51, 47-60. Doi: 10.1111/j.1467-8373.2010.001413.x. Goltermann D., Ujeyl G., Pasche E., 2008. Making coastal cities flood resilient in the era of climate change. Proceedings of the 4th International Symposium on flood defense: Managing flood risk, reliability and vulnerability, 148-1-148-11. Toronto, Canada. Gong W., Shen J., 2011. The response of salt intrusion to changes in river discharge and tidal mixing during the dry season in the Modaomen Estuary, China.Continental Shelf Research, 31, 769-788. Doi: 10.1016/j.csr.2011.01.011. Gosian L., 2014. Protect the world’s deltas. Nature, 516, 31-34. Graham S., Barnett J., Fincher R., Mortreux C., Hurlimann A., 2015. Towards fair outcomes in adaptation to sea-level rise. Climatic Change, 130, 411-424. Doi: 10.1007/s10584-014-1171-7. COASTRES-D-12-00175.1. Güneralp B., Güneralp I., Liu Y., 2015. Changing global patterns of urban expoàsure to flood and drought hazards. Global Environmental Change, 31, 217-225. Doi: 10.1016/j.gloenvcha.2015.01.002. Hallegatte S., Green C., Nicholls R.J., Corfee-Morlot J., 2013. Future flood losses in major coastal cities. Nature Climate Change, 3, 802-806. Doi: 10.1038/nclimate1979. Hamlington B.D., Strassburg M.W., Leben R.R., Han W., Nerem R.S., Kim K.-Y., 2014. Uncovering an anthropogenic sea-level rise signal in the Pacific Ocean. Nature Climate Change, 4, 782-785. Doi: 10.1038/nclimate2307. Hashimoto T.R., 2001. Environmental issues and recent infrastructure development in the Mekong Delta: Review, analysis and recommendations with particular reference to large-scale water control projects and the development of coastal areas. Working paper series (Working paper No. 4). Australian Mekong Resource Centre, University of Sydney, Australia, 1-70. Hibbert F.D., Rohling E.J., Dutton A., Williams F.H., Chutcharavan P.M., Zhao C., Tamisiea M.E., 2016. Coral indicators of past sea-level change: A global repository of U-series dated benchmarks. Quaternary Science Reviews, 145, 1-56. Doi: 10.1016/j.quascirev.2016.04.019. Hinkel J., Lincke D., Vafeidis A., Perrette M., Nicholls R.J., Tol R.S.J., Mazeion B., Fettweis X., Ionescu C., Levermann A., 2014. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111, 3292-3297. Doi: 10.1073/pnas.1222469111. Hinkel J., Nicholls R.J., Tol R.S.J., Wang Z.B., Hamilton J.M., Boot G., Vafeidis A.T., McFadden L., Ganapolski A., Klei R.J.Y., 2013. A global analysis of erosion of sandy beaches and sea level rise: An application of DIVA. Global and Planetary Change, 111, 150-158. Doi: 10.1016/j.gloplacha.2013.09.002. Huong H.T.L., Pathirana A., 2013. Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrol. Earth Syst. Sci., 17, 379-394. Doi: 10.5194/hess-17-379-2013. Hurlimann A., Barnett J., Fincher R., Osbaldiston N., Montreux C., Graham S., 2014. Urban planning and sustainable adaptation to sea-level rise. Landscape and Urban Planning, 126, 84-93. Doi: 10.1016/j.landurbplan.2013.12.013. IMHEN-Vietnam Institute of Meteorology, Hydrology and Environment, 2011. Climate change vulnerability and risk assessment study for Ca Mau and KienGiang provinces, Vietnam. Hanoi, Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN), 250p. IMHEN-Vietnam Institute of Meteorology, Hydrology and Environment, Ca Mau PPC, 2011. Climate change impact and adaptation study in The Mekong Delta - Part A: Ca Mau Atlas. Hanoi, Vietnam: Institute of Meteorology, Hydrology and Environment (IMHEN), 48p. IPCC-Intergovernmental Panel on Climate Change, 2014. Fifth assessment report. Cambridge University Press, Cambridge, UK. Jevrejeva S., Jackson L.P., Riva R.E.M., Grinsted A., Moore J.C., 2016. Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113, 13342-13347. Doi: 10.1073/pnas.1605312113. Junk W.J., AN S., Finlayson C.M., Gopal B., Kvet J., Mitchell S.A., Mitsch W.J., Robarts R.D., 2013. Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquatic Science, 75, 151-167. Doi: 10.1007/s00027-012-0278-z. Jordan A., Rayner T., Schroeder H., Adger N., Anderson K., Bows A., Le Quéré C., Joshi M., Mander S., Vaughan N., Whitmarsh L., 2013. Going beyond two degrees? The risks and opportunities of alternative options. Climate Policy, 13, 751-769. Doi: 10.1080/14693062.2013.835705. Kelly P.M., Adger W.N., 2000. Theory and practice in assessing vulnerability to climate change and facilitating adaptation. Climatic Change, 47, 325-352. Doi: 10.1023/A:1005627828199. Kirwan M.L., Megonigal J.P., 2013. Tidal wetland stability in the face of human impacts and sea-level rice. Nature, 504, 53-60. Doi: 10.1038/nature12856. Koerth J., Vafeidis A.T., Hinkel J., Sterr H., 2013. What motivates coastal households to adapt pro actively to sea-level rise and increased flood risk? Regional Environmental Change, 13, 879-909. Doi: 10.1007/s10113-12-399-x. Kontgis K., Schneider A., Fox J;,Saksena S., Spencer J.H., Castrence M., 2014. Monitoring peri urbanization in the greater Ho Chi Minh City metropolitan area. Applied Geography, 53, 377-388. Doi: 10.1016/j.apgeogr.2014.06.029. Kopp R.E., Horton R.M., Little C.M., Mitrovica J.X., Oppenheimer M., Rasmussen D.J., Strauss B.H., Tebaldi C., 2014. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future, 2, 383-406. Doi: 10.1002/2014EF000239. Kuenzer C., Bluemel A., Gebhardt S., Quoc T., Dech S., 2011. Remote sensing of mangrove ecosystems: A review.Remote Sensing, 3, 878-928. Doi: 10.3390/rs3050878. Lacerda G.B.M., Silva C., Pimenteira C.A.P., Kopp Jr. R.V., Grumback R., Rosa L.P., de Freitas M.A.V., 2013. Guidelines for the strategic management of flood risks in industrial plant oil in the Brazilian coast: Adaptive measures to the impacts of sea level rise. Mitigation and Adaptation Strategies for Global Change, 19, 104-1062. Doi: 10.1007/s11027-013-09459-x. Lam Dao Nguyen, Pham Van Bach, Nguyen Thanh Minh, Pham Thi Mai Thy, Hoang Phi Hung, 2011. Change detection of land use and river bank in Mekong Delta, Vietnam using time series remotely sensed data. Journal of Resources and Ecology, 2, 370-374. Doi: 10.3969/j.issn.1674-764x.2011.04.011. Lang N.T., Ky B.X., Kobayashi H., Buu B.C., 2004. Development of salt tolerant varieties in the Mekong delta. JIRCAS Project, Can Tho University, Can Tho, Vietnam, 152. Le Cozannet G., Rohmer J., Cazenave A., Idier D., Van de Wal R., de Winter R., Pedreros R., Balouin Y., Vinchon C., Oliveros C., 2015. Evaluating uncertainties of future marine flooding occurrence as sea-level rises. Environmental Modelling and Software, 73, 44-56. Doi: 10.1016/j.envsoft.2015.07.021. Le Cozannet G., Manceau J.-C., Rohmer J., 2017. Bounding probabilistic sea-level projections with the framework of the possible theory. Environmental Letters Research, 12, 12-14. Doi.org/10.1088/1748-9326/aa5528.Chikamoto Y., 2014. Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nature Climate Change, 4, 888-892. Doi: 10.1038/nclimate2330. Lovelock C.E., Cahoon D.R., Friess D.A., Gutenspergen G.R., Krauss K.W., Reef R., Rogers K., Saunders M.L., Sidik F., Swales A., Saintilan N., Le Xuan Tuyen, Tran Triet, 2015. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526, 559-563. Doi: 10.1038/nature15538. MA Millennium Ecosystem Assessment, 2005. Ecosystems and human well-being: Current state and trends. Island Press, Washington DC, 266p. Masterson J.P., Fienen M.N., Thieler E.R., Gesch D.B., Gutierrez B.T., Plant N.G., 2014. Effects of sea level rise on barrier island groundwater system dynamics - ecohydrological implications. Ecohydrology, 7, 1064-1071. Doi: 10.1002/eco.1442. McGanahan G., Balk D., Anderson B., 2007. The rising tide: Assessing the risks of climate changes and human settlements in low elevation coastal zones.Environment and urbanization, 19, 17-37. Doi: 10.1177/095624780707960. McIvor A., Möller I., Spencer T., Spalding M., 2012. Reduction of wind and swell waves by mangroves. The Nature Conservancy and Wetlands International, 1-27. Merryn T., Pidgeon N., Whitmarsh L., Ballenger R., 2016. Expert judgements of sea-level rise at the local scale. Journal of Risk Research, 19, 664-685. Doi.org/10.1080/13669877.2015.1043568. Monioudi I.N., Velegrakis A.F., Chatzipavlis A.E., Rigos A., Karambas T., Vousdoukas M.I., Hasiotis T., Koukourouvli N., Peduzzi P., Manoutsoglou E., Poulos S.E., Collins M.B., 2017. Assessment of island beach erosion due to sea level rise: The case of the Aegean archipelago (Eastern Mediterranean). Nat. Hazards Earth Syst. Sci., 17, 449-466. Doi: 10.5194/nhess-17-449-2017. MONRE - Ministry of Natural Resources and Environment, 2016. Scenarios of climate change and sea level rise for Vietnam. Publishing House of Environmental Resources and Maps Vietnam, Hanoi, 188p. Montz B.E., Tobin G.A., Hagelman III R.R., 2017. Natural hazards. Explanation and integration. The Guilford Press, NY, 445p. Morgan L.K., Werner A.D., 2014. Water intrusion vulnerability for freshwater lenses near islands. Journal of Hydrology, 508, 322-327. Doi: 10.1016/j.jhydrol.2013.11.002. Muis S., Güneralp B., Jongman B., Aerts J.C.H.J., Ward P.J., 2015. Science of the Total Environment, 538, 445-457. Doi: 10.1016/j.scitotenv.2015.08.068. Murray N.J., Clemens R.S., Phinn S.R., Possingham H.P., Fuller R.A., 2014. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Frontiers in Ecology and Environment, 12, 267-272. Doi: 10.1890/130260. Neumann B., Vafeidis A.T., Zimmermann J., Nicholls R.J., 2015a. Future coastal population growth and exposure to sea-level rise and coastal flooding. A global assessment. Plos One, 10, 1-22. Doi: 10.1371/journal.pone.0118571. Nguyen A. Duoc, Savenije H. H., 2006. Salt intrusion in multi-channel estuaries: a case study in the Mekong Delta, Vietnam. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 10, 743-754. Doi: 10.5194/hess-10-743-2006. Nguyen An Thinh, Nguyen Ngoc Thanh, Luong Thi Tuyen, Luc Hens, 2017. Tourism and beach erosion: Valuing the damage of beach erosion for tourism in the Hoi An, World Heritage site. Journal of Environment, Development and Sustainability. Nguyen An Thinh, Luc Hens (Eds.), 2018. Human ecology of climate change associated disasters in Vietnam: Risks for nature and humans in lowland and upland areas. Springer Verlag, Berlin.Nguyen An Thinh, Vu Anh Dung, Vu Van Phai, Nguyen Ngoc Thanh, Pham Minh Tam, Nguyen Thi Thuy Hang, Le Trinh Hai, Nguyen Viet Thanh, Hoang Khac Lich, Vu Duc Thanh, Nguyen Song Tung, Luong Thi Tuyen, Trinh Phuong Ngoc, Luc Hens, 2017. Human ecological effects of tropical storms in the coastal area of Ky Anh (Ha Tinh, Vietnam). Environ Dev Sustain, 19, 745-767. Doi: 10.1007/s/10668-016-9761-3. Nguyen Van Hoang, 2017. Potential for desalinization of brackish groundwater aquifer under a background of rising sea level via salt-intrusion prevention river gates in the coastal area of the Red River delta, Vietnam. Environment, Development and Sustainability. Nguyen Tho, Vromant N., Nguyen Thanh Hung, Hens L., 2008. Soil salinity and sodicity in a shrimp farming coastal area of the Mekong Delta, Vietnam. Environmental Geology, 54, 1739-1746. Doi: 10.1007/s00254-007-0951-z. Nguyen Thang T.X., Woodroffe C.D., 2016. Assessing relative vulnerability to sea-level rise in the western part of the Mekong River delta. Sustainability Science, 11, 645-659. Doi: 10.1007/s11625-015-0336-2. Nicholls N.N., Hoozemans F.M.J., Marchand M., Analyzing flood risk and wetland losses due to the global sea-level rise: Regional and global analyses.Global Environmental Change, 9, S69-S87. Doi: 10.1016/s0959-3780(99)00019-9. Phan Minh Thu, 2006. Application of remote sensing and GIS tools for recognizing changes of mangrove forests in Ca Mau province. In Proceedings of the International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Ho Chi Minh City, Vietnam, 9-11 November, 1-17. Reise K., 2017. Facing the third dimension in coastal flatlands.Global sea level rise and the need for coastal transformations. Gaia, 26, 89-93. Renaud F.G., Le Thi Thu Huong, Lindener C., Vo Thi Guong, Sebesvari Z., 2015. Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre province, Mekong Delta. Climatic Change, 133, 69-84. Doi: 10.1007/s10584-014-1113-4. Serra P., Pons X., Sauri D., 2008. Land cover and land use in a Mediterranean landscape. Applied Geography, 28, 189-209. Shearman P., Bryan J., Walsh J.P., 2013.Trends in deltaic change over three decades in the Asia-Pacific Region. Journal of Coastal Research, 29, 1169-1183. Doi: 10.2112/JCOASTRES-D-12-00120.1. SIWRR-Southern Institute of Water Resources Research, 2016. Annual Report. Ministry of Agriculture and Rural Development, Ho Chi Minh City, 1-19. Slangen A.B.A., Katsman C.A., Van de Wal R.S.W., Vermeersen L.L.A., Riva R.E.M., 2012. Towards regional projections of twenty-first century sea-level change based on IPCC RES scenarios. Climate Dynamics, 38, 1191-1209. Doi: 10.1007/s00382-011-1057-6. Spencer T., Schuerch M., Nicholls R.J., Hinkel J., Lincke D., Vafeidis A.T., Reef R., McFadden L., Brown S., 2016. Global coastal wetland change under sea-level rise and related stresses: The DIVA wetland change model. Global and Planetary Change, 139, 15-30. Doi:10.1016/j.gloplacha.2015.12.018. Stammer D., Cazenave A., Ponte R.M., Tamisiea M.E., 2013. Causes of contemporary regional sea level changes. Annual Review of Marine Science, 5, 21-46. Doi: 10.1146/annurev-marine-121211-172406. Tett P., Mee L., 2015. Scenarios explored with Delphi. In: Coastal zones ecosystems services. Eds., Springer, Berlin, Germany, 127-144. Tran Hong Hanh, 2017. Land use dynamics, its drivers and consequences in the Ca Mau province, Mekong delta, Vietnam. PhD dissertation, 191p. VUBPRESS Brussels University Press, ISBN 9789057186226, Brussels, Belgium. Tran Thuc, Nguyen Van Thang, Huynh Thi Lan Huong, Mai Van Khiem, Nguyen Xuan Hien, Doan Ha Phong, 2016. Climate change and sea level rise scenarios for Vietnam. Ministry of Natural resources and Environment. Hanoi, Vietnam. Tran Hong Hanh, Tran Thuc, Kervyn M., 2015. Dynamics of land cover/land use changes in the Mekong Delta, 1973-2011: A remote sensing analysis of the Tran Van Thoi District, Ca Mau province, Vietnam. Remote Sensing, 7, 2899-2925. Doi: 10.1007/s00254-007-0951-z Van Lavieren H., Spalding M., Alongi D., Kainuma M., Clüsener-Godt M., Adeel Z., 2012. Securing the future of Mangroves. The United Nations University, Okinawa, Japan, 53, 1-56. Water Resources Directorate. Ministry of Agriculture and Rural Development, 2016. Available online: http://www.tongcucthuyloi.gov.vn/Tin-tuc-Su-kien/Tin-tuc-su-kien-tong-hop/catid/12/item/2670/xam-nhap-man-vung-dong-bang-song-cuu-long--2015---2016---han-han-o-mien-trung--tay-nguyen-va-giai-phap-khac-phuc. Last accessed on: 30/9/2016. Webster P.J., Holland G.J., Curry J.A., Chang H.-R., 2005. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844-1846. Doi: 10.1126/science.1116448. Were K.O., Dick O.B., Singh B.R., 2013. Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage Basin, Kenya. Applied Geography, 41, 75-86. Williams G.A., Helmuth B., Russel B.D., Dong W.-Y., Thiyagarajan V., Seuront L., 2016. Meeting the climate change challenge: Pressing issues in southern China an SE Asian coastal ecosystems. Regional Studies in Marine Science, 8, 373-381. Doi: 10.1016/j.rsma.2016.07.002. Woodroffe C.D., Rogers K., McKee K.L., Lovdelock C.E., Mendelssohn I.A., Saintilan N., 2016. Mangrove sedimentation and response to relative sea-level rise. Annual Review of Marine Science, 8, 243-266. Doi: 10.1146/annurev-marine-122414-034025.

Hamsters were exposed to sodium arsenite (173 mg As/L) in drinking water for 6 days. Equal amounts of proteins from urinary bladder or liver extracts of control and arsenic-treated hamsters were labeled with Cy3 and Cy5 dyes, respectively. After differential in gel electrophoresis and analysis by the DeCyder software, several protein spots were found to be down-regulated and several were up regulated. Our experiments indicated that in the bladder tissues of hamsters exposed to arsenite, transgelin was down-regulated and GST-pi was up-regulated. The loss of transgelin expression has been reported to be an important early event in tumor progression and a diagnostic marker for cancer development [29-32]. Down-regulation of transgelin expression may be associated with the carcinogenicity of inorganic arsenic in the urinary bladder. In the liver of arsenite-treated hamsters, ornithine aminotransferase was up-regulated, and senescence marker protein 30 and fatty acid binding protein were down-regulated. The volume ratio changes of these proteins in the bladder and liver of hamsters exposed to arsenite were significantly different than that of control hamsters. Introduction Chronic exposure to inorganic arsenic can cause cancer of the skin, lungs, urinary bladder, kidneys, and liver [1-6]. The molecular mechanisms of the carcinogenicity and toxicity of inorganic arsenic are not well understood [7-9). Humans chronically exposed to inorganic arsenic excrete MMA(V), DMA(V) and the more toxic +3 oxidation state arsenic biotransformants MMA(III) and DMA (III) in their urine [10, 11], which are carcinogen [12]· After injection of mice with sodium arsenate, the highest concentrations of the very toxic MMA(III) and DMA(III) were in the kidneys and urinary bladder tissue, respectively, as shown by experiments of Chowdhury et al [13]. Many mechanisms of arsenic toxicity and carcinogenicity have been suggested [1, 7, 14] including chromosome abnormalities [15], oxidative stress [16, 17], altered growth factors [18], cell proliferation [19], altered DNA repair [20], altered DNA methylation patterns [21], inhibition of several key enzymes [22], gene amplification [23] etc. Some of these mechanisms result in alterations in protein expression. Methods for analyzing multiple proteins have advanced greatly in the last several years. In particularly, mass spectrometry (MS) and tandem MS (MS/MS) are used to analyze peptides following protein isolation using two-dimensional (2-D) gel electrophoresis and proteolytic digestion [24]. In the present study, Differential In Gel Electrophoresis (DIGE) coupled with Mass Spectrometry (MS) has been used to study some of the proteomic changes in the urinary bladder and liver of hamsters exposed to sodium arsenite in their drinking water. Our results indicated that transgelin was down-regulated and GST-pi was up-regulated in the bladder tissues. In the liver tissues ornithine aminotransferase was up-regulated, and senescence marker protein 30, and fatty acid binding protein were down-regulated. Materials and Methods Chemicals Tris, Urea, IPG strips, IPG buffer, CHAPS, Dry Strip Cover Fluid, Bind Silane, lodoacetamide, Cy3 and Cy5 were from GE Healthcare (formally known as Amersham Biosciences, Uppsala, Sweden). Thiourea, glycerol, SDS, DTT, and APS were from Sigma-Aldrich (St. Louis, MO, USA). Glycine was from USB (Cleveland, OH, USA). Acrylamide Bis 40% was from Bio-Rad (Hercules, CA, USA). All other chemicals and biochemicals used were of analytical grade. All solutions were made with Milli-Q water. Animals Male hamsters (Golden Syrian), 4 weeks of age, were purchased from Harlan Sprague Dawley, USA. Upon arrival, hamsters were acclimated in the University of Arizona animal care facility for at least 1 week and maintained in an environmentally controlled animal facility operating on a 12-h dark/12-h light cycle and at 22-24°C. They were provided with Teklad (Indianapolis, IN) 4% Mouse/Rat Diet # 7001 and water, ad libitum, throughout the acclimation and experimentation periods. Sample preparation and labelling Hamsters were exposed to sodium arsenite (173 mg) in drinking water for 6 days and the control hamsters were given tap water. On the 6th day hamsters were decapitated rapidly by guillotine. Urinary bladder tissues and liver were removed, blotted on tissue papers (Kimtech Science, Precision Wipes), and weighed. Hamster urinary bladder or liver tissues were homogenized in lysis buffer (30mMTris, 2M thiourea, 7M urea, and 4% w/w CHAPS adjusted to pH 8.5 with dilute HCI), at 4°C using a glass homogenizer and a Teflon coated steel pestle; transferred to a 5 ml acid-washed polypropylene tube, placed on ice and sonicated 3 times for 15 seconds. The sonicate was centrifuged at 12,000 rpm for 10 minutes at 4°C. Small aliquots of the supernatants were stored at -80°C until use (generally within one week). Protein concentration was determined by the method of Bradford [25] using bovine serum albumin as a standard. Fifty micrograms of lysate protein was labeled with 400 pmol of Cy3 Dye (for control homogenate sample) and Cy5 Dye (for arsenic-treated urinary bladder or liver homogenate sample). The samples containing proteins and dyes were incubated for 30 min on ice in the dark. To stop the labeling reaction, 1uL of 10 mM lysine was added followed by incubation for 10 min on ice in the dark. To each of the appropriate dye-labeled protein samples, an additional 200 ug of urinary bladderor liver unlabeled protein from control hamster sample or arsenic-treated hamster sample was added to the appropriate sample. Differentially labeled samples were combined into a single Microfuge tube (total protein 500 ug); protein was mixed with an equal volume of 2x sample buffer [2M thiourea, 7M urea, pH 3-10 pharmalyte for isoelectric focusing 2% (v/v), DTT 2% (w/v), CHAPS 4% (w/v)]; and was incubated on ice in the dark for 10 min. The combined samples containing 500 ug of total protein were mixed with rehydration buffer [CHAPS 4% (w/v), 8M urea, 13mM DTT, IPG buffer (3-10) 1% (v/v) and trace amount of bromophenol blue]. The 450 ul sample containing rehydration buffer was slowly pipetted into the slot of the ImmobilinedryStripReswelling Tray and any large bubbles were removed. The IPG strip (linear pH 3-10, 24 cm) was placed (gel side down) into the slot, covered with drystrip cover fluid (Fig. 1), and the lid of the Reswelling Tray was closed. The ImmobillineDryStrip was allowed to rehydrate at room temperature for 24 hours. First dimension Isoelectric focusing (IEF) The labeled sample was loaded using the cup loading method on universal strip holder. IEF was then carried out on EttanIPGphor II using multistep protocol (6 hr @ 500 V, 6 hr @ 1000 V, 8 hr @ 8000 V). The focused IPG strip was equilibrated in two steps (reduction and alkylation) by equilibrating the strip for 10 min first in 10 ml of 50mM Tris (pH 8.8), 6M urea, 30% (v/v) glycerol, 2% (w/v) SDS, and 0.5% (w/v) DTT, followed by another 10 min in 10 ml of 50mM Tris (pH 8.8), 6M urea, 30% (v/v) glycerol, 2% (w/v) SDS, and 4.5% (w/v) iodoacetamide to prepare it for the second dimension electrophoresis. Second dimension SDS-PAGE The equilibrated IPG strip was used for protein separation by 2D-gel electrophoresis (DIGE). The strip was sealed at the top of the acrylamide gel for the second dimension (vertical) (12.5% polyacrylamide gel, 20x25 cm x 1.5 mm) with 0.5% (w/v) agarose in SDS running buffer [25 mMTris, 192 mM Glycine, and 0.1% (w/v) SDS]. Electrophoresis was performed in an Ettan DALT six electrophoresis unit (Amersham Biosciences) at 1.5 watts per gel, until the tracking dye reached the anodic end of the gel. Image analysis and post-staining The gel then was imaged directly between glass plates on the Typhoon 9410 variable mode imager (Sunnyvale, CA, USA) using optimal excitation/emission wavelength for each DIGE fluor: Cy3 (532/580 nm) and Cy5 (633/670 nm). The DIGE images were previewed and checked with Image Quant software (GE Healthcare) where all the two separate gel images could be viewed as a single gel image. DeCyde v.5.02 was used to analyze the DIGE images as described in the Ettan DIGE User Manual (GE Healthcare). The appropriate up-/down regulated spots were filtered based on an average volume ratio of ± over 1.2 fold. After image acquisition, the gel was fixed overnight in a solution containing 40% ethanol and 10% acetic acid. The fixed gel was stained with SyproRuby (BioRad) according to the manufacturer protocol (Bio-Rad Labs., 2000 Alfred Nobel Drive, Hercules, CA 94547). Identification of proteins by MS Protein spot picking and digestion Sypro Ruby stained gels were imaged using an Investigator ProPic and HT Analyzer software, both from Genomic Solutions (Ann Arbor, MI). Protein spots of interest that matched those imaged using the DIGE Cy3/Cy5 labels were picked robotically, digested using trypsin as described previously [24] and saved for mass spectrometry identification. Liquid chromatography (LC)- MS/MS analysis LC-MS/MS analyses were carried out using a 3D quadrupole ion trap massspectrometer (ThermoFinnigan LCQ DECA XP PLUS; ThermoFinnigan, San Jose, CA) equipped with a Michrom Paradigm MS4 HPLC (MichromBiosources, Auburn, CA) and a nanospray source, or with a linear quadrupole ion trap mass spectrometer (ThermoFinnigan LTQ), also equipped with a Michrom MS4 HPLC and a nanospray source. Peptides were eluted from a 15 cm pulled tip capillary column (100 um I.D. x 360 um O.D.; 3-5 um tip opening) packed with 7 cm Vydac C18 (Vydac, Hesperia, CA) material (5 µm, 300 Å pore size), using a gradient of 0-65% solvent B (98% methanol/2% water/0.5% formic acid/0.01% triflouroacetic acid) over a 60 min period at a flow rate of 350 nL/min. The ESI positive mode spray voltage was set at 1.6 kV, and the capillary temperature was set at 200°C. Dependent data scanning was performed by the Xcalibur v 1.3 software on the LCQ DECA XP+ or v 1.4 on the LTQ [27], with a default charge of 2, an isolation width of 1.5 amu, an activation amplitude of 35%, activation time of 50 msec, and a minimal signal of 10,000 ion counts (100 ion counts on the LTQ). Global dependent data settings were as follows: reject mass width of 1.5 amu, dynamic exclusion enabled, exclusion mass width of 1.5 amu, repeat count of 1, repeat duration of a min, and exclusion duration of 5 min. Scan event series were included one full scan with mass range of 350-2000 Da, followed by 3 dependent MS/MS scans of the most intense ion. Database searching Tandem MS spectra of peptides were analyzed with Turbo SEQUEST, version 3.1 (ThermoFinnigan), a program that allows the correlation of experimental tandem MS data with theoretical spectra generated from known protein sequences. All spectra were searched against the latest version of the non redundant protein database from the National Center for Biotechnology Information (NCBI 2006; at that time, the database contained 3,783,042 entries). Statistical analysis The means and standard error were calculated. The Student's t-test was used to analyze the significance of the difference between the control and arsenite exposed hamsters. P values less than 0.05 were considered significant. The reproducibility was confirmed in separate experiments. Results Analysis of proteins expression After DIGE (Fig. 1), the gel was scanned by a Typhoon Scanner and the relative amount of protein from sample 1 (treated hamster) as compared to sample 2 (control hamster) was determined (Figs. 2, 3). A green spot indicates that the amount of protein from sodium arsenite-treated hamster sample was less than that of the control sample. A red spot indicates that the amount of protein from the sodium arsenite-treated hamster sample was greater than that of the control sample. A yellow spot indicates sodium arsenite-treated hamster and control hamster each had the same amount of that protein. Several protein spots were up-regulated (red) or down-regulated (green) in the urinary bladder samples of hamsters exposed to sodium arsenite (173 mg As/L) for 6 days as compared with the urinary bladder of controls (Fig. 2). In the case of liver, several protein spots were also over-expressed (red) or under-expressed (green) for hamsters exposed to sodium arsenite (173 mg As/L) in drinking water for 6 days (Fig. 3). The urinary bladder samples were collected from the first and second experiments in which hamsters were exposed to sodium arsenite (173 mg As/L) in drinking water for 6 days and the controls were given tap water. The urinary bladder samples from the 1st and 2nd experiments were run 5 times in DIGE gels on different days. The protein expression is shown in Figure 2 and Table 1. The liver samples from the 1st and 2nd experiments were also run 3 times in DIGE gels on different days. The proteins expression were shown in Figure 3 and Table 2. The volume ratio changed of the protein spots in the urinary bladder and liver of hamsters exposed to arsenite were significantly differences than that of the control hamsters (Table 1 and 2). Protein spots identified by LC-MS/MS Bladder The spots of interest were removed from the gel, digested, and their identities were determined by LC-MS/MS (Fig. 2 and Table 1). The spots 1, 2, & 3 from the gel were analyzed and were repeated for the confirmation of the results (experiments; 173 mg As/L). The proteins for the spots 1, 2, and 3 were identified as transgelin, transgelin, and glutathione S-transferase Pi, respectively (Fig. 2). Liver We also identified some of the proteins in the liver samples of hamsters exposed to sodium arsenite (173 mg As/L) in drinking water for 6 days (Fig. 3). The spots 4, 5, & 6 from the gels were analyzed and were repeated for the confirmation of the results. The proteins for the spots 4, 5, and 6 were identified as ornithine aminotransferase, senescence marker protein 30, and fatty acid binding protein, respectively (Fig. 3) Discussion The identification and functional assignment of proteins is helpful for understanding the molecular events involved in disease. Weexposed hamsters to sodium arsenite in drinking water. Controls were given tap water. DIGE coupled with LC-MS/MS was then used to study the proteomic change in arsenite-exposed hamsters. After electrophoresis DeCyder software indicated that several protein spots were down-regulated (green) and several were up-regulated (red). Our overall results as to changes and functions of the proteins we have studied are summarized in Table 3. Bladder In the case of the urinary bladder tissue of hamsters exposed to sodium arsenite (173 mg As/L) in drinking water for 6 days, transgelin was down-regulated and GST-pi was up-regulated. This is the first evidence that transgelin is down-regulated in the bladders of animals exposed to sodium arsenite. Transgelin, which is identical to SM22 or WS3-10, is an actin cross linking/gelling protein found in fibroblasts and smooth muscle [28, 29]. It has been suggested that the loss of transgelin expression may be an important early event in tumor progression and a diagnostic marker for cancer development [30-33]. It may function as a tumor suppressor via inhibition of ARA54 (co-regulator of androgen receptor)-enhanced AR (androgen receptor) function. Loss of transgelin and its suppressor function in prostate cancer might contribute to the progression of prostate cancer [30]. Down-regulation of transgelin occurs in the urinary bladders of rats having bladder outlet obstruction [32]. Ras-dependent and Ras-independent mechanisms can cause the down regulation of transgelin in human breast and colon carcinoma cell lines and patient-derived tumorsamples [33]. Transgelin plays a role in contractility, possibly by affecting the actin content of filaments [34]. In our experiments loss of transgelin expression may be associated or preliminary to bladder cancer due to arsenic exposure. Arsenite is a carcinogen [1]. In our experiments, LC-MS/MS analysis showed that two spots (1 and 2) represent transgelin (Fig. 2 and Table 1). In human colonic neoplasms there is a loss of transgelin expression and the appearance of transgelin isoforms (31). GST-pi protein was up-regulated in the bladders of the hamsters exposed to sodium arsenite. GSTs are a large family of multifunctional enzymes involved in the phase II detoxification of foreign compounds [35]. The most abundant GSTS are the classes alpha, mu, and pi classes [36]. They participate in protection against oxidative stress [37]. GST-omega has arsenic reductase activity [38]. Over-expression of GST-pi has been found in colon cancer tissues [39]. Strong expression of GST-pi also has been found in gastric cancer [40], malignant melanoma [41], lung cancer [42], breast cancer [43] and a range of other human tumors [44]. GST-pi has been up-regulated in transitional cell carcinoma of human urinary bladder [45]. Up-regulation of glutathione – related genes and enzyme activities has been found in cultured human cells by sub lethal concentration of inorganic arsenic [46]. There is evidence that arsenic induces DNA damage via the production of ROS (reactive oxygen species) [47]. GST-pi may be over-expressed in the urinary bladder to protect cells against arsenic-induced oxidative stress. Liver In the livers of hamsters exposed to sodium arsenite, ornithine amino transferase was over-expressed, senescence marker protein 30 was under-expressed, and fatty acid binding protein was under-expressed. Ornithine amino transferase has been found in the mitochondria of many different mammalian tissues, especially liver, kidney, and small intestine [48]. Ornithine amino transferase knockdown inhuman cervical carcinoma and osteosarcoma cells by RNA interference blocks cell division and causes cell death [49]. It has been suggested that ornithine amino transferase has a role in regulating mitotic cell division and it is required for proper spindle assembly in human cancer cells [49]. Senescence marker protein-30 (SMP30) is a unique enzyme that hydrolyzes diisopropylphosphorofluoridate. SMP30, which is expressed mostly in the liver, protects cells against various injuries by stimulating membrane calcium-pump activity [50]. SMP30 acts to protect cells from apoptosis [51]. In addition it protects the liver from toxic agents [52]. The livers of SMP30 knockout mice accumulate phosphatidylethanolamine, cardiolipin, phosphatidyl-choline, phosphatidylserine, and sphingomyelin [53]. Liver fatty acid binding protein (L-FABP) also was down- regulated. Decreased liver fatty acid-binding capacity and altered liver lipid distribution hasbeen reported in mice lacking the L-FABP gene [54]. High levels of saturated, branched-chain fatty acids are deleterious to cells and animals, resulting in lipid accumulation and cytotoxicity. The expression of fatty acid binding proteins (including L-FABP) protected cells against branched-chain saturated fatty acid toxicity [55]. Limitations: we preferred to study the pronounced spots seen in DIGE gels. Other spots were visible but not as pronounced. Because of limited funds, we did not identify these others protein spots. In conclusion, urinary bladders of hamsters exposed to sodium arsenite had a decrease in the expression of transgelin and an increase in the expression of GST-pi protein. Under-expression of transgelin has been found in various cancer systems and may be associated with arsenic carcinogenicity [30-33). Inorganic arsenic exposure has resulted in bladder cancer as has been reported in the past [1]. Over-expression of GST-pi may protect cells against oxidative stress caused by arsenite. In the liver OAT was up regulated and SMP-30 and FABP were down regulated. These proteomic results may be of help to investigators studying arsenic carcinogenicity. The Superfund Basic Research Program NIEHS Grant Number ES 04940 from the National Institute of Environmental Health Sciences supported this work. Additional support for the mass spectrometry analyses was provided by grants from NIWHS ES06694, NCI CA023074 and the BIOS Institute of the University of Arizona. Acknowledgement The Author wants to dedicate this paper to the memory of his former supervisor Dr. H. VaskenAposhian who passed away in September 6, 2019. He was an emeritus professor of the Department of Molecular and Cellular Biology at the University of Arizona. This research work was done under his sole supervision and with his great contribution.I also would like to thanks Dr. George Tsapraills, Center of Toxicology, The University of Arizona for identification of proteins by MS. References NRC (National Research Council), Arsenic in Drinking Water, Update to the 1999 Arsenic in Drinking Water Report. National Academy Press, Washington, DC 2001. Hopenhayn-Rich, C.; Biggs, M. L.; Fuchs, A.; Bergoglio, R.; et al. Bladder cancer mortality with arsenic in drinking water in Argentina. Epidemiology 1996, 7, 117-124. Chen, C.J.; Chen, C. W.; Wu, M. M.; Kuo, T. L. Cancer potential in liver, lung, bladder, and kidney due to ingested inorganic arsenic in drinking water. J. Cancer. 1992, 66, 888-892. IARC (International Agency for Research on Cancer), In IARC monograph on the evaluation of carcinogenicity risk to humans? Overall evaluation of carcinogenicity: an update of IARC monographs 1-42 (suppl. 7), International Agency for Research on Cancer, Lyon, France, 1987, pp. 100-106. Rossman, T. G.; Uddin, A. N.; Burns, F. J. Evidence that arsenite acts as a cocarcinogen in skin cancer. Appl. Pharmacol. 2004, 198, 394 404. Smith, A. H.; Hopenhayn-Rich, C.; Bates, M. N.; Goeden, H. M.; et al. Cancer risks from arsenic in drinking water. Health Perspect. 1992, 97, 259-267. Aposhian, H. V.; Aposhian, M. M. Arsenic toxicology: five questions. Res. Toxicol. 2006, 19, 1-15. Goering, P. L.; Aposhian, H. V.; Mass, M. J.; Cebrián, M., et al. The enigma of arsenic carcinogenesis: role of metabolism. Sci. 1999, 49, 5-14. Waalkes, M. P.; Liu, J.; Ward, J. M.; Diwan, B. A. Mechanisms underlying arsenic carcinogenesis: hypersensitivity of mice exposed to inorganic arsenic during gestation. 2004, 198, 31-38. Aposhian, H. V.; Gurzau, E. S.; Le, X. C.; Gurzau, A.; et al. Occurrence of monomethylarsonous acid in urine of humans exposed to inorganic arsenic. Res. Toxicol. 2000, 13, 693-697. Del Razo, L. M.; Styblo, M.; Cullen, W. R.; Thomas, D. J. Determination of trivalent methylated arsenicals in biological matrices. Appl. Pharmacol. 2001, 174, 282-293. Styblo, M.; Drobna, Z.; Jaspers, I.; Lin, S.; Thomas, D. J.; The role of biomethylation in toxicity and carcinogenicity of arsenic: a research update. Environ. Health Perspect. 2002, 5, 767-771. Chowdhury, U. K.; Zakharyan, R. A.; Hernandez, A.; Avram, M. D.; et al. Glutathione-S-transferase-omega [MMA(V) reductase] knockout mice: Enzyme and arsenic species concentrations in tissues after arsenate administration. Appl. Pharmaol. 2006, 216, 446-457. Kitchin, K. T. Recent advances in arsenic carcinogenesis: modes of action, animal model systems, and methylated arsenic metabolites. Appl. Pharmacol. 2001, 172, 249-261. Beckman, G.; Beckman, L.; Nordenson, I. Chromosome aberrations in workers exposed to arsenic. Health Perspect. 1977, 19, 145-146. Yamanaka, K.; Hoshino, M.; Okanoto, M.; Sawamura, R.; et al. Induction of DNA damage by dimethylarsine, a metabolite of inorganic arsenics, is for the major part likely due to its peroxyl radical. Biophys. Res. Commun. 1990, 168, 58-64. Yamanaka, K.; Okada, S. Induction of lung-specific DNA damage by metabolically methylated arsenics via the production of free radicals. Health Perspect. 1994, 102, 37-40. Simeonova, P. P.; Luster, M. I. Mechanisms of arsenic carcinogenicity:Genetic or epigenetic mechanisms? Environ. Pathol. Toxicol. Oncol. 2000, 19, 281-286. Popovicova, J.; Moser, G. J.; Goldsworthy, T. L.; Tice, R. R, Carcinogenicity and co-carcinogenicity of sodium arsenite in p53+/- male mice. 2000, 54, 134. Li, J. H.; Rossman, T. G. Mechanism of co-mutagenesis of sodium arsenite with N-methyl-N-nitrosourea. Trace Elem. 1989, 21, 373-381. Zhao, C. Q.; Young, M. R.; Diwan, B. A.; Coogan, T. P.; et al. Association of arsenic-induced malignant transformation with DNA hypomethylation and aberrant gene expression. Proc. Natl. Acad. Sci. USA, 1997, 94, 10907-10912. Abernathy, C. O.; Lui, Y. P.; Longfellow, D.; Aposhian, H. V.; et al. Arsenic: Health effects, mechanisms of actions and research issues. Health Perspect. 1999, 107, 593-597. Lee, T. C.; Tanaka, N.; Lamb, P. W.; Gilmer, T. M.; et al. Induction of gene amplification by arsenic. 1988, 241, 79-81. Lantz, R. C.; Lynch, B. J.; Boitano, S.; Poplin, G. S.; et al. Pulmonary biomarkers based on alterations in protein expression after exposure to arsenic. Health Perspect. 2007, 115, 586-591. Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Biochem. 1976, 72, 248-254. Chowdhury, U. K.; Aposhian, H. V. Protein expression in the livers and urinary bladders of hamsters exposed to sodium arsenite. N. Y. Acad. Sci. 2008, 1140, 325-334. Andon, N. L.; Hollingworth, S.; Koller, A.; Greenland, A. J.; et al. Proteomic characterization of wheat amyloplasts using identification of proteins by Tandem Mass Spectrometry. 2002, 2, 1156-1168. Shapland, C.; Hsuan, J. J.; Totty, N. F.; Lawson, D. Purification and properties of transgelin: a transformation and shape change sensitive actin-gelling protein. Cell Biol. 1993, 121, 1065-1073. Lawson, D.; Harrison, M.; Shapland, C. Fibroblast transgelin and smooth muscle SM22 alpha are the same protein, the expression of which is down-regulated in may cell lines. Cell Motil. Cytoskeleton. 1997, 38, 250-257. Yang, Z.; Chang, Y- J.; Miyamoto, H.; Ni, J.; et al. Transgelin functions as a suppressor via inhibition of ARA54-enhanced androgen receptor transactivation and prostate cancer cell grown. Endocrinol. 2007, 21, 343-358. Yeo, M.; Kim, D- K.; Park, H. J.; Oh, T. Y.; et al. Loss of transgelin in repeated bouts of ulcerative colitis-induced colon carcinogenesis. 2006, 6, 1158-1165. Kim, H- J.; Sohng, I.; Kim, D- H.; Lee, D- C.; et al. Investigation of early protein changes in the urinary bladder following partial bladder outlet obstruction by proteomic approach. Korean Med. Sci. 2005, 20, 1000-1005. Shields, J. M.; Rogers-Graham, K.; Der, C. J. Loss of transgelin in breast and colon tumors and in RIE-1 cells by Ras deregulation of gene expression through Raf-independent pathways. Biol. Chem. 2002, 277, 9790-9799. Zeiden, A.; Sward, K.; Nordstrom, J.; Ekblad, E.; et al. Ablation of SM220c decreases contractility and actin contents of mouse vascular smooth muscle. FEBS Lett. 2004, 562, 141-146. Hoivik, D.; Wilson, C.; Wang, W.; Willett, K.; et al. Studies on the relationship between estrogen receptor content, glutathione S-transferase pi expression, and induction by 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin and drug resistance in human breast cancer cells. Biochem. Biophys. 1997, 348, 174-182. Hayes, J. D.; Pulford. D. J. The glutathione S-transferase super gene family: regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance. Critical Rev. Biochem. Mol. Biol. 1995, 30, 445-600. Zhao, T.; Singhal, S. S.; Piper, J. T.; Cheng, J.; et al. The role of human glutathione S-transferases hGSTA1-1 and hGSTA2-2 in protection against oxidative stress. Biochem. Biophys. 1999, 367, 216-224. Zakharyan, R. A.; Sampayo-Reyes, A.; Healy, S. M.; Tsaprailis, G.; et al. Human monomethylarsonic acid (MMA) reductase is a member of the glutathione-S-transferase superfamily. Res. Toxicol. 2001, 14, 1051-1057. Tsuchida, S.; Sekine, Y.; Shineha, R.; Nishihira, T.; et al. Elevation of the placental glutathione S-transferase form (GST-PI) in tumor tissues and the levels in sera of patients with cancer. Cancer Res. 1989, 43, 5225-5229. Tsutsumi, M.; Sugisaki, T.; Makino, T.; Miyagi, N.; et al. Oncofetal expression of glutathione S-transferase placental form in human stomach carcinomas. Gann. 1987, 78, 631-633. Mannervik, B.; Castro, V. M.; Danielson, U. H.; Tahir, M. K.; et al. Expression of class Pi glutathione transferase in human malignant melanoma cells. Carcinogenesis (Lond.). 1987, 8, 1929-1932. Di llio, C.; Del Boccio, G.; Aceto, A.; Casaccia, R.; et al. Elevation of glutathione transferase activity in human lung tumor. Carcinogenesis (Lond.). 1988, 9, 335-340. Sreenath, A. S.; Ravi, K. K.; Reddy, G. V.; Sreedevi, B.; et al. Evidence for the association of synaptotagmin with glutathione S- transferase: implications for a novel function in human breast cancer. Clinical Biochem. 2005, 38, 436-443. Shea, T. C.; Kelley S. L.; Henner, W. D. Identification of an anionic form ofglutathione transferase present in many human tumors and human tumor cell lines. Cancer Res. 1988, 48, 527-533. Simic, T.; Mimic-Oka, J.; Savic-Radojevic, A.; Opacic, M.; et al. Glutathione S- transferase T1-1 activity upregulated in transitional cell carcinoma of urinary bladder. 2005, 65, 1035-1040. Schuliga, M.; Chouchane, S.; Snow, E. T. Up-regulation of glutathione - related genes and enzyme activities in cultured human cells by sub-lethal concentration of inorganic arsenic. Sci. 2002, 70, 183-192. Matsui, M.; Nishigori, C.; Toyokuni, S.; Takada, J.; et al. The role of oxidative DNA damage in human arsenic carcinogenesis: detection of 8 hydroxy-2'-deoxyguanosine in arsenic-related Bowen's disease. Invest. Dermatol. 1999, 113, 26-31. Sanada, Y.; Suemori, I.; Katunuma, N. Properties of ornithine aminotransferase from rat liver, kidney, and small intestine. Biophys. Acta. 1970, 220, 42-50. Wang, G.; Shang, L.; Burgett, A. W. G.; Harran, P. G.; et al. Diazonamide toxins reveal an unexpected function for ornithine d-amino transferase in mitotic cell division. PNAS, 2007, 104, 2068-2073. Fujita, T.; Inoue, H.; Kitamura, T.; Sato, N.; et al. Senescence marker protein-30 (SMP30) rescues cell death by enhancing plasma membrane Caat-pumping activity in hep G2 cells. Biophys. Res. Commun. 1998, 250, 374-380. Ishigami, A.; Fujita, T.; Handa, S.; Shirasawa, T.; et al. Senescence marker protein-30 knockout mouse liver is highly susceptible to tumors necrosis factor-∞ and fas-mediated apoptosis. J. Pathol. 2002, 161, 1273-1281. Kondo, Y.; Ishigami, A.; Kubo, S.; Handa, S.; et al. Senescence marker protein-30is a unique enzyme that hydrolyzes diisopropylphosphorofluoridate in the liver. FEBS Letters. 2004, 570, 57-62. Ishigami, A.; Kondo, Y.; Nanba, R.; Ohsawa, T.; et al. SMP30 deficiency in mice causes an accumulation of neutral lipids and phospholipids in the liver and shortens the life span. Biophys. Res. Commun. 2004, 315, 575-580. Martin, G. G.; Danneberg, H.; Kumar, L. S.; Atshaves, B. P.; et al. Decreased liver fatty acid binding capacity and altered liver lipid distribution in mice lacking the liver fatty acid binding protein gene. Biol. Chem. 2003, 278, 21429-21438. Atshaves, B. P.; Storey, S. M.; Petrescu, A.; Greenberg, C. C.; et al. Expression of fatty acid binding proteins inhibits lipid accumulation and alters toxicity in L cell fibroblasts. J. Physiol. Cell Physiol. 2002, 283, C688-2703.

Subvocalization has been described as a series of attenuated movements of the vocal tract during silent reading and imagination. This two-part study investigated covert laryngeal activations among singers during the perception and imagination of music and text. In the first part, 155 singers responded to an online survey investigating their self-perceived corporal activation when listening to live or recorded singing. Respondents reported frequent corporal activation in their larynx and other body parts in response to live singing and, to a lesser extent, recordings. In the second part, an exploratory experiment was conducted to investigate physiological correlates of subvocalization in singers during the perception and imagery of melody and text stimuli, using simultaneous measurements of laryngeal activation both externally, with surface electromyography, and internally, with nasolaryngoscopy. Experimental results indicate the occurrence of subvocalization during imagination—but not during listening—of both stimuli and suggest that laryngoscopy is more sensitive to detection of subvocalization in singers. The results may point to vocal resonance or empathy in the perception of singers.

Les paroles de chansons fournissent un grand nombre d’informations sur la musique car ellescontiennent une grande partie de la sémantique des chansons. Ces informations pourraient aider les utilisateurs à naviguer facilement dans une large collection de chansons et permettre de leur offrir des recommandations personnalisées. Cependant, ces informations ne sont souvent pas disponibles sous leur forme textuelle. Les systèmes de reconnaissance de la voix chantée pourraient être utilisés pour obtenir des transcriptions directement à partir de la source audio. Ces approches sont usuellement adaptées de celles de la reconnaissance vocale. La transcription de la parole est un domaine vieux de plusieurs décennies qui a récemment connu des avancées significatives en raison des derniers développements des techniques d’apprentissage automatique. Cependant, appliqués au chant, ces algorithmes donnent des résultats peu satisfaisants et le processus de transcription des paroles reste difficile avec des complications particulières. Dans cette thèse, nous étudions plusieurs problèmes de ’Music Information Retrieval’ scientifiquement et industriellement complexes en utilisant des informations sur les paroles générées directement à partir de l’audio. L’accent est mis sur la nécessité de rendre les approches aussi pertinentes que possible dans le monde réel. Cela implique par exemple de les tester sur des ensembles de données vastes et diversifiés et d’étudier leur extensibilité. A cette fin, nous utilisons un large ensemble de données publiques possédant des annotations vocales et adaptons avec succès plusieurs des algorithmes de reconnaissance de paroles les plus performants. Nous présentons notamment, pour la première fois, un système qui détecte le contenu explicite directement à partir de l’audio. Les premières recherches sur la création d’un système d’alignement paroles audio multilingue sont également décrites. L’étude de la tâche alignement paroles-audio est complétée de deux expériences quantifiant la perception de la synchronisation de l’audio et des paroles. Une nouvelle approche phonotactique pour l’identification de la langue est également présentée. Enfin, nous proposons le premier algorithme de détection de versions employant explicitement les informations sur les paroles extraites de l’audio
Lyrics provide a lot of information about music since they encapsulate a lot of the semantics of songs. Such information could help users navigate easily through a large collection of songs and to recommend new music to them. However, this information is often unavailable in its textual form. To get around this problem, singing voice recognition systems could be used to obtain transcripts directly from the audio. These approaches are generally adapted from the speech recognition ones. Speech transcription is a decades-old domain that has lately seen significant advancements due to developments in machine learning techniques. When applied to the singing voice, however, these algorithms provide poor results. For a number of reasons, the process of lyrics transcription remains difficult. In this thesis, we investigate several scientifically and industrially difficult ’Music Information Retrieval’ problems by utilizing lyrics information generated straight from audio. The emphasis is on making approaches as relevant in real-world settings as possible. This entails testing them on vast and diverse datasets and investigating their scalability. To do so, a huge publicly available annotated lyrics dataset is used, and several state-of-the-art lyrics recognition algorithms are successfully adapted. We notably present, for the first time, a system that detects explicit content directly from audio. The first research on the creation of a multilingual lyrics-toaudio system are as well described. The lyrics-toaudio alignment task is further studied in two experiments quantifying the perception of audio and lyrics synchronization. A novel phonotactic method for language identification is also presented. Finally, we provide the first cover song detection algorithm that makes explicit use of lyrics information extracted from audio

This chapter reviews the biological definition of communication and some of the sensory modalities involved in the detection of communication signals. It explains the concepts of true communication and mutualism, as well as the sensory modalities involved in communication. The discussion covers visual, acoustic, chemical, tactile, and electrical signals. Furthermore, the chapter focuses on two intensively studied model systems. One of the model systems is the neuroethology of cricket song where the mechanism and neural control of sound production are examined, and the concept of positive phonotaxis is explained. The discussion covers the use of Kramer locomotion compensator, the song recognition by auditory interneurons, as well as the effect of temperature on calling songs of crickets. The second model system explains the development of bird songs — describing the neural circuits for song perception, song production, and song learning.

Abstract Question-marks are everywhere in Is he Popenjoy?. They figure on the cover, at the head of chapters, and throughout the text. Questions are profusely interpolated into the authorial commentary and characters interrogate themselves and others constantly. There are, as I count them, some 1,175 question-marks speckling the narrative of Is he Popenjoy?. For what the statistic is worth, Barchester Towers, written twenty years earlier and much the same length, has only 626. The big question, of course, is the titular one. In the novel as it opens, Mary Lovelace, the daughter of an amiably combative Dean (only a generation away from ‘trade’), marries the younger son of an aristocratic family, Lord George Germain: Although he is not the heir, Lord George has very great expectations. His elder brother (by some ten years), the Marquis of Brotherton, lives in Italy, is in poor health, has dissolute habits, and is unmarried.

The National Park Service (NPS) began monitoring landbirds at Channel Islands National Park in 1993 as part of its long-term inventory and monitoring program. The park?s landbird monitoring later became part of the NPS Inventory and Monitoring Division?s Mediterranean Coast Network long-term monitoring programs. Consequently, landbird monitoring has been conducted in the park during every breeding season since 1993. In this report, we summarize data collected during the 2020 breeding season. Landbird monitoring was conducted between 1 April and 30 June 2020. Using distance-based sampling methods in a standardized protocol, birds were counted on 7 of 10 permanent line transects (70%) (2 of 3 on Santa Barbara Island, 1 of 1 on East Anacapa Island, and 4 of 5 on San Miguel Island). Two transects were not sampled in 2020 because of nesting California Brown Pelicans (Pelecanus occidentalis) on Santa Barbara Island (i.e., Canyons Transect) and reduced person hours and unfavorable winds on San Miguel Island (i.e., San Miguel Hill Transect). For point counts, 225 of 338 (67%) permanent stations were counted (i.e., 30 of 33 points on Santa Barbara Island, 8 of 8 on East Anacapa Island, 100 of 112 on Santa Cruz Island, 40 of 40 on San Miguel Island, and 47 of 145 on Santa Rosa Island). The 8 Prisoners? Cove points were not counted in 2020. Three points were not counted on Santa Barbara Island due to nesting pelicans: these points and the transect were also not counted in 2016?2019 to avoid disturbing breeding pelicans. Other points (i.e., on east Santa Cruz Island and Santa Rosa Island) were not counted due in large part to the global COVID-19 pandemic. Traveling to and from the park was only granted to essential NPS staff for the majority of the landbird season. Fifty-one bird species were counted from points and transects across all of the islands in 2020; 39 of these are breeding species on the island. Parkwide, the 10 most commonly detected breeding landbirds in 2020 were, in descending order: Horned Lark, Spotted Towhee, Song Sparrow, White-crowned Sparrow, Western Meadowlark, Orange-crowned Warbler, Bewick?s Wren, Island Scrub-Jay, House Finch, and Common Raven. On East Anacapa Island, 26 landbird species have been counted since 1993; 5 species were counted in 2020. No new transient species were detected in 2020; 10 transient or visiting species (nonbreeding, native species recorded only once or twice during surveys) have been counted on the island overall since 1993. On Santa Barbara Island, 49 landbird species have been counted since 1993; 15 species were counted in 2020. The highest number of Horned Lark since 1993 were counted in 2020 (n = 451). Warbling Vireo was a new transient species counted in 2020 on Santa Barbara; 30 transient or visiting species have been counted on the island since 1993. On Santa Cruz Island, 74 landbird species have been counted since 2013; 34 species were counted in 2020. Bullock?s Oriole was a new transient species counted in 2020 on Santa Cruz; 21 transient or visiting species have been counted on this island since 2013. On San Miguel Island, 69 landbird species have been counted since 1993; 14 were counted in 2020. No transient species were counted in 2020; 34 transient or visiting species have been counted on the island since 1993. On Santa Rosa Island, 75 landbird species have been counted since 1994; 26 were detected in 2020. Rose-breasted Grosbeak was a new transient species counted in 2020 on Santa Rosa Island; 30 transient or visiting species have been counted on the island since 1994. Across all the 5 islands, 3 transient or visiting bird species were newly counted in 2020, for a total of 77 such species counted since NPS monitoring began on the islands. Nonnative and invasive birds were counted on only 1 of the 5 islands in 2020: 4 European Starlings on Santa Rosa Island. However, anecdotal sightings of nonnative species occurred much more frequently (i.e., outside of the point and transect counts), and were made on all islands except Anacapa in 2020. The highest numbers of nonnative species detections occurred on Santa Cruz Island, with 33 detections of Eurasian Collared Dove (primarily at the Main Ranch area in the Central Valley), 15 detections of Brown-headed Cowbird (primarily at Scorpion Harbor), and 15 detections of European Starling (primarily at the Main Ranch and Scorpion Harbor). House Sparrows were observed fewer times, but on all islands except Anacapa; cowbirds occurred on all islands except Anacapa and San Miguel; and Rock Pigeon occurred on Santa Barbara and Santa Cruz Islands. In 2020, despite the COVID-19 pandemic, 67% of all points and 77% of all transects were counted among the 5 islands. Santa Rosa received the lightest sampling of points (32%), due to the difficulty of getting observers onto the island. Even with diminished sampling, species richness (number of species) values fell in predictable patterns: richness was greatest on the larger islands (75 on Santa Rosa, 73 on Santa Cruz) and least on the smallest islands (26 on Anacapa, 48 on Santa Barbara). We continue to recommend that nonnative invasive species, such as European Starlings on Santa Rosa Island, be removed before their numbers become harder to manage. Also, because Distance analyses assist statistically with evaluations of trends, we continue to recommend that a trend analysis using program DISTANCE, or newer hierarchical distance analyses, should be used after the 2020 season to assess 5-year trends in breeding species? numbers following the 2015 trend analysis conducted by Coonan and Dye (2016).