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34 results on '"Karnieli, A."'

1. Designing Nonlinear Photonic Crystals for High-Dimensional Quantum State Engineering

Author: Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Foley-Comer, Joshua, Trajtenberg-Mills, Sivan, Mishra, Sarika, Prabhakar, Shashi, Pratap, Ravindra, Freedman, Daniel, Bronstein, Alex M., Arie, Ady, Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Foley-Comer, Joshua, Trajtenberg-Mills, Sivan, Mishra, Sarika, Prabhakar, Shashi, Pratap, Ravindra, Freedman, Daniel, Bronstein, Alex M., and Arie, Ady
Abstract: We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics. We circumvent any limitations imposed by the inherently stochastic nature of the physical process and incorporate a set of stochastic dynamical equations governing its evolution under the SPDC Hamiltonian. We demonstrate the effectiveness of our model through the design of structured nonlinear photonic crystals (NLPCs) and shaped pump beams; and show, theoretically and experimentally, how to generate maximally entangled states in the spatial degree of freedom. The learning of NLPC structures offers a promising new avenue for shaping and controlling arbitrary quantum states and enables all-optical coherent control of the generated states. We believe that this approach can readily be extended from bulky crystals to thin Metasurfaces and potentially applied to other quantum systems sharing a similar Hamiltonian structures, such as superfluids and superconductors., Comment: ICLR Machine Learning for Materials (ML4Materials) Workshop 2023. arXiv admin note: text overlap with arXiv:2112.05934
Published: 2023

2. Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection

Author: Wang, Yi, Liu, Chenying, Tiwari, Arti, Silver, Micha, Karnieli, Arnon, Zhu, Xiao Xiang, Albrecht, Conrad M, Wang, Yi, Liu, Chenying, Tiwari, Arti, Silver, Micha, Karnieli, Arnon, Zhu, Xiao Xiang, and Albrecht, Conrad M
Abstract: Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys are both costly and limited in scale. With the increasing accessibility of aerial and satellite data, machine learning techniques bear large potential for the automatic detection and recognition of archaeological landscapes. In this paper, we propose a deep semantic model fusion method for ancient agricultural terrace detection. The input data includes aerial images and LiDAR generated terrain features in the Negev desert. Two deep semantic segmentation models, namely DeepLabv3+ and UNet, with EfficientNet backbone, are trained and fused to provide segmentation maps of ancient terraces and walls. The proposed method won the first prize in the International AI Archaeology Challenge. Codes are available at https://github.com/wangyi111/international-archaeology-ai-challenge., Comment: IEEE Big Data 2022 workshop on Digital Twins for Accelerated Discovery of Climate & Sustainability Solutions (ADoCS)
Published: 2023

3. DropCompute: simple and more robust distributed synchronous training via compute variance reduction

Author: Giladi, Niv, Gottlieb, Shahar, Shkolnik, Moran, Karnieli, Asaf, Banner, Ron, Hoffer, Elad, Levy, Kfir Yehuda, Soudry, Daniel, Giladi, Niv, Gottlieb, Shahar, Shkolnik, Moran, Karnieli, Asaf, Banner, Ron, Hoffer, Elad, Levy, Kfir Yehuda, and Soudry, Daniel
Abstract: Background: Distributed training is essential for large scale training of deep neural networks (DNNs). The dominant methods for large scale DNN training are synchronous (e.g. All-Reduce), but these require waiting for all workers in each step. Thus, these methods are limited by the delays caused by straggling workers. Results: We study a typical scenario in which workers are straggling due to variability in compute time. We find an analytical relation between compute time properties and scalability limitations, caused by such straggling workers. With these findings, we propose a simple yet effective decentralized method to reduce the variation among workers and thus improve the robustness of synchronous training. This method can be integrated with the widely used All-Reduce. Our findings are validated on large-scale training tasks using 200 Gaudi Accelerators., Comment: https://github.com/paper-submissions/dropcompute
Published: 2023

4. Remote sensing of geomorphodiversity linked to biodiversity — Part III: Traits, processes and remote sensing characteristics

Author: Lausch, Angela, Schaepman, M.E., Skidmore, A.K., Catana, E., Bannehr, L., Bastian, O., Borg, E., Bumberger, Jan, Dietrich, Peter, Glässer, C., Hacker, J.M., Höfer, R., Jagdhuber, T., Jany, S., Jung, A., Karnieli, A., Klenke, Reinhard, Kirsten, T., Ködel, Uta, Kresse, W., Mallast, Ulf, Montzka, C., Möller, M., Mollenhauer, Hannes, Pause, M., Rahman, M., Schrodt, F., Schmullius, C., Schütze, Claudia, Selsam, Peter, Syrbe, R.-U., Truckenbrodt, S., Vohland, M., Volk, Martin, Wellmann, Thilo, Zacharias, Steffen, Baatz, R., Lausch, Angela, Schaepman, M.E., Skidmore, A.K., Catana, E., Bannehr, L., Bastian, O., Borg, E., Bumberger, Jan, Dietrich, Peter, Glässer, C., Hacker, J.M., Höfer, R., Jagdhuber, T., Jany, S., Jung, A., Karnieli, A., Klenke, Reinhard, Kirsten, T., Ködel, Uta, Kresse, W., Mallast, Ulf, Montzka, C., Möller, M., Mollenhauer, Hannes, Pause, M., Rahman, M., Schrodt, F., Schmullius, C., Schütze, Claudia, Selsam, Peter, Syrbe, R.-U., Truckenbrodt, S., Vohland, M., Volk, Martin, Wellmann, Thilo, Zacharias, Steffen, and Baatz, R.
Abstract: Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in
Published: 2022

5. Sustainability challenges for the social-environmental systems across the Asian Drylands Belt

Author: Chen, Jiquan, John, Ranjeet, Yuan, Jing, Mack, Elizabeth A., Groisman, Pavel, Allington, Ginger, Wu, Jianguo, Fan, Peilei, De Beurs, Kirsten M., Karnieli, Arnon, Gutman, Garik, Kappas, Martin, Dong, Gang, Zhao, Fangyuan, Ouyang, Zutao, Pearson, Amber L., Şat, Beyza, Graham, Norman A., Shao, Changliang, Graham, Anna K., Henebry, Geoffrey M., Xue, Zhichao, Amartuvshin, Amarjargal, Qu, Luping, Park, Hogeun, Xin, Xiaoping, Chen, Jingyan, Tian, Li, Knight, Colt, Kussainova, Maira, Li, Fei, Fürst, Christine, Qi, Jiaguo, Chen, Jiquan, John, Ranjeet, Yuan, Jing, Mack, Elizabeth A., Groisman, Pavel, Allington, Ginger, Wu, Jianguo, Fan, Peilei, De Beurs, Kirsten M., Karnieli, Arnon, Gutman, Garik, Kappas, Martin, Dong, Gang, Zhao, Fangyuan, Ouyang, Zutao, Pearson, Amber L., Şat, Beyza, Graham, Norman A., Shao, Changliang, Graham, Anna K., Henebry, Geoffrey M., Xue, Zhichao, Amartuvshin, Amarjargal, Qu, Luping, Park, Hogeun, Xin, Xiaoping, Chen, Jingyan, Tian, Li, Knight, Colt, Kussainova, Maira, Li, Fei, Fürst, Christine, and Qi, Jiaguo
Abstract: This paper synthesizes the contemporary challenges for the sustainability of the social-environmental system (SES) across a geographically, environmentally, and geopolitically diverse region - the Asian Drylands Belt (ADB). This region includes 18 political entities, covering 10.3% of global land area and 30% of total global drylands. At the present time, the ADB is confronted with a unique set of environmental and socioeconomic changes including water shortage-related environmental challenges and dramatic institutional changes since the collapse of the Union of Soviet Socialist Republics. The SES of the ADB is assessed using a conceptual framework rooted in the three pillars of sustainability science: social, economic, and ecological systems. The complex dynamics are explored with biophysical, socioeconomic, institutional, and local context-dependent mechanisms with a focus on institutions and land use and land cover change (LULCC) as important drivers of SES dynamics. This paper also discusses the following five pressing, practical challenges for the sustainability of the ADB SES: (a) reduced water quantity and quality under warming, drying, and escalating extreme events, (b) continued, if not intensifying, geopolitical conflicts, (c) volatile, uncertain, and shifting socioeconomic structures, (d) globalization and cross-country influences, and (e) intensification and shifts in LULCC. To meet the varied challenges across the region, place-based, context-dependent transdisciplinary approaches are needed to focus on the human-environment interactions within and between regional landscapes with explicit consideration of specific forcings and regulatory mechanisms. Future work focused on this region should also assess the role of the following mechanisms that may moderate SES dynamics: socioeconomic regulating mechanisms, biophysical regulating mechanisms, regional and national institutional regulating mechanisms, and localized institutional regulating mechanisms.
Published: 2022

6. DeepShadow: Neural Shape from Shadow

Author: Karnieli, Asaf, Fried, Ohad, Hel-Or, Yacov, Karnieli, Asaf, Fried, Ohad, and Hel-Or, Yacov
Abstract: This paper presents DeepShadow, a one-shot method for recovering the depth map and surface normals from photometric stereo shadow maps. Previous works that try to recover the surface normals from photometric stereo images treat cast shadows as a disturbance. We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals. We demonstrate that 3D reconstruction from shadows can even outperform shape-from-shading in certain cases. To the best of our knowledge, our method is the first to reconstruct 3D shape-from-shadows using neural networks. The method does not require any pre-training or expensive labeled data, and is optimized during inference time., Comment: ECCV 2022. Project page available at https://asafkar.github.io/deepshadow
Published: 2022

7. Deepening the teaching and learning of clinical communication: The importance of reflection and feedback in health education

Author: Soleiman Franco, Renato, Giuliani dos Santos Franco, Camila Ament, Karnieli Miller, Orit, Soleiman Franco, Renato, Giuliani dos Santos Franco, Camila Ament, and Karnieli Miller, Orit
Abstract: Aims: clinical communication (CC) relates to health professionals’ interaction with patients/families. CC is fundamental for the physicians’ role. This paper aims to contribute to the discussion about reflection and feedback for meaningful teaching and learning of CC.Methods: the authors provided a short review and conceptual discussion of the history and nature of CC teaching, followed by exploring the role of reflection and feedback in teaching CC.Results: communicating well can be challenging as it requires medical students and professionals to adapt their communication to each patient/family while obtaining all the needed information, conveying trustworthiness, care, and compassion. The teaching of CC to medical students involves deepening the doctor-patient relationship’s technical, relational, and emotional elements. CC requires teaching that is flexible and tailored to the participants’ needs. Therefore, teaching CC must go beyond asking the appropriate question or applying specific checklist-based behaviours. In teaching CC, it is crucial to give medical students support to discuss personal and institutional barriers and attitudes and explore how to transfer their learning to clinical practice. To that end, reflection should be encouraged to allow students to express difficulties and feelings and enhance their understanding of themselves and others. Within this process, feedback is essential to moving beyond skill-based teaching to reflection-based learning.Conclusion: the move from skills-based learning requires using reflective processes and feedback to allow students to learn about their communication tendencies and needs to become more flexible and attuned to different patient’s needs in clinical encounters., Objetivos: a comunicação clínica (CC) está relacionada à interação entre profissionais de saúde e pacientes ou familiares, sendo fundamental para prática médica. Este artigo tem como objetivo contribuir para a discussão sobre reflexão e feedback no ensino e aprendizagem significativos de CC.Métodos: os autores realizaram uma breve revisão e discussão conceitual da história e da natureza do ensino do CC, seguida pela exploração do papel da reflexão e do feedback no ensino de CC.Resultados: comunicar-se bem pode ser desafiador, pois exige que os estudantes de medicina e profissionais adaptem sua comunicação a cada paciente/família enquanto obtêm todas as informações necessárias, transmitindo confiabilidade, cuidado e compaixão. O ensino do CC para estudantes de medicina envolve o aprofundamento de elementos técnicos, relacionais e emocionais da relação médico-paciente. A CC requer um ensino flexível e adaptado às necessidades dos estudantes. Portanto, ensinar CC deve ir além de fazer a pergunta apropriada ou aplicar comportamentos específicos baseados em listas de verificação. No ensino do CC, é crucial dar aos estudantes de medicina apoio para discutir as barreiras e atitudes pessoais e institucionais e explorar como transferir seu aprendizado para a prática clínica. Para tanto, deve-se estimular a reflexão para permitir que os alunos expressem dificuldades e sentimentos e ampliem sua compreensão de si e dos outros. Nesse processo, o feedback é essencial para ir além do ensino baseado em habilidades para o aprendizado baseado em reflexão.Conclusão: a mudança de uma aprendizagem baseada em habilidades requer o uso de um processo reflexivo e feedback para permitir que os alunos aprendam sobre si, assim como, sobre se tornarem mais flexíveis e sintonizados com as diferentes necessidades do paciente nos encontros clínicos.
Published: 2021

8. SPDCinv: Inverse Quantum-Optical Design of High-Dimensional Qudits

Author: Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Foley-Comer, Joshua, Trajtenberg-Mills, Sivan, Freedman, Daniel, Bronstein, Alex M., Arie, Ady, Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Foley-Comer, Joshua, Trajtenberg-Mills, Sivan, Freedman, Daniel, Bronstein, Alex M., and Arie, Ady
Abstract: Spontaneous parametric down-conversion in quantum optics is an invaluable resource for the realization of high-dimensional qudits with spatial modes of light. One of the main open challenges is how to directly generate a desirable qudit state in the SPDC process. This problem can be addressed through advanced computational learning methods; however, due to difficulties in modeling the SPDC process by a fully differentiable algorithm that takes into account all interaction effects, progress has been limited. Here, we overcome these limitations and introduce a physically-constrained and differentiable model, validated against experimental results for shaped pump beams and structured crystals, capable of learning every interaction parameter in the process. We avoid any restrictions induced by the stochastic nature of our physical model and integrate the dynamic equations governing the evolution under the SPDC Hamiltonian. We solve the inverse problem of designing a nonlinear quantum optical system that achieves the desired quantum state of down-converted photon pairs. The desired states are defined using either the second-order correlations between different spatial modes or by specifying the required density matrix. By learning nonlinear volume holograms as well as different pump shapes, we successfully show how to generate maximally entangled states. Furthermore, we simulate all-optical coherent control over the generated quantum state by actively changing the profile of the pump beam. Our work can be useful for applications such as novel designs of high-dimensional quantum key distribution and quantum information processing protocols. In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian., Comment: A code supporting the paper has been published (https://github.com/EyalRozenberg1/SPDCinv)
Published: 2021

9. Inverse Design of Quantum Holograms in Three-Dimensional Nonlinear Photonic Crystals

Author: Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Trajtenberg-Mills, Sivan, Freedman, Daniel, Bronstein, Alex M., Arie, Ady, Rozenberg, Eyal, Karnieli, Aviv, Yesharim, Ofir, Trajtenberg-Mills, Sivan, Freedman, Daniel, Bronstein, Alex M., and Arie, Ady
Abstract: We introduce a systematic approach for designing 3D nonlinear photonic crystals and pump beams for generating desired quantum correlations between structured photon-pairs. Our model is fully differentiable, allowing accurate and efficient learning and discovery of novel designs., Comment: A supporting code will be published shortly
Published: 2021
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10. Linking remote sensing and geodiversity and their traits relevant to biodiversity—Part I: Soil characteristics

Author: Lausch, Angela, Baade, J., Bannehr, L., Borg, E., Bumberger, Jan, Chabrilliat, S., Dietrich, Peter, Gerighausen, H., Glässer, C., Hacker, J.M., Haase, Dagmar, Jagdhuber, T., Jany, S., Jung, A., Karnieli, A., Kraemer, Roland, Makki, M., Mielke, C., Möller, M., Mollenhauer, Hannes, Montzka, C., Pause, M., Rogass, C., Rozenstein, O., Schmullius, C., Schrodt, F., Schrön, Martin, Schulz, K., Schütze, Claudia, Schweitzer, C., Selsam, Peter, Skidmore, A.K., Spengler, D., Thiel, C., Truckenbrodt, S.C., Vohland, M., Wagner, R., Weber, Ute, Werban, Ulrike, Wollschläger, Ute, Zacharias, Steffen, Schaepman, M.E., Lausch, Angela, Baade, J., Bannehr, L., Borg, E., Bumberger, Jan, Chabrilliat, S., Dietrich, Peter, Gerighausen, H., Glässer, C., Hacker, J.M., Haase, Dagmar, Jagdhuber, T., Jany, S., Jung, A., Karnieli, A., Kraemer, Roland, Makki, M., Mielke, C., Möller, M., Mollenhauer, Hannes, Montzka, C., Pause, M., Rogass, C., Rozenstein, O., Schmullius, C., Schrodt, F., Schrön, Martin, Schulz, K., Schütze, Claudia, Schweitzer, C., Selsam, Peter, Skidmore, A.K., Spengler, D., Thiel, C., Truckenbrodt, S.C., Vohland, M., Wagner, R., Weber, Ute, Werban, Ulrike, Wollschläger, Ute, Zacharias, Steffen, and Schaepman, M.E.
Abstract: In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.
Published: 2019

11. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

Author: Lausch, Angela, Baade, Jussi, Bannehr, Lutz, Borg, Erik, Bumberger, Jan, Chabrilliat, Sabine, Dietrich, Peter, Gerighausen, Heike, Glässer, Cornelia, Hacker, Jorg M., Haase, Dagmar, Jagdhuber, Thomas, Jany, Sven, Jung, András, Karnieli, Arnon, Kraemer, Roland, Makki, Mohsen, Mielke, Christian, Möller, Markus, Mollenhauer, Hannes, Montzka, Carsten, Pause, Marion, Rogass, Christian, Rozenstein, Offer, Schmullius, Christiane, Schrodt, Franziska, Schrön, Martin, Schulz, Karsten, Schütze, Claudia, Schweitzer, Christian, Selsam, Peter, Skidmore, Andrew K., Spengler, Daniel, Thiel, Christian, Truckenbrodt, Sina C., Vohland, Michael, Wagner, Robert, Weber, Ute, Werban, Ulrike, Wollschläger, Ute, Zacharias, Steffen, Schaepman, Michael E., Lausch, Angela, Baade, Jussi, Bannehr, Lutz, Borg, Erik, Bumberger, Jan, Chabrilliat, Sabine, Dietrich, Peter, Gerighausen, Heike, Glässer, Cornelia, Hacker, Jorg M., Haase, Dagmar, Jagdhuber, Thomas, Jany, Sven, Jung, András, Karnieli, Arnon, Kraemer, Roland, Makki, Mohsen, Mielke, Christian, Möller, Markus, Mollenhauer, Hannes, Montzka, Carsten, Pause, Marion, Rogass, Christian, Rozenstein, Offer, Schmullius, Christiane, Schrodt, Franziska, Schrön, Martin, Schulz, Karsten, Schütze, Claudia, Schweitzer, Christian, Selsam, Peter, Skidmore, Andrew K., Spengler, Daniel, Thiel, Christian, Truckenbrodt, Sina C., Vohland, Michael, Wagner, Robert, Weber, Ute, Werban, Ulrike, Wollschläger, Ute, Zacharias, Steffen, and Schaepman, Michael E.
Abstract: In the face of rapid global change it is imperative to preserve geodiversity for the overallconservation of biodiversity. Geodiversity is important for understanding complex biogeochemicaland physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystemorganization. Despite the great importance of geodiversity, there is a lack of suitable monitoringmethods. Compared to conventional in-situ techniques, remote sensing (RS) techniques providea pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, aswell as standardized monitoring of continuous geodiversity on the local to global scale. This papergives an overview of the state-of-the-art approaches for monitoring soil characteristics and soilmoisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques.Initially, the definitions for geodiversity along with its five essential characteristics are provided,with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral traitvariations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging),thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soilcharacteristics and soil moisture are also presented. Furthermore, the paper discusses current andfuture satellite-borne sensors and missions as well as existing data products. Due to the prospectsand limitations of the characteristics of different RS sensors, only specific geotraits and geodiversitycharacteristics can be recorded. The paper provides an overview of those geotraits., Peer Reviewed
Published: 2019

12. Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images

Author: Oriani, F, Ohana-Levi, N, Marra, F, Straubhaar, J, Mariethoz, Gregoire, Renard, Philippe, Karnieli, A, Morin, E, Oriani, F, Ohana-Levi, N, Marra, F, Straubhaar, J, Mariethoz, Gregoire, Renard, Philippe, Karnieli, A, and Morin, E
Abstract: The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high-resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple-point simulation techniques dealing with extremely nonstationary heterogeneities and extensive data sets.
Published: 2018

13. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Author: Pasetto, Damiano (author), Arenas-Castro, Salvador (author), Bustamante, Javier (author), Casagrandi, Renato (author), Chrysoulakis, Nektarios (author), Cord, Anna F. (author), Dittrich, Andreas (author), Domingo‐Marimon, Cristina (author), El Serafy, G.Y.H. (author), Karnieli, Arnon (author), Kordelas, Georgios A. (author), Manakos, Ioannis (author), Mari, Lorenzo (author), Monteiro, Antonio (author), Palazzi, Elisa (author), Poursanidis, Dimitris (author), Rinaldo, Andrea (author), Terzago, Silvia (author), Ziemba, A.M. (author), Ziv, Guy (author), Pasetto, Damiano (author), Arenas-Castro, Salvador (author), Bustamante, Javier (author), Casagrandi, Renato (author), Chrysoulakis, Nektarios (author), Cord, Anna F. (author), Dittrich, Andreas (author), Domingo‐Marimon, Cristina (author), El Serafy, G.Y.H. (author), Karnieli, Arnon (author), Kordelas, Georgios A. (author), Manakos, Ioannis (author), Mari, Lorenzo (author), Monteiro, Antonio (author), Palazzi, Elisa (author), Poursanidis, Dimitris (author), Rinaldo, Andrea (author), Terzago, Silvia (author), Ziemba, A.M. (author), and Ziv, Guy (author)
Abstract: Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the us, Mathematical Physics
Published: 2018
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14. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Author: Pasetto, D., Arenas‐Castro, S., Bustamante, J., Casagrandi, R., Chrysoulakis, N., Cord, Anna, Dittrich, Andreas, Domingo‐Marimon, C., El Serafy, G., Karnieli, A., Kordelas, G.A., Manakos, I., Mari, L., Monteiro, A., Palazzi, E., Poursanidis, D., Rinaldo, A., Terzago, S., Ziemba, A., Ziv, G., Pasetto, D., Arenas‐Castro, S., Bustamante, J., Casagrandi, R., Chrysoulakis, N., Cord, Anna, Dittrich, Andreas, Domingo‐Marimon, C., El Serafy, G., Karnieli, A., Kordelas, G.A., Manakos, I., Mari, L., Monteiro, A., Palazzi, E., Poursanidis, D., Rinaldo, A., Terzago, S., Ziemba, A., and Ziv, G.
Abstract: Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use o
Published: 2018

15. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Author: Pasetto, Damiano (author), Arenas-Castro, Salvador (author), Bustamante, Javier (author), Casagrandi, Renato (author), Chrysoulakis, Nektarios (author), Cord, Anna F. (author), Dittrich, Andreas (author), Domingo‐Marimon, Cristina (author), El Serafy, G.Y.H. (author), Karnieli, Arnon (author), Kordelas, Georgios A. (author), Manakos, Ioannis (author), Mari, Lorenzo (author), Monteiro, Antonio (author), Palazzi, Elisa (author), Poursanidis, Dimitris (author), Rinaldo, Andrea (author), Terzago, Silvia (author), Ziemba, A.M. (author), Ziv, Guy (author), Pasetto, Damiano (author), Arenas-Castro, Salvador (author), Bustamante, Javier (author), Casagrandi, Renato (author), Chrysoulakis, Nektarios (author), Cord, Anna F. (author), Dittrich, Andreas (author), Domingo‐Marimon, Cristina (author), El Serafy, G.Y.H. (author), Karnieli, Arnon (author), Kordelas, Georgios A. (author), Manakos, Ioannis (author), Mari, Lorenzo (author), Monteiro, Antonio (author), Palazzi, Elisa (author), Poursanidis, Dimitris (author), Rinaldo, Andrea (author), Terzago, Silvia (author), Ziemba, A.M. (author), and Ziv, Guy (author)
Abstract: Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the us, Mathematical Physics
Published: 2018
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16. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Author: European Commission, Pasetto, D., Arenas-Castro, S., Bustamante, Javier, Casagrandi, R., Chrysoulakis, N., Cord, A.F., Dittrich, A., Domingo-Marimon, Cristina, El Serafy, G., Karnieli, A., Kordelas, G.A., Manakos, I., Mari, L., Monteiro, A., Palazzi, E., Poursanidis, Dimitris, Rinaldo, A., Terzago, S., Ziemba, A., Ziv, G., European Commission, Pasetto, D., Arenas-Castro, S., Bustamante, Javier, Casagrandi, R., Chrysoulakis, N., Cord, A.F., Dittrich, A., Domingo-Marimon, Cristina, El Serafy, G., Karnieli, A., Kordelas, G.A., Manakos, I., Mari, L., Monteiro, A., Palazzi, E., Poursanidis, Dimitris, Rinaldo, A., Terzago, S., Ziemba, A., and Ziv, G.
Abstract: Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use o
Published: 2018

17. Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Author: Matzrafi, Maor, Matzrafi, Maor, Herrmann, Ittai, Nansen, Christian, Kliper, Tom, Zait, Yotam, Ignat, Timea, Siso, Dana, Rubin, Baruch, Karnieli, Arnon, Eizenberg, Hanan, Matzrafi, Maor, Matzrafi, Maor, Herrmann, Ittai, Nansen, Christian, Kliper, Tom, Zait, Yotam, Ignat, Timea, Siso, Dana, Rubin, Baruch, Karnieli, Arnon, and Eizenberg, Hanan
Abstract: Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) wa
Published: 2017

18. Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Author: Matzrafi, Maor, Matzrafi, Maor, Herrmann, Ittai, Nansen, Christian, Kliper, Tom, Zait, Yotam, Ignat, Timea, Siso, Dana, Rubin, Baruch, Karnieli, Arnon, Eizenberg, Hanan, Matzrafi, Maor, Matzrafi, Maor, Herrmann, Ittai, Nansen, Christian, Kliper, Tom, Zait, Yotam, Ignat, Timea, Siso, Dana, Rubin, Baruch, Karnieli, Arnon, and Eizenberg, Hanan
Abstract: Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) wa
Published: 2017

19. Saxagliptin and Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus

Author: Scirica, B, Bhatt, D, Braunwald, E, Steg, P, Davidson, J, Hirshberg, B, Ohman, P, Frederich, R, Wiviott, S, Hoffman, E, Cavender, M, Udell, J, Desai, N, Mosenzon, O, Mcguire, D, Ray, K, Leiter, L, Raz, I, Abrahamsen, T, Grossman, M, Morin, S, Im, K, Gabovitch, D, Pricken, A, Buskila, A, Stahre, C, Price, D, Billing-Clason, S, Sabel, K, Monyak, J, Sjostrand, M, Wei, C, Lu, J, Miller, E, Raichlen, J, Fitt, S, Iqbal, N, Donovan, M, Aguilar-Salinas, C, Alvarsson, M, Amerena, J, Ardissino, D, Averkov, O, Avogaro, A, Barnett, A, Bretzel, R, Chiang, C, Codoceo, V, Corbalan, R, Dalby, A, Darius, H, Deerochanawong, C, Dellborg, M, Eliaschewitz, F, Garcia-Castillo, A, Gomis, R, Henry, P, Hoekstra, J, Jermendy, G, Kastelein, J, Keech, A, Kiss, R, Krempf, M, Laakso, M, Leitersdorf, E, Lewis, B, Litwak, L, Lopez-Sendon, J, Ma, R, Medina, F, Moses, R, Nicolau, J, Opolski, G, Ophuis, T, Paolasso, E, Ruda, M, Kumar, K, Shestakova, M, Sheu, W, Smahelova, A, Bhupathiraju, B, Spinar, J, Sritata, P, Strojeck, K, Villena-Chavez, J, Jia, W, Huo, Y, Lowe, C, Awtry, E, Berger, C, Desai, A, Gelfand, E, Leeman, D, Link, M, Ruberg, F, Vita, J, Rost, N, Silverman, S, Greenberger, N, Lerch, M, Gersh, B, Nesto, R, Del Prato, S, Tuomilehto, J, Kelsey, S, Alvarisqueta, A, Cuadrado, J, Rista, L, Hermida, S, Baccaro, C, Luquez, C, Lagrutta, M, Maffei, L, Bartolacci, I, Montana, O, Cutuli, H, Berli, M, Lorenzatti, A, Frechtel, G, La Greca, R, Fretes, O, Diaz, M, Papini, N, Farias, E, Issa, C, Elbert, A, Lehman, R, Arya, M, Singh, B, Colquhoun, D, Jayasinghe, R, de Looze, F, Blombery, P, Ward, G, Szto, G, Abhayaratna, W, Borges, J, Russo, L, Felicio, J, Santos, F, Guimaraes, F, Castro, M, Rossi, P, Armaganijan, D, Leaes, P, Bandeira, F, Franken, M, Rassi, N, Rea, R, Zanella, M, Amodeo, C, Cesar, L, Betti, R, Chacra, A, Schmid, H, Bell, A, Syan, G, Zadra, R, Conter, H, Dumas, R, Borts, D, Dattani, I, Poirier, P, Cha, J, Dzongowski, P, Labonte, R, St Pierre, B, Gaudet, D, Kouz, S, Lamy, A, Tishler, S, Chehayeb, R, Bedard, J, Hramiak, I, Teitelbaum, I, Fortin, C, Woo, V, Conway, J, Mehta, P, Robinson, S, Sussex, B, Chiasson, J, Muirhead, N, Bose, S, Ouellet, A, Yale, J, Bhargava, R, Lau, D, Tobe, S, Perron, P, Sigalas, J, Bilodeau, L, Tytus, R, Achyuthan, G, Pearce, M, Steele, A, Bailey, G, Ma, P, St-Maurice, F, Rupka, D, Houlden, R, Bailey, A, Rewa, G, Sohal, P, Ting, R, Prieto, J, Rodriguez, M, Godoy, G, Larenas, G, Pincetti, C, Cobos, L, Saavedra, V, Varleta, P, Lucero, F, Kuzmanic, O, Acevedo, M, Aguirre, M, Florenzano, F, Ma, J, Bao, Y, Jiang, M, Xu, W, Shi, Y, Zheng, M, Li, Y, Dong, Y, Zhao, W, Sun, M, Lei, M, Wang, J, Pistek, Z, Rihacek, I, Kucera, D, Brada, M, Naplava, R, Karasova, J, Vlasicova, H, Skopecek, J, Spinarova, L, Raclavska, L, Sarbochova, R, Okenka, L, Racicka, E, Urbancova, K, Oznerova, M, Lorenc, Z, Wasserburger, B, Zahumensky, E, Grunfeldova, H, Hradec, J, Lukac, M, Svacina, S, Podzimek, J, Hemzsky, L, Mikulkova, I, Pavlickova, J, Brychta, T, Chochola, J, Couffinhal, T, Elbaz, M, Petit, C, Faller, B, Marechaud, R, Moulin, P, Fendri, S, Nazeyrollas, P, Wendisch, U, Busch, K, Klausmann, G, Duengen, H, Appel, K, Toursarkissian, N, Jung, T, Ott, P, Schenkenberger, I, Kuesters, D, Landers, B, Nischik, R, Fischer, H, Tschoepe, D, Paschen, B, Krause, K, Derwahl, K, Wenzl-Bauer, V, Hamann, A, Strotmann, H, Milek, K, Mueller, S, Chu, D, Tan, K, Kung, K, Tsang, C, Tomlinson, B, Koranyi, L, Kerenyi, Z, Benczur, B, Illyes, L, Hidvegi, T, Somogyi, A, Valco, J, Ferenczi, S, Rapi, J, Voros, P, Winkler, G, Sydo, T, Hetey, M, Simon, K, Penzes, J, Kempler, P, Bako, B, Lengyel, Z, Witmann, I, Dudas, M, Vandorfi, G, Takacs, J, Matoltsy, A, Ladanyi, E, Gyimesi, A, Sadikot, S, Parikh, K, Jain, S, Yajnik, C, Sosale, A, Shamanna, P, Srikanta, S, Shah, S, Srinivas, A, Banker, D, Shah, P, Sharda, A, Makkar, B, Rais, N, Mardikar, H, Mishra, A, Bhupati, S, Menon, J, Sathe, S, Gupta, R, Sharma, V, Darawsha, M, Herskovits, T, Hamoud, S, Nikolsky, E, Adawi, F, Zimlichman, R, Tsalihin, D, Wainstein, J, Klainman, E, Mosseri, M, Yerushalmi, Y, Karnieli, E, Knobler, H, Benchetrit, S, Tsur, A, Yagil, Y, Atar, S, Beberashvili, I, Fuchs, S, Stern, N, Pollak, A, Chajek-Shaul, T, Rozenman, Y, Biton, A, Bramucci, E, Fiscella, A, Grassia, V, Piatti, P, De Cosmo, S, Di Lorenzo, L, Merlini, P, Mannucci, E, Frontoni, S, Trevisan, R, Zenari, L, Lambiase, C, Salvioni, A, Silvestri, O, Ambrosio, G, Di Bartolo, P, Fattore, L, Presbitero, P, Calabrese, M, Evola, R, Gamba, M, Ibarra, M, Munoz, E, Sanchez, D, Herrera, C, Rios, J, Llamas, E, Esperon, G, Cantu, E, Fragoso, J, Gonzalez, J, Martinez, G, Padilla, F, Mier, G, Marmolejo, D, Ruiz, J, Portilla, N, Rosas, E, Machado, G, Ramos, J, Briones, I, van Hessen, M, Strikwerda, S, The, S, Kooy, A, Ronner, E, Nierop, P, Remmen, J, Groenemeijer, B, Hamer, B, Basart, D, van Lennep, H, Nieuwdorp, M, van Dijk, M, Kentgens, S, van Kempen, W, Hoogendijk, J, Spiering, W, Voors-Pette, C, Kose, V, de Waard, D, 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Chapman, G, Calvert, J, Reed, R, Tam, D, Butcher, G, Jones, N, Takhar, A, Turner, W, Mcnally, D, Corey, O, Chapman, J, Mohr, S, Edwards, S, Ocampo, A, Kandath, D, Aude, Y, Ervin, W, Savin, V, Anderson, R, Littlefield, R, Oberoi, M, Platt, G, Yazdani, S, Mangoo-Karim, R, Walder, J, Gogia, H, Chandrashekhar, Y, Boccalandro, F, Rogers, W, Bilazarian, S, Zieve, F, Siage, Y, O'Connor, T, Mudaliar, S, Nikas, A, Giusti, R, Glover, R, Chilka, S, French, W, Roth, E, Singh, N, Christofferson, R, Stich, M, Dagogo-Jack, S, Allison, J, Zengotita, G, Ison, R, Iteld, B, Sulistio, M, Gonzalez, E, Gorman, T, Hage-Korban, E, Reddy, R, Byars, W, Antonishen, M, Benjamin, S, First, B, Rosado, J, Bruschetta, H, Poling, T, Rosendorff, C, Kerstein, H, Saba, F, Willis, J, Adams, K, Vazquez, E, Ellison, H, Kahn, B, Kereiakes, D, Powell, S, Raskin, P, Smith, K, Varma, S, Whittier, F, Casanova, R, Isserman, S, Kaye, W, Mcguinn, W, Bartkowiak, A, Dworkin, L, Labrador, C, Podlecki, D, Popovtzer, M, Aronoff, S, 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Abstract: BACKGROUND: The cardiovascular safety and efficacy of many current antihyperglycemic agents, including saxagliptin, a dipeptidyl peptidase 4 (DPP-4) inhibitor, are unclear. METHODS: We randomly assigned 16,492 patients with type 2 diabetes who had a history of, or were at risk for, cardiovascular events to receive saxagliptin or placebo and followed them for a median of 2.1 years. Physicians were permitted to adjust other medications, including antihyperglycemic agents. The primary end point was a composite of cardiovascular death, myocardial infarction, or ischemic stroke. RESULTS: A primary end-point event occurred in 613 patients in the saxagliptin group and in 609 patients in the placebo group (7.3% and 7.2%, respectively, according to 2-year Kaplan-Meier estimates; hazard ratio with saxagliptin, 1.00; 95% confidence interval [CI], 0.89 to 1.12; P=0.99 for superiority; P<0.001 for noninferiority); the results were similar in the "on-treatment" analysis (hazard ratio, 1.03; 95% CI, 0.91 to 1.17). The major secondary end point of a composite of cardiovascular death, myocardial infarction, stroke, hospitalization for unstable angina, coronary revascularization, or heart failure occurred in 1059 patients in the saxagliptin group and in 1034 patients in the placebo group (12.8% and 12.4%, respectively, according to 2-year Kaplan-Meier estimates; hazard ratio, 1.02; 95% CI, 0.94 to 1.11; P=0.66). More patients in the saxagliptin group than in the placebo group were hospitalized for heart failure (3.5% vs. 2.8%; hazard ratio, 1.27; 95% CI, 1.07 to 1.51; P=0.007). Rates of adjudicated cases of acute and chronic pancreatitis were similar in the two groups (acute pancreatitis, 0.3% in the saxagliptin group and 0.2% in the placebo group; chronic pancreatitis, <0.1% and 0.1% in the two groups, respectively). CONCLUSIONS: DPP-4 inhibition with saxagliptin did not increase or decrease the rate of ischemic events, though the rate of hospitalization for heart failure was
Published: 2013

20. Saxagliptin and Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus

Author: Scirica, B, Bhatt, D, Braunwald, E, Steg, P, Davidson, J, Hirshberg, B, Ohman, P, Frederich, R, Wiviott, S, Hoffman, E, Cavender, M, Udell, J, Desai, N, Mosenzon, O, Mcguire, D, Ray, K, Leiter, L, Raz, I, Abrahamsen, T, Grossman, M, Morin, S, Im, K, Gabovitch, D, Pricken, A, Buskila, A, Stahre, C, Price, D, Billing-Clason, S, Sabel, K, Monyak, J, Sjostrand, M, Wei, C, Lu, J, Miller, E, Raichlen, J, Fitt, S, Iqbal, N, Donovan, M, Aguilar-Salinas, C, Alvarsson, M, Amerena, J, Ardissino, D, Averkov, O, Avogaro, A, Barnett, A, Bretzel, R, Chiang, C, Codoceo, V, Corbalan, R, Dalby, A, Darius, H, Deerochanawong, C, Dellborg, M, Eliaschewitz, F, Garcia-Castillo, A, Gomis, R, Henry, P, Hoekstra, J, Jermendy, G, Kastelein, J, Keech, A, Kiss, R, Krempf, M, Laakso, M, Leitersdorf, E, Lewis, B, Litwak, L, Lopez-Sendon, J, Ma, R, Medina, F, Moses, R, Nicolau, J, Opolski, G, Ophuis, T, Paolasso, E, Ruda, M, Kumar, K, Shestakova, M, Sheu, W, Smahelova, A, Bhupathiraju, B, Spinar, J, Sritata, P, Strojeck, K, Villena-Chavez, J, Jia, W, Huo, Y, Lowe, C, Awtry, E, Berger, C, Desai, A, Gelfand, E, Leeman, D, Link, M, Ruberg, F, Vita, J, Rost, N, Silverman, S, Greenberger, N, Lerch, M, Gersh, B, Nesto, R, Del Prato, S, Tuomilehto, J, Kelsey, S, Alvarisqueta, A, Cuadrado, J, Rista, L, Hermida, S, Baccaro, C, Luquez, C, Lagrutta, M, Maffei, L, Bartolacci, I, Montana, O, Cutuli, H, Berli, M, Lorenzatti, A, Frechtel, G, La Greca, R, Fretes, O, Diaz, M, Papini, N, Farias, E, Issa, C, Elbert, A, Lehman, R, Arya, M, Singh, B, Colquhoun, D, Jayasinghe, R, de Looze, F, Blombery, P, Ward, G, Szto, G, Abhayaratna, W, Borges, J, Russo, L, Felicio, J, Santos, F, Guimaraes, F, Castro, M, Rossi, P, Armaganijan, D, Leaes, P, Bandeira, F, Franken, M, Rassi, N, Rea, R, Zanella, M, Amodeo, C, Cesar, L, Betti, R, Chacra, A, Schmid, H, Bell, A, Syan, G, Zadra, R, Conter, H, Dumas, R, Borts, D, Dattani, I, Poirier, P, Cha, J, Dzongowski, P, Labonte, R, St Pierre, B, Gaudet, D, Kouz, S, Lamy, A, Tishler, S, Chehayeb, R, Bedard, J, Hramiak, I, Teitelbaum, I, Fortin, C, Woo, V, Conway, J, Mehta, P, Robinson, S, Sussex, B, Chiasson, J, Muirhead, N, Bose, S, Ouellet, A, Yale, J, Bhargava, R, Lau, D, Tobe, S, Perron, P, Sigalas, J, Bilodeau, L, Tytus, R, Achyuthan, G, Pearce, M, Steele, A, Bailey, G, Ma, P, St-Maurice, F, Rupka, D, Houlden, R, Bailey, A, Rewa, G, Sohal, P, Ting, R, Prieto, J, Rodriguez, M, Godoy, G, Larenas, G, Pincetti, C, Cobos, L, Saavedra, V, Varleta, P, Lucero, F, Kuzmanic, O, Acevedo, M, Aguirre, M, Florenzano, F, Ma, J, Bao, Y, Jiang, M, Xu, W, Shi, Y, Zheng, M, Li, Y, Dong, Y, Zhao, W, Sun, M, Lei, M, Wang, J, Pistek, Z, Rihacek, I, Kucera, D, Brada, M, Naplava, R, Karasova, J, Vlasicova, H, Skopecek, J, Spinarova, L, Raclavska, L, Sarbochova, R, Okenka, L, Racicka, E, Urbancova, K, Oznerova, M, Lorenc, Z, Wasserburger, B, Zahumensky, E, Grunfeldova, H, Hradec, J, Lukac, M, Svacina, S, Podzimek, J, Hemzsky, L, Mikulkova, I, Pavlickova, J, Brychta, T, Chochola, J, Couffinhal, T, Elbaz, M, Petit, C, Faller, B, Marechaud, R, Moulin, P, Fendri, S, Nazeyrollas, P, Wendisch, U, Busch, K, Klausmann, G, Duengen, H, Appel, K, Toursarkissian, N, Jung, T, Ott, P, Schenkenberger, I, Kuesters, D, Landers, B, Nischik, R, Fischer, H, Tschoepe, D, Paschen, B, Krause, K, Derwahl, K, Wenzl-Bauer, V, Hamann, A, Strotmann, H, Milek, K, Mueller, S, Chu, D, Tan, K, Kung, K, Tsang, C, Tomlinson, B, Koranyi, L, Kerenyi, Z, Benczur, B, Illyes, L, Hidvegi, T, Somogyi, A, Valco, J, Ferenczi, S, Rapi, J, Voros, P, Winkler, G, Sydo, T, Hetey, M, Simon, K, Penzes, J, Kempler, P, Bako, B, Lengyel, Z, Witmann, I, Dudas, M, Vandorfi, G, Takacs, J, Matoltsy, A, Ladanyi, E, Gyimesi, A, Sadikot, S, Parikh, K, Jain, S, Yajnik, C, Sosale, A, Shamanna, P, Srikanta, S, Shah, S, Srinivas, A, Banker, D, Shah, P, Sharda, A, Makkar, B, Rais, N, Mardikar, H, Mishra, A, Bhupati, S, Menon, J, Sathe, S, Gupta, R, Sharma, V, Darawsha, M, Herskovits, T, Hamoud, S, Nikolsky, E, Adawi, F, Zimlichman, R, Tsalihin, D, Wainstein, J, Klainman, E, Mosseri, M, Yerushalmi, Y, Karnieli, E, Knobler, H, Benchetrit, S, Tsur, A, Yagil, Y, Atar, S, Beberashvili, I, Fuchs, S, Stern, N, Pollak, A, Chajek-Shaul, T, Rozenman, Y, Biton, A, Bramucci, E, Fiscella, A, Grassia, V, Piatti, P, De Cosmo, S, Di Lorenzo, L, Merlini, P, Mannucci, E, Frontoni, S, Trevisan, R, Zenari, L, Lambiase, C, Salvioni, A, Silvestri, O, Ambrosio, G, Di Bartolo, P, Fattore, L, Presbitero, P, Calabrese, M, Evola, R, Gamba, M, Ibarra, M, Munoz, E, Sanchez, D, Herrera, C, Rios, J, Llamas, E, Esperon, G, Cantu, E, Fragoso, J, Gonzalez, J, Martinez, G, Padilla, F, Mier, G, Marmolejo, D, Ruiz, J, Portilla, N, Rosas, E, Machado, G, Ramos, J, Briones, I, van Hessen, M, Strikwerda, S, The, S, Kooy, A, Ronner, E, Nierop, P, Remmen, J, Groenemeijer, B, Hamer, B, Basart, D, van Lennep, H, Nieuwdorp, M, van Dijk, M, Kentgens, S, van Kempen, W, Hoogendijk, J, Spiering, W, Voors-Pette, C, Kose, V, de Waard, D, Gonkel, F, Kaasjager, H, Lingan, G, Agous, I, Kruik, H, Imholz, B, Pieterse, M, Manrique, H, Villena, J, Leon, L, Kundert, K, Minchola, J, Pinto, M, Heredia, J, Rodriguez, A, Guerreros, C, Berrospi, P, Zubiate, C, Allemant, A, Arbanil, H, Ponciano, W, Calderon, J, Lisson, R, Segura, L, Sidorowicz-Bialynicka, A, Sciborski, R, Fares, I, Mader, P, Skierkowska, J, Stasinska, T, Pomiecko, W, Skorski, M, Krzyzagorska, E, Polaszewska-Muszynska, M, Sowinski, D, Strojek, K, Rosinska-Migda, J, Romanczuk, P, Golebiowski, G, Kubica, J, Mazurek, T, Wojnowski, L, Pasternak, D, Stachlewski, P, Trzepla, E, Bogdanowicz, G, Uzunow, A, Potakowska, I, Miszczyszyn, Z, Waszyrowski, T, Smolenskaya, O, Lukyanov, Y, Vorokhobina, N, Khalimov, Y, Orlikova, O, Rebrov, A, Kukharchuk, V, Boldueva, S, Arkhipov, M, Zhelninova, T, Pavlysh, E, Antsiferov, M, Panov, A, Pavlova, M, Shustov, S, Demchenko, E, Galyavich, A, Malakhina, E, Semenova, O, Kobalava, Z, Kotova, S, Gavrisheva, I, Oschepkova, E, Karpov, Y, Sidorenko, B, Kislyak, O, Ametov, A, Dreval, A, Grineva, E, Mkrtumyan, A, Tyurina, T, Sazonova, O, Bonnici, F, Ranjith, N, Burgess, L, Nortje, H, Distiller, L, Mitha, I, Moore, R, Conradie, M, Horak, A, Pillay, S, Wellmann, H, Berg, E, Pillai, P, Padayachee, T, Corbett, C, Makan, H, Wing, J, Vawda, Z, Ebrahim, I, Mitha, E, Bhorat, A, Fernandez, J, Munoz, C, Cortada, J, Conde, A, Calvo, C, Extremera, B, Delgado, E, Masmiquel, L, Puig, J, Parreno, L, Mauricio, D, Redon, J, Brito, M, Lopez, C, de la Morera, J, Linderfalk, C, Larnefeldt, H, Olsson, A, Lonneborg, L, Ekelund, M, Samad, B, Borgencrantz, B, Nilsson, J, Berglund, O, Svensson, M, Mooe, T, Curiac, D, Albin, J, Angesjo, E, Lannemyr, O, Chen, J, Tien, K, Ueng, K, Lai, W, Yin, W, Hung, Y, Shyu, K, Hou, J, Lam, H, Laothavorn, P, Kuanprasert, S, Khovidhunkit, W, Benjasuratwong, Y, Chotinaiwattarakul, C, Mamanasiri, S, Suraamornkul, S, Pratipanawatr, T, Nitiyanant, W, Heller, S, Pieters, R, Strang, C, Bodalia, B, Middleton, A, Hall, T, Chapman, G, Calvert, J, Reed, R, Tam, D, Butcher, G, Jones, N, Takhar, A, Turner, W, Mcnally, D, Corey, O, Chapman, J, Mohr, S, Edwards, S, Ocampo, A, Kandath, D, Aude, Y, Ervin, W, Savin, V, Anderson, R, Littlefield, R, Oberoi, M, Platt, G, Yazdani, S, Mangoo-Karim, R, Walder, J, Gogia, H, Chandrashekhar, Y, Boccalandro, F, Rogers, W, Bilazarian, S, Zieve, F, Siage, Y, O'Connor, T, Mudaliar, S, Nikas, A, Giusti, R, Glover, R, Chilka, S, French, W, Roth, E, Singh, N, Christofferson, R, Stich, M, Dagogo-Jack, S, Allison, J, Zengotita, G, Ison, R, Iteld, B, Sulistio, M, Gonzalez, E, Gorman, T, Hage-Korban, E, Reddy, R, Byars, W, Antonishen, M, Benjamin, S, First, B, Rosado, J, Bruschetta, H, Poling, T, Rosendorff, C, Kerstein, H, Saba, F, Willis, J, Adams, K, Vazquez, E, Ellison, H, Kahn, B, Kereiakes, D, Powell, S, Raskin, P, Smith, K, Varma, S, Whittier, F, Casanova, R, Isserman, S, Kaye, W, Mcguinn, W, Bartkowiak, A, Dworkin, L, Labrador, C, Podlecki, D, Popovtzer, M, Aronoff, S, Ballantyne, C, Ortiz, E, Mora, A, Pitts, T, Reinhardt, S, Soucie, G, Wainwright, W, Henson, B, Sklaver, N, Arakaki, R, Brown, J, Chalavarya, G, Chochinov, R, Dixon, T, Kutner, M, Perlman, R, Raisinghani, A, Salacata, A, Awasty, V, Elinoff, V, George, W, LaRochelle-Gryseels, A, Mercado, A, Miller, G, Qureshi, M, Steljes, A, Wefald, F, Wilson, J, Chinn, J, Chuang, R, Comulada-Rivera, A, Hartman, I, Narayan, P, Pacheco, T, Weiss, R, Beavins, J, Creevy, J, Hamroff, G, Hodson, R, Kosinski, E, Krichmar, P, Patel, R, Schneider, R, Shapiro, J, Sharp, D, Speer, J, Stegemoller, R, Waxman, F, Chang, F, Braun, E, Eder, F, Minor, S, Albert, M, Carr, K, Diaczok, B, Gastman, I, Gupta, V, Longshaw, K, Gonzalez-Campoy, J, Raikhel, M, Thomas, J, Wood, K, Diab, I, Furda, J, Gelernt, M, Halter, M, House, B, Kaster, S, Raad, G, Stamatin, R, Barker, B, Blonder, R, Bloomberg, R, Calderon, R, Carrol, A, Comerota, A, Feinglos, M, Henderson, D, Kastelic, R, Stonesifer, L, Talano, J, Lee, P, Brosseau, J, Clark, W, Cohen, E, Fialkow, J, Horton, K, Kozman, H, Mcgill, J, Mihills, C, Poonawala, R, Shore, K, Tejada, L, Torres, R, Wright, W, Calatayud, G, Chandna, H, Drozdiak, R, Fink, R, Gill, R, Glandt, M, Gottlieb, D, Hack, T, Kay, J, Mansouri, V, Mcknight, T, Mostel, E, Schmidt, L, Seide, H, Sonel, E, Taylor, R, Velasquez, M, Bretton, E, Feldman, R, Hartman, A, Hershon, K, Leach, C, Martin, E, Mohiuddin, F, Naygandhi, Y, Riske, T, Schima, S, Uzoaga, E, Ward, H, Weinstock, R, Williams, T, Altschuller, A, Aoki, T, Blumenthal, S, Cash, A, Eisner, G, Gutmann, J, Hagan, M, Kabour, A, Markus, T, Mckenzie, W, Moursi, M, Mystkowski, P, Ovalle, F, Perkins, R, Popeil, L, Saniuk, R, Sierra, Y, Alvarado, O, Anderson, J, Bajaj, M, Blank, R, Chu, A, Levinsky, L, Levy, P, Osborne, J, Pavon, H, Sanderlin, D, Schaer, G, Zarich, S, Atassi, K, Bayron, C, Casagrande, M, Das, D, Gimness, M, Handel, F, Kinstrey, T, Leu, S, Osei, K, Nouel, J, Soltani, Z, Sussman, H, Chiu, K, Duda, R, Farnsworth, K, Lano, M, Lee, F, Levin, P, Pratt, S, Richwine, R, Ruiz-Rivera, L, Turner, J, Wood, J, Zigrang, W, Baquerizo, H, Colon, C, Demattia, J, Desai, V, Fitz-Patrick, D, Goral, S, Odhav, A, Prentiss, A, Ruff, C, W, W, Wyne, K, Abbott, L, Applegate, R, Cabral, J, Kotha, P, Ortega, P, Simmons, D, Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman P, Frederich R, Wiviott SD, Hoffman EB, Cavender MA, Udell JA, Desai NR, Mosenzon O, McGuire DK, Ray KK, Leiter LA, Raz I, Desai N, Abrahamsen T, Grossman M, Morin S, Im K, Hoffman E, Gabovitch D, Pricken A, Buskila A, Stahre C, Price D, Billing-Clason S, Sabel K, Monyak J, Sjostrand M, Wei C, Lu J, Miller E, Raichlen J, Fitt S, Iqbal N, Donovan M, Davidson JA, Aguilar-Salinas C, Alvarsson M, Amerena J, Ardissino D, Averkov O, Avogaro A, Barnett A, Bretzel R, Chiang CE, Codoceo V, Corbalan R, Dalby A, Darius H, Deerochanawong C, Dellborg M, Eliaschewitz F, Garcia-Castillo A, Gomis R, Henry P, Hoekstra J, Jermendy G, Kastelein J, Keech A, Kiss R, Krempf M, Laakso M, Leiter L, Leitersdorf E, Lewis B, Litwak L, Lopez-Sendon J, Ma R, McGuire D, Medina F, Moses R, Nicolau JC, Opolski G, Ophuis TO, Paolasso E, Ruda M, Kumar KMP, Shestakova M, Sheu WHH, Smahelova A, Bhupathiraju BSR, Spinar J, Sritata P, Strojeck K, Villena-Chavez JE, Jia WP, Huo Y, Lowe C, Awtry E, Berger C, Desai AS, Gelfand E, Leeman D, Link M, Ruberg F, Vita J, Rost N, Silverman S, Greenberger NJ, Lerch MM, Gersh B, Nesto R, Del Prato S, Tuomilehto J, Kelsey S, Alvarisqueta A, Cuadrado J, Rista L, Hermida S, Baccaro C, Luquez C, Lagrutta M, Maffei L, Bartolacci I, Montana O, Cutuli H, Berli M, Lorenzatti A, Frechtel G, La Greca R, Fretes O, Diaz M, Papini NR, Farias E, Issa C, Elbert A, Lehman R, Arya M, Singh B, Colquhoun D, Jayasinghe R, de Looze F, Blombery P, Ward G, Szto G, Abhayaratna W, Borges J, Russo L, Felicio J, Santos F, Guimaraes F, Castro ML, Rossi P, Armaganijan D, Leaes P, Bandeira F, Franken M, Rassi N, Rea R, Zanella M, Amodeo C, Cesar L, Betti R, Chacra A, Schmid H, Bell A, Syan G, Zadra R, Conter H, Dumas R, Borts D, Dattani I, Poirier P, Cha J, Dzongowski P, Labonte R, St Pierre B, Gaudet D, Kouz S, Lamy A, Tishler S, Chehayeb R, Bedard J, Hramiak I, Teitelbaum I, Fortin C, Woo V, Conway J, Mehta P, Robinson S, Sussex B, Chiasson J, Muirhead N, Bose S, Ouellet A, Yale J, Bhargava R, Lau D, Tobe S, Perron P, Sigalas J, Bilodeau L, Tytus R, Achyuthan G, Pearce M, Steele A, Bailey G, Ma P, St-Maurice F, Rupka D, Houlden R, Bailey A, Rewa G, Sohal P, Ting R, Prieto J, Rodriguez M, Godoy G, Larenas G, Pincetti C, Cobos L, Saavedra V, Varleta P, Lucero F, Kuzmanic O, Acevedo M, Aguirre M, Florenzano F, Ma J, Bao Y, Jiang M, Xu W, Shi Y, Zheng M, Li Y, Dong Y, Zhao W, Sun M, Lei M, Wang J, Pistek Z, Rihacek I, Kucera D, Brada M, Naplava R, Karasova J, Vlasicova H, Skopecek J, Spinarova L, Raclavska L, Sarbochova R, Okenka L, Racicka E, Urbancova K, Oznerova M, Lorenc Z, Wasserburger B, Zahumensky E, Grunfeldova H, Hradec J, Lukac M, Svacina S, Podzimek J, Hemzsky L, Mikulkova I, Pavlickova J, Brychta T, Chochola J, Couffinhal T, Elbaz M, Petit C, Faller B, Marechaud R, Moulin P, Fendri S, Nazeyrollas P, Wendisch U, Busch K, Klausmann G, Duengen H, Appel K, Toursarkissian N, Jung T, Ott P, Schenkenberger I, Kuesters D, Landers B, Nischik R, Fischer H, Tschoepe D, Paschen B, Krause K, Derwahl K, Wenzl-Bauer V, Hamann A, Strotmann H, Milek K, Mueller S, Chu D, Tan K, Kung K, Tsang C, Tomlinson B, Koranyi L, Kerenyi Z, Benczur B, Illyes L, Hidvegi T, Somogyi A, Valco J, Ferenczi S, Rapi J, Voros P, Winkler G, Sydo T, Hetey M, Simon K, Penzes J, Kempler P, Bako B, Lengyel Z, Witmann I, Dudas M, Vandorfi G, Takacs J, Matoltsy A, Ladanyi E, Gyimesi A, Sadikot S, Parikh K, Jain S, Yajnik C, Sosale A, Shamanna P, Srikanta S, Shah S, Srinivas A, Banker D, Shah P, Sharda A, Makkar B, Rais N, Mardikar H, Mishra A, Bhupati S, Menon J, Sathe S, Gupta R, Sharma V, Darawsha M, Herskovits T, Hamoud S, Nikolsky E, Adawi F, Zimlichman R, Tsalihin D, Wainstein J, Klainman E, Mosseri M, Yerushalmi Y, Karnieli E, Knobler H, Benchetrit S, Tsur A, Yagil Y, Atar S, Beberashvili I, Fuchs S, Stern N, Pollak A, Chajek-Shaul T, Rozenman Y, Biton A, Bramucci E, Fiscella A, Grassia V, Piatti P, De Cosmo S, Di Lorenzo L, Merlini P, Mannucci E, Frontoni S, Trevisan R, Zenari L, Lambiase C, Salvioni A, Silvestri O, Ambrosio G, Di Bartolo P, Fattore L, Presbitero P, Calabrese M, Evola R, Gamba MA, Ibarra MOD, Munoz EC, Sanchez DR, Herrera CH, Rios JP, Llamas EB, Esperon GL, Cantu EG, Fragoso JN, Gonzalez JG, Martinez GR, Padilla FP, Mier GM, Marmolejo DH, Ruiz JG, Portilla NC, Rosas EL, Machado GM, Ramos JC, Briones IR, van Hessen MWJ, Strikwerda S, The SHK, Kooy A, Ronner E, Nierop PR, Remmen JJ, Groenemeijer BE, Hamer BJB, Basart DCG, van Lennep HWOR, Nieuwdorp M, van Dijk MPM, Kentgens S, van Kempen WW, Hoogendijk J, Spiering W, Voors-Pette C, Kose V, de Waard DEP, Gonkel F, Kaasjager HAH, Lingan GMR, Agous I, Kruik HJ, Imholz BPM, Pieterse M, Manrique H, Villena J, Leon L, Kundert K, Minchola J, Pinto M, Heredia J, Rodriguez A, Guerreros C, Berrospi P, Zubiate C, Allemant A, Arbanil H, Ponciano W, Calderon J, Lisson R, Segura L, Sidorowicz-Bialynicka A, Sciborski R, Fares I, Mader P, Skierkowska J, Stasinska T, Pomiecko W, Skorski M, Krzyzagorska E, Polaszewska-Muszynska M, Sowinski D, Strojek K, Rosinska-Migda J, Romanczuk P, Golebiowski G, Kubica J, Mazurek T, Wojnowski L, Pasternak D, Stachlewski P, Trzepla E, Bogdanowicz G, Uzunow A, Potakowska I, Miszczyszyn Z, Waszyrowski T, Smolenskaya O, Lukyanov Y, Vorokhobina N, Khalimov Y, Orlikova O, Rebrov A, Kukharchuk V, Boldueva S, Arkhipov M, Zhelninova T, Pavlysh E, Antsiferov M, Panov A, Pavlova M, Shustov S, Demchenko E, Galyavich A, Malakhina E, Semenova O, Kobalava Z, Kotova S, Gavrisheva I, Oschepkova E, Karpov Y, Sidorenko B, Kislyak O, Ametov A, Dreval A, Grineva E, Mkrtumyan A, Tyurina T, Sazonova O, Bonnici F, Ranjith N, Burgess L, Nortje H, Distiller L, Mitha I, Moore R, Conradie M, Horak A, Pillay S, Wellmann H, Berg E, Pillai P, Padayachee T, Corbett C, Makan H, Wing J, Vawda Z, Ebrahim I, Mitha E, Bhorat A, Fernandez JC, Munoz C, Cortada JB, Conde AC, Calvo C, Extremera BG, Delgado E, Masmiquel L, Puig JG, Parreno LD, Mauricio D, Redon J, Brito M, Lopez C, de la Morera JS, Linderfalk C, Larnefeldt H, Olsson A, Lonneborg L, Ekelund M, Samad B, Borgencrantz B, Nilsson J, Berglund O, Svensson M, Mooe T, Curiac D, Albin J, Angesjo E, Lannemyr O, Chiang C, Sheu W, Chen J, Tien K, Ueng K, Lai W, Yin W, Hung Y, Shyu K, Hou J, Lam H, Laothavorn P, Kuanprasert S, Khovidhunkit W, Benjasuratwong Y, Chotinaiwattarakul C, Mamanasiri S, Suraamornkul S, Pratipanawatr T, Nitiyanant W, Heller S, Ray K, Pieters R, Strang C, Bodalia B, Middleton A, Hall T, Chapman G, Calvert J, Reed R, Tam D, Butcher G, Jones N, Takhar A, Turner W, McNally D, Corey O, Chapman J, Mohr S, Edwards S, Bhatt D, Ocampo A, Kandath D, Aude Y, Ervin W, Savin V, Anderson R, Littlefield R, Oberoi M, Platt GE, Yazdani S, Mangoo-Karim R, Walder J, Gogia H, Chandrashekhar Y, Boccalandro F, Rogers W, Bilazarian S, Zieve F, Siage Y, O'Connor T, Mudaliar S, Nikas A, Giusti R, Glover R, Chilka S, French W, Roth E, Singh N, Christofferson R, Stich M, Dagogo-Jack S, Allison J, Zengotita GA, Ison R, Iteld B, Sulistio M, Gonzalez E, Gorman T, Hage-Korban E, Reddy R, Byars W, Antonishen M, Benjamin S, First B, Rosado J, Bruschetta H, Poling T, Rosendorff C, Kerstein HJ, Saba F, Willis J, Adams K, Vazquez EC, Ellison H, Kahn B, Kereiakes D, Powell S, Raskin P, Smith K, Varma S, Whittier F, Casanova R, Isserman S, Kaye W, McGuinn W, Bartkowiak A, Dworkin L, Labrador CA, Podlecki D, Popovtzer M, Aronoff S, Ballantyne C, Ortiz EG, Mora A, Pitts T, Reinhardt S, Soucie G, Wainwright W, Henson B, Sklaver N, Arakaki R, Brown J, Chalavarya G, Chochinov R, Dixon T, Kutner M, Perlman R, Raisinghani A, Salacata A, Awasty V, Elinoff V, George W, LaRochelle-Gryseels A, Mercado A, Miller G, Qureshi M, Steljes A, Wefald F, Wilson J, Chinn J, Chuang RB, Comulada-Rivera A, Hartman I, Narayan P, Pacheco T, Weiss R, Beavins J, Creevy J, Hamroff G, Hodson R, Kosinski E, Krichmar P, Patel R, Schneider R, Shapiro J, Sharp D, Speer J, Stegemoller R, Waxman F, Chang FY, Braun E, Eder F, Minor S, Albert M, Carr KW, Diaczok B, Gastman I, Gupta V, Longshaw K, Gonzalez-Campoy JM, Raikhel M, Thomas J, Wood K, Diab I, Furda J, Gelernt M, Halter M, House B, Kaster S, Raad G, Stamatin R, Barker B, Blonder R, Bloomberg RJ, Calderon RB, Carrol A, Comerota A, Feinglos M, Henderson D, Kastelic R, Stonesifer LD, Talano J, Lee PV, Brosseau J, Clark W, Cohen E, Fialkow J, Horton K, Kozman H, McGill J, Mihills C, Poonawala R, Shore K, Tejada L, Torres R, Wright W, Calatayud G, Chandna H, Drozdiak R, Fink R, Gill R, Glandt M, Gottlieb DW, Hack T, Kay J, Mansouri V, McKnight T, Mostel E, Schmidt L, Seide H, Sonel E, Taylor R, Velasquez M, Bretton E, Feldman R, Hartman A, Hershon K, Leach CR, Martin E, Mohiuddin F, Naygandhi Y, Riske T, Schima S, Uzoaga ER, Ward H, Weinstock R, Williams T, Altschuller A, Aoki T, Blumenthal S, Cash A, Eisner G, Gutmann J, Hagan M, Kabour A, Markus T, McKenzie W, Moursi M, Mystkowski P, Ovalle F, Perkins R, Popeil L, Saniuk R, Sierra Y, Alvarado O, Anderson J, Bajaj M, Blank R, Chu A, Levinsky L, Levy P, Osborne J, Pavon H, Sanderlin D, Schaer G, Zarich S, Atassi K, Bayron C, Casagrande M, Das D, Gimness M, Handel F, Kinstrey T, Leu S, Osei K, Nouel JS, Soltani Z, Sussman H, Chiu K, Duda R, Farnsworth K, Lano M, Lee F, Levin P, Pratt S, Richwine R, Ruiz-Rivera L, Turner J, Wood J, Zigrang WD, Baquerizo HR, Colon CB, Demattia J, Desai V, Fitz-Patrick D, Goral S, Odhav A, Prentiss A, Ruff C, Wu W, Wyne K, Abbott LG, Applegate R, Cabral J, Kotha P, Ortega P, Simmons D, Scirica, B, Bhatt, D, Braunwald, E, Steg, P, Davidson, J, Hirshberg, B, Ohman, P, Frederich, R, Wiviott, S, Hoffman, E, Cavender, M, 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Abstract: BACKGROUND: The cardiovascular safety and efficacy of many current antihyperglycemic agents, including saxagliptin, a dipeptidyl peptidase 4 (DPP-4) inhibitor, are unclear. METHODS: We randomly assigned 16,492 patients with type 2 diabetes who had a history of, or were at risk for, cardiovascular events to receive saxagliptin or placebo and followed them for a median of 2.1 years. Physicians were permitted to adjust other medications, including antihyperglycemic agents. The primary end point was a composite of cardiovascular death, myocardial infarction, or ischemic stroke. RESULTS: A primary end-point event occurred in 613 patients in the saxagliptin group and in 609 patients in the placebo group (7.3% and 7.2%, respectively, according to 2-year Kaplan-Meier estimates; hazard ratio with saxagliptin, 1.00; 95% confidence interval [CI], 0.89 to 1.12; P=0.99 for superiority; P<0.001 for noninferiority); the results were similar in the "on-treatment" analysis (hazard ratio, 1.03; 95% CI, 0.91 to 1.17). The major secondary end point of a composite of cardiovascular death, myocardial infarction, stroke, hospitalization for unstable angina, coronary revascularization, or heart failure occurred in 1059 patients in the saxagliptin group and in 1034 patients in the placebo group (12.8% and 12.4%, respectively, according to 2-year Kaplan-Meier estimates; hazard ratio, 1.02; 95% CI, 0.94 to 1.11; P=0.66). More patients in the saxagliptin group than in the placebo group were hospitalized for heart failure (3.5% vs. 2.8%; hazard ratio, 1.27; 95% CI, 1.07 to 1.51; P=0.007). Rates of adjudicated cases of acute and chronic pancreatitis were similar in the two groups (acute pancreatitis, 0.3% in the saxagliptin group and 0.2% in the placebo group; chronic pancreatitis, <0.1% and 0.1% in the two groups, respectively). CONCLUSIONS: DPP-4 inhibition with saxagliptin did not increase or decrease the rate of ischemic events, though the rate of hospitalization for heart failure was
Published: 2013

21. Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

Author: Pisek, Jan, Chen, Jing M, Kobayashi, Hideki, Rautiainen, Miina, Schaepman, Michael E; https://orcid.org/0000-0002-9627-9565, Karnieli, Arnon, Sprinstin, Michael, Ryu, Youngryel, Nikopensius, Maris, Raabe, Kairi, Pisek, Jan, Chen, Jing M, Kobayashi, Hideki, Rautiainen, Miina, Schaepman, Michael E; https://orcid.org/0000-0002-9627-9565, Karnieli, Arnon, Sprinstin, Michael, Ryu, Youngryel, Nikopensius, Maris, and Raabe, Kairi
Abstract: Spatial and temporal patterns of forest background (understory) reflectance are crucial for retrieving biophysical parameters of forest canopies (overstory) and subsequently for ecosystem modeling. In this communication, we retrieved seasonal courses of understory normalized difference vegetation index (NDVI) from multiangular Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (MODIS BRDF)/albedo data. We compared satellite-based seasonal courses of understory NDVI to understory NDVI values measured in different types of forests distributed along a wide latitudinal gradient (65.12°N–31.35°N). Our results indicated that the retrieval method performs well particularly over open forests of different types. We also demonstrated the limitations of the method for closed canopies, where the understory signal retrieval is much attenuated.
Published: 2016

22. Rationale, design, and baseline characteristics in Evaluation of LIXisenatide in Acute Coronary Syndrome, a long-term cardiovascular end point trial of lixisenatide versus placebo

Author: UCL - SSS/IREC/CARD - Pôle de recherche cardiovasculaire, Bentley-Lewis, R., Aguilar, D., Riddle, M.C., Claggett, B., Diaz, R., Dickstein, K., Gerstein, H.C., Johnston, P., Køber, L.V., Lawson, F., Lewis, E.F., Maggioni, A.P., McMurray, J.J.V., Ping, L., Probstfield, J.L., Solomon, S.D., Tardif, J.-C., Wu, Y., Pfeffer, M.A., Barkoudah, E., Brahimi, A., Charytan, D., Finn, P., Flynn, A., Hartley, L.H., Henderson, G., Joseph, J., Odutayo, K., Rajesh, V., Vazir, A., Weinrauch, L., Del Prato, S., Petrie, J., Kaplan, A., Lieberman, P., Zuraw, B.L., O'Reilly, E., Patel, K., Allen, P., Scarpa, A., Schattner, M., Granger, C., Rouleau, J., DeMets, D., Chaturvedi, N., Raccah, D., Aizenberg, D., Alvarez, C., Alvarisqueta, A., Baccaro, C., Bartolacci, I., Bordonava, A., Bustamante Labarta, M., Caccavo, A., Calella, P., Cantero, M., Codutti, R., Commendatore, V., Costamagna, O., Cuello, J., Fernandez, A., Garcia Duran, R., Gomez Vilamajo, O., Gorban De Lapertosa, S., Grinfeld, D., Hermida, S., Lagrutta, M., Leon De La Fuente, R., Licheri, A., Luciardi, H., Mackinnon, I., Maffei, L., Marino, J., Montaña, O., Novaretto, L., Orio, S., Orlandini, A., Oviedo, A., Pérez Manghi, F., Patocchi, C., Ramos, H., Rolandi, F., Saa Zarandon, R., Saavedra, S.S., Schiavi, L., Schygiel, P., Trivi, M., Ulla, M., Urdiales, P., Vallejos, J., Vico, M., Waisman, F., Amerena, J., Paul, V., Sangla, K., Van Gaal, W., Yeap, B., Fasching, P., Pieber, T., Danilova, L., Mitkovskaya, N., Sudzhaeva, S., Mathieu, C., Pouleur, Anne-Catherine, Botelho, R.V., Cerqueira, M.J., Chacra, A., Dos Santos, F., Feitosa, G., Forti, A., Golbert, M., Halpern, A., Hissa, M., Lisboa, H., Moraes, J., Nery, M., Quadros, A., Raduan, R., Reis, G., Ribeiro Filho, F., Rollin, G., Rossi, P., Santos, E., Sgarbi, J., Souza, J., Souza, M.R., Delchev, A., Ivanov, V., Klyuchkova, N., Kyoleyan, M., Levterov, G., Lucheva, M., Raev, D., Shumkova, R., Tokmakova, M., Tzekova, M., Yordanov, V., Bailey, G., Bertrand, O., Bhargava, R., Burton, J., Campeau, J., Dumas, R., Filteau, P., Syan, G., Syan, R., Tsoukas, G., Warnica, J.W., Acuña, S., Araneda, G., Bunster, L., Cobos, L., Corbalán, R., Dussaillant, G., Eggers, G., Florenzano, F., Godoy, G., Huidobro, L.A., Lahsen, R., Lanas, F., Larenas, G., Manriquez, L., Medina, M., Palma, J.C., Perez, L., Potthoff, S., Raffo, C., Reyes, E., Saavedra, V., Sanhueza, P., Sepulveda, P., Solis, C.L., Soto, N., Torres, C., Westerberg, B., Yañez, M., Chen, L., Dong, Y., Du, J., Guo, X., Han, P., Hu, T., Jiang, B., Ke, Y., Li, Z., Lu, J., Ma, C., Peng, Y., Shi, Y., Su, G., Tang, B., Xu, B., Yang, J., Yang, Y., Accini, J.L., Arteaga, J.M., Botero, R., Castillo, G., Coronel, J., Cure, C., Garcia, H., Garcia, L., Gomez, C., Hernandez Triana, E., Hernandez, H., Lopez, M., Lopez, P., Manzur, F., Molina, D., Rodriguez, J., Sanchez Vallejo, G., Yupanqui, H., Bronnum-Schou, J., Kaiser Nielsen, P., Nielsen, H., Rønne, H., Rasmussen, S., Rungby, J., Skagen, K., Thomsen, K.K., Torp-Pedersen, C., Duarte-Vera, Y., Marmol Alvear, R., Peñaherrera-Patiño, C., Assaad, S., Shelbaya, S., Ambos, A., Jakovlev, U., Lubi, M., Märtsin, K., Rosenthal, S., Vides, H., Alanko, J., Korsoff, P., Koski, A.-M., Lahtela, J., Nelimarkka, L., Tuomilehto, J., Cariou, B., Catargi, B., Ducloux, R., Hadjadj, S., Kerlan, V., Malecot, J.-M., Petit, C., Rodier, M., Chumburidze, V., Glonti, S., Lominadze, Z., Todua, F., Contzen, C., Marck, C., Fischer, H., Hagenow, A., Himpel-Bönninghoff, A., Kihm, L., Killat, H., Kleinertz, K., Kosch, C., Kreutzmann, K., Lappo, M., Mertes, B., Piechatzek, R., Prohaska, M., Rinke, A., Schellenberg, D., Toursarkissian, N., Arango, J., Gonzalez, R., Granados, A., Herrera, M., Montenegro, P., Munoz, R., Rodriguez, E., Turcios, E., Villalobos, R., Wyss, F., Hatterjee, S., Chopda, M., Chopra, V., Deshpande, N., Dutta, R., Dwivedi, S., Gandhi, P., Gupta, S.K., Gupta, J.B., Gupta, S., Hiregouder, N., Jha, S., Joshi, A., Khan, N., Kumbla, M., Magdum, M., Murthy, K., Prabhu, M., Sahay, R., Sethuraman, S., Shah, N., Shamanna, P., Singh, K.S., Singh, P., Somasekharan, A., Sreenivasamurthy, L., Supe, P., Adawi, F., Atar, S., Cohen, O., Efrati, S., Karnieli, E., Klainman, E., Minuchin, O., Mosenzon, O., Stern, N., Turgeman, Y., Wainstein, J., Aimaretti, G., Berra, C., Ciardullo, A.V., Consoli, A., Cucinotta, D., Di Marco, S., Giorda, C., Giordano, C., Mannucci, E., Orsi, E., Piatti, P., Pontiroli, A., Ponzani, P., Pozzilli, P., Rivellese, A., Akahori, H., Eki, Y., Fujii, K., Hata, Y., Himeno, H., Hirayama, A., Kishimoto, I., Kobayashi, Y., Miyaoka, H., Niiya, T., Nishi, Y., Nozaki, A., Nunohiro, T., Saito, T., Satoh, Y., Takahashi, A., Takahashi, J., Takase, H., Takase, S., Tsuboko, Y., Tsujimoto, M., Tsujino, M., Tsuzuki, M., Watanabe, S., Yamada, T., Chung, W.J., Rim, S., Jang, H., Kim, U., Chung, C.H., Shin, S.-H., Kim, K., Kim, J., Rha, S., Lee, N.H., Kim, C.-J., Park, K.S., Amolina, I., Ducena, K., Helda, R., Konrade, I., Pirags, V., Sime, I., Sokolova, J., Kakariekiene, V., Kavaliauskiene, R., Petrulioniene, Z., Sakalyte, G., Urboniene, A., Zarankiene, R., Uribe, M., Garcia-Hernandez, P., Garza, J., Vazquez-Garcia, A., Gonzalez, J.G., Escalante, M., Zavala, A., Bayram, E., Hernandez-Muñuzuri, J., Ramos, Lopez, G.A., Lujan, J., Garcia-Soria, M., Velasco-Sanchez, R., Rodriguez, I., Jimenez, S., Galeana, C., Lara, S., Garcia-Castillo, A., Castro, M.G., Aguilar-Orozco, R., Lopez Rosas, E., Vidrio, M., Llamas, G., Stobschinski De Alba, C.A., Carranza-Madrigal, J., Cardosa-Torres, F., Cornel, J.H., Dekkers, P., Frederiks, J., Hermans, W., Lok, D., Meeder, J., Nierop, P., Cooper, J., Lappegård, K.T., Nedrebø, B.G., Castro, E., Gonzalez Castillo, B., Gonzalez, E., Nieto Ortega, R., Andrade, M., Calderon, J., Chavez, C., Correa Flores, R.M., Farfan, J., Lu, L., Luque, E., Manrique, H., Mogrovejo, W., Pariona-Javier, M., Pinto, M., Roldan, Y., Zubiate, C., Dans, A., Gomez, M.H., Panelo, A., Rey, N., Sulit, D.J., Sy, R.A., Timonera, M., Bednarski, J., Bijata-Bronisz, R., Bryniarski, L., Busz-Papiez, B., Czajkowska-Kaczmarek, E., Drzewiecka, A., Dulak, E., Gorska, M., Hamankiewicz, M., Janik, K., Kincel, K., Konieczny, M., Lubinski, A., Olszanecka-Glinianowicz, M., Ponikowski, P., Pulka, G., Rekosz, J., Skudlarski, D., Szymkowiak, K., Wilczewski, P., Bragança, N., Duarte, J., Monteiro, P., Rodrigues, E., Vinhas, M., Adina, P.-M., Avram, R.I., Cif, A., Creteanu, G., Dragomir, D., Ferariu, I.E., Iancu, A.-C., Istratoaie, O., Ivanica, G., Lichiardopol, R., Militaru, C., Minescu, B., Onaca, A., Pintilei, E., Podoleanu, C., Pop, L., Popa, B., Ranetti, A.E., Rosu, D., Tase, A., Tesloianu, D.N., Vinereanu, D., Ageev, F., Akhmedzhanov, N., Barbarash, O., Barbarich, V., Belousov, Y., Berns, S., Bokarev, I., Bolshakova, O., Boyarkin, M., Chumakova, G., Dmitry, P., Fitilev, S., Galyavich, A., Glezer, M., Ivanova, L., Kalashnikov, V., Kalashnikova, M., Karpov, Y., Khaisheva, L., Khalimov, Y., Kobalava, Z., 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Boberg, G., Jörneskog, G., Jendle, J., Mathiesen, U., Svensson, K.-A., Torstensson, I., Vasko, P., Moccetti, T., Chiang, C.-E., Chiu, Y.-W., Huang, T.-Y., Lu, C.-H., Pei, D., Shyu, K.-G., Ueng, K.-C., Wang, T.-D., Abid, M., Ben Abdallah, N., Haouala, H., Slimane, H., Zidi, B., Bascil Tutuncu, N., Camsari, A., Delibasi, T., Dinccag, N., Kultursay, H., Oto, A., Sahin, M., Saygili, F., Yigit, Z., Zorkun, C., Karpenko, O., Korpachev, V., Koval, O., Maslyanko, V., Perepelytsya, M., Pertseva, T., Petrosyan, O., Rudenko, L., Sychov, O., Synenko, V., Tseluyko, V., Zhuravleva, L., Al Mahmeed, W., Kaddaha, G.M., Andrews, R., Bain, S., Basu, A., Bhatnagar, D., Bickerton, A., Browne, D., Gibson, M., Hammond, P., Hanna, F., Issa, B., Jaap, A., Joseph, F., Jude, E., Kelly, C., Khan, A., Malik, R., Mukhopadhyay, B., O'Kane, M., Rayman, G., Robinson, A., Rooney, D., Sainsbury, C., Saravanan, P., Shakher, J., Singh, B., Turner, J., Whitelaw, D., Wilding, J., Wiles, P., Adenuga, B., Ahmad, Z., 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Enciso, F., Gaztambide, S., Marin, F., Martin, V., Mauricio, D., Soto, A., Vida, M., Boberg, G., Jörneskog, G., Jendle, J., Mathiesen, U., Svensson, K.-A., Torstensson, I., Vasko, P., Moccetti, T., Chiang, C.-E., Chiu, Y.-W., Huang, T.-Y., Lu, C.-H., Pei, D., Shyu, K.-G., Ueng, K.-C., Wang, T.-D., Abid, M., Ben Abdallah, N., Haouala, H., Slimane, H., Zidi, B., Bascil Tutuncu, N., Camsari, A., Delibasi, T., Dinccag, N., Kultursay, H., Oto, A., Sahin, M., Saygili, F., Yigit, Z., Zorkun, C., Karpenko, O., Korpachev, V., Koval, O., Maslyanko, V., Perepelytsya, M., Pertseva, T., Petrosyan, O., Rudenko, L., Sychov, O., Synenko, V., Tseluyko, V., Zhuravleva, L., Al Mahmeed, W., Kaddaha, G.M., Andrews, R., Bain, S., Basu, A., Bhatnagar, D., Bickerton, A., Browne, D., Gibson, M., Hammond, P., Hanna, F., Issa, B., Jaap, A., Joseph, F., Jude, E., Kelly, C., Khan, A., Malik, R., Mukhopadhyay, B., O'Kane, M., Rayman, G., Robinson, A., Rooney, D., Sainsbury, C., Saravanan, P., Shakher, J., Singh, 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S., Mariash, C., Martin, P., Mathew, J., May, M., Mayfield, R., McCall, A., Mcdaniel, C., Mcgrew, F., Mckenzie, M., Mefford, I., Mehta, A., Mikdadi, G., Mikhail, M., Monchamp, T., Moore, C., Moran, M., Morrar, N., Mosley, J., Mulford, M., Murray, J., Nakhle, S., Nallasivan, M., Odio, A., Oh, C., Olelewe, S., Palchick, B., Paliwal, Y., Papademetriou, V., Parikh, N., Patel, S., Phillips, L., Pitts, T., Prieto, F., Puttnam, R., Quyyumi, A., Randhawa, P., Rendell, M., Rhie, F., Roberts, J., Robinson, J., Robinson, M., Rubino, J., Ryan, E., Saathoff, S., Sachmechi, I., Saifi, A., Salacata, A., Sanders, R., Sanson, J., Savin, V., Schabauer, A., Schmedtje, J., Schwartz, A., Scott, C., Selagamsetty, M., Shannon, M., Shaw, S., Singal, D., Sjoberg, R., Smith, K., Sofley, C., Sonn, A., Sorof, S., Soroka, E., Spellman, C.W., Steinhoff, J., Suresh, D., Tahir, M., Tanenberg, R., Thawani, H., Thomson, S., Thrasher, J., Trachtenbarg, D., Trotta, M., Tuan, W., Twahirwa, M., Umpierrez, G., Vaid, B., Vance, C., Wang, T., Warner, A., Watson, H., Weber, S., Webster, B., Weindorff, K., Welch, M., Welker, J., White, A., White, L., Williams, M., Wu, W.-C., Wynne, A., Yocono, M., and Yuen, K.
Abstract: Background: Cardiovascular (CV) disease is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Furthermore, patients with T2DM and acute coronary syndrome (ACS) have a particularly high risk of CV events. The glucagonlike peptide 1 receptor agonist, lixisenatide, improves glycemia, but its effects on CV events have not been thoroughly evaluated. Methods: ELIXA (www.clinicaltrials.gov no. NCT01147250) is a randomized, double-blind, placebo-controlled, parallelgroup, multicenter study of lixisenatide in patients with T2DM and a recent ACS event. The primary aim is to evaluate the effects of lixisenatide on CV morbidity and mortality in a population at high CV risk. The primary efficacy end point is a composite of time to CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. Data are systematically collected for safety outcomes, including hypoglycemia, pancreatitis, and malignancy. Results: Enrollment began in July 2010 and ended in August 2013; 6,068 patients from 49 countries were randomized. Of these, 69% are men and 75% are white; at baseline, the mean ± SD age was 60.3 ± 9.7 years, body mass index was 30.2 ± 5.7 kg/m2, and duration of T2DM was 9.3±8.2 years. The qualifying ACS wasamyocardial infarctionin83% and unstableangina in 17%. The study will continue until the positive adjudication of the protocol-specified number of primary CV events. Conclusion: ELIXA will be the first trial to report the safety and efficacy of a glucagon-like peptide 1 receptor agonist in people with T2DM and high CV event risk. © 2015 Elsevier Inc. All rights reserved.
Published: 2015

23. Mapping the spectral soil quality index (SSQI) using airborne imaging spectroscopy

Author: Paz-Kagan, T., Zaady, E., Salbach, Christoph, Schmidt, Andreas, Lausch, Angela, Zacharias, Steffen, Notesco, G., Ben-Dor, E., Karnieli, A., Paz-Kagan, T., Zaady, E., Salbach, Christoph, Schmidt, Andreas, Lausch, Angela, Zacharias, Steffen, Notesco, G., Ben-Dor, E., and Karnieli, A.
Abstract: Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.
Published: 2015

24. Comparing the effect of preprocessing transformations on methods of land-use classification derived from spectral soil measurements

Author: Rozenstein, O., Paz-Kagan, T., Salbach, Christoph, Karnieli, A., Rozenstein, O., Paz-Kagan, T., Salbach, Christoph, and Karnieli, A.
Abstract: Advanced classifiers, e.g., partial least squares discriminant analysis (PLS-DA) and random forests (RF), have been recently used to model reflectance spectral data in general, and of soil properties in particular, since their spectra are multivariate and highly collinear. Preprocessing transformations (PPTs) can improve the classification accuracy by increasing the variability between classes while decreasing the variability within classes. Such PPTs are common practice prior to a PLS-DA, but are rarely used for RF. The objectives of this paper are twofold: to compare the performances of PLS-DA and RF for modeling the spectral reflectance of soil in changed land-uses with different treatments and to compare the effects of nine different PPTs on the prediction accuracy of each of these classification methods. Differences in six physical, biological, and chemical soil properties of changed land-uses from the northern Negev Desert in Israel were evaluated. Significant differences were found between soil properties, which are used to classify land-uses and treatments. Depending on the dataset, different PPTs improved the classification accuracy by 11%-24% and 32%-42% for PLS-DA and RF, respectively, in comparison to the spectra without PPT. Out of the PPTs tested, the generalized least squares weighting (GLSW)-based transformations were found to be the most effective for most classifications using both PLS-DA and RF. Our results show that both PLS-DA and RF are suitable classifiers for spectral data, provided that an appropriate PPT is applied.
Published: 2014

25. The effect of large-scale conservation programs on the vegetative development of China's Loess Plateau

Author: de Beurs, Kirsten M., Yan, Dong, Karnieli, Arnon, de Beurs, Kirsten M., Yan, Dong, and Karnieli, Arnon
Abstract: The Natural Forest Conservation Program (NFCP) in 1998 and the Grain for Green Program (GfGP) in 1999 were launched by the Chinese government to mitigate the degraded vegetation condition of China. In this study, we applied MODIS time series data to analyze the impacts of land cover change as a result of vegetation conservation programs on vegetation dynamics in the Chinese Loess Plateau from 2001 through 2009. We applied the seasonal Mann-Kendall trend test to the Normalized Difference Vegetation Index (NDVI) time series calculated based on the Nadir BRDF-Adjusted Reflectance data at 500 m (MCD43A4). In addition, to identify the changes at multiple scales, we also applied the seasonal Mann-Kendall trend test to the global Albedo product and NDVI calculated based on the Nadir BRDF-Adjusted Reflectance data at 0.05 deg CMG (MCD43C3 and MCD43C4). To understand the effect of grazing changes in grasslands we selected data from three separate Landsat tiles and two different years and calculated both NDVI and the Enhanced Vegetation Index (EVI). We show that the observed changes are the most widespread when observed with NDVI data at 0.05 resolution and the least widespread when observed with albedo data. The data at the higher spatial resolution of 500 m revealed that the rainfed croplands are changing (i.e., 69% exhibiting change) while the irrigated croplands remain stable (just 11% reveal change). This change appears consistent with the effect of GfGP. Both the counties with increased and degreased grazing reveal an increase in 500 m NDVI data. Thus, grazing analyses are more complicated for these areas.
Published: 2014

26. Initiation of once daily insulin detemir is not associated with weight gain in patients with type 2 diabetes mellitus: results from an observational study

Author: Yale, J-F, Damci, T, Kaiser, M, Karnieli, E, Khunti, K, Liebl, A, Baeres, FMM, Svendsen, AL, Ross, SA, Yale, J-F, Damci, T, Kaiser, M, Karnieli, E, Khunti, K, Liebl, A, Baeres, FMM, Svendsen, AL, and Ross, SA
Abstract: BACKGROUND: Obesity is common in type 2 diabetes (T2DM) and is associated with increased risk of morbidity and all-cause mortality. This analysis describes weight changes associated with insulin detemir initiation in real-life clinical practice. METHODS: Study of Once-Daily Levemir (SOLVE) was a 24-week international observational study of once-daily insulin detemir as add-on therapy in patients with T2DM receiving oral hypoglycaemic agents (OHAs). RESULTS: 17,374 participants were included in the analysis: mean age 62 ± 12 years; weight 80.8 ± 17.6 kg; body mass index (BMI) 29.2 ± 5.3 kg/m2; diabetes duration 10 ± 7 years; HbA1c 8.9 ± 1.6%. HbA1c decreased by 1.3 ± 1.5% during the study, with insulin doses of 0.27 ± 0.17 IU/kg. Patients with higher BMI had higher pre-insulin HbA1c, and similar reductions in HbA1c with insulin therapy. Weight decreased from 80.8 ± 17.6 kg to 80.3 ± 17.0 kg (change of -0.6 [95% CI -0.65; -0.47] kg), with 35% of patients losing >1 kg. Patients with the highest pre-insulin BMI lost the greatest amount of weight: BMI < 25: +0.8 [95% CI: 0.6; 0.9] kg, 25 ≤ BMI < 30: -0.2 [95% CI: -0.3; -0.8] kg, 30 ≤ BMI < 35: -1.0 [95% CI: -1.1; -0.8] kg; BMI ≥ 35: -1.9 [95% CI: -2.2; -1.6] kg. Minor hypoglycaemia decreased with increasing BMI: 2.3 and 1.3 events per patient year for BMI <25 and ≥ 35, respectively. CONCLUSIONS: Overall, patients with poorly controlled T2DM achieved significant reductions in HbA1c after initiation of once-daily insulin detemir therapy, without weight gain. The favourable impact of insulin detemir on weight may not apply to other insulin preparations. TRIAL REGISTRATIONS: ClinicalTrials.gov, NCT00825643 and NCT00740519.
Published: 2013

27. Comparison of UV-absorbing Nets in Pepper Crops: Spectral Properties, Effects on Plants and Pest Control

Author: Legarrea, Saioa, Karnieli, A., Fereres, Alberto, Weintraub, P. G., Legarrea, Saioa, Karnieli, A., Fereres, Alberto, and Weintraub, P. G.
Abstract: In horticultural crops, the use of screens to protect plants is the usual strategy in the Mediterranean area. Screen manufacturers offer a range of netting that vary in their UV-absorbing properties. We compared the photoeffects of seven different screens. Sweet pepper trials were conducted at the Gilat Research Center, Israel, where the spectral properties of the nets and their influence on pest infestation and crop development were evaluated. UV transmittance varied among the materials studied ranging from 40% to 70% of the incident radiation. BioNet white and P-Optinet, which absorbed and reflected the highest amount of UV radiation, performed the best protection against the main pepper pest (thrips, whiteflies and broad mites). Spectral measurements also showed that the photosynthetically active radiation differentially penetrated the nets, which together with the amount of UV absorbed by the screenings, resulted in a range of plant height and chlorophyll content. A global understanding of the UV-absorbing nets’ effect on pepper crops and their pests was evaluated in this work because of the importance of these screening to integrated pest management and sustainable agriculture production.
Published: 2009

28. Cloud formation and rainfalls generated by an artificial solar setting: a weather engineering project for fighting aridity

Author: Brenig, Léon, Zaady, Eli, Vigo-Aguiar, J., Karnieli, A., Fovel, R., Arbel, Ch., Al Baz, I., Offer, Zvi Yehoshua, Brenig, Léon, Zaady, Eli, Vigo-Aguiar, J., Karnieli, A., Fovel, R., Arbel, Ch., Al Baz, I., and Offer, Zvi Yehoshua
Abstract: info:eu-repo/semantics/published
Published: 2008

29. Cloud formation and rainfalls generated by an artificial solar setting: a weather engineering project for fighting aridity

Author: Brenig, Léon, Zaady, Eli, Vigo-Aguiar, J., Karnieli, A., Fovel, R., Arbel, Ch., Al Baz, I., Offer, Zvi Yehoshua, Brenig, Léon, Zaady, Eli, Vigo-Aguiar, J., Karnieli, A., Fovel, R., Arbel, Ch., Al Baz, I., and Offer, Zvi Yehoshua
Abstract: info:eu-repo/semantics/published
Published: 2008

30. Weather modification by artificial ground temperature inhomogeneity in semi arid regions

Author: Brenig, Léon, Offer, Zvi Yehoshua, Karnieli, A., Brenig, Léon, Offer, Zvi Yehoshua, and Karnieli, A.
Abstract: info:eu-repo/semantics/published
Published: 1995

31. Weather modification by artificial ground temperature inhomogeneity in semi arid regions

Author: Brenig, Léon, Offer, Zvi Yehoshua, Karnieli, A., Brenig, Léon, Offer, Zvi Yehoshua, and Karnieli, A.
Abstract: info:eu-repo/semantics/published
Published: 1995

32. Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements

Author: Kaufman, Y. J., Gitelson, A. A., Karnieli, A., Ganor, E., Fraser, R. S., Nakajima, T., Mattoo, S., Holben, B. N., Kaufman, Y. J., Gitelson, A. A., Karnieli, A., Ganor, E., Fraser, R. S., Nakajima, T., Mattoo, S., and Holben, B. N.
Abstract: Ground-based measurements of the solar transmission and sky radiance in a horizontal plane through the Sun are taken in several geographical regions and aerosol types: dust in a desert transition zone in Israel, sulfate particles in Eastern and Western Europe, tropical aerosol in Brazil, and mixed continental/maritime aerosol in California. Stratospheric aerosol was introduced after the eruption of Mount Pinatubo in June 1991. Therefore measurements taken before the eruption are used to analyze the properties of tropospheric aerosol; measurements from 1992 are also used to detect the particle size and concentration of stratospheric aerosol. The measurements are used to retrieve the size distribution and the scattering phase function at large scattering angles of the undisturbed aerosol particles. The retrieved properties represent an average on the entire atmospheric column. A comparison between the retrieved phase function for a scattering angle of 120°, with phase function predicted from the retrieved size distribution, is used to test the assumption of particle homogeneity and sphericity in radiative transfer models (Mie theory). The effect was found to be small (20% ± 15%). For the stratospheric aerosol (sulfates), as expected, the phase function was very well predicted using the Mie theory. A model with a power law size distribution, based on the spectral dependence of the optical thickness, α, cannot estimate accurately the phase function (up to 50% error for λ = 0.87µm). Before the Pinatubo eruption the ratio between the volumes of sulfate and coarse particles was very well correlated with α. The Pinatubo stratospheric aerosol destroyed this correlation. The aerosol optical properties are compared with analysis of the size, shape, and composition of the individual particles by electron microscopy of in situ samples. The measured volume size distributions before the injection of stratospheric aerosol consistently show two modes, sulfate particles with r m < 0.
Published: 1994

33. Storm runoff forecasting model incorporating spatial data

Author: Fogel, Martin M., Gray, Lloyd W., Guertin, David P., Hutchinson, Charles F., Lehman, Gordon S., Karnieli, Arnon,1952, Fogel, Martin M., Gray, Lloyd W., Guertin, David P., Hutchinson, Charles F., Lehman, Gordon S., and Karnieli, Arnon,1952
Abstract: This study is concerned with design forecasting of storm hydrographs with emphasis on runoff volume and peak discharge. The objective of the study was to develop, calibrate and test a method for forecasting storm runoff from small semi-arid watersheds using an available prediction model. In order to turn the selected prediction model into a forecasting model an objective procedure in terms of an API-type model was developed for evaluating the soil moisture deficit in the upper soil layer at the beginning of each storm. Distinction was made between the physically-based parameters and the other fitting parameters. The rainfall excess calculation was computed by solving the Green and Ampt equation for unsteady rainfall conditions using the physically-based parameters. For the physically-based parameters a geographic information system was developed in order to account for the variability in time and space of the input data and the watershed characteristics and to coregister parameters on a common basis. The fitting parameters were used to calibrate the model on one subwatershed in the Walnut Gulch Experimental Watershed while the physically-based parameters remained constant. Two objective functions were selected for the optimization procedure. These functions expressed the goodness of fit between the calculated hydrograph volume and peak discharge and the observed volume and peak discharge. Linear relationships between the effective matric potential parameter and the two objective functions obtained from the sensitivity analyses made it possible to develop a bilinear interpolation algorithm to minimize, simultaneously, the difference between the calculated and observed volume and peak discharge. The prediction mode of the model was tested both on different storm events on the same subwatershed and on another subwatershed with satisfactory results. In the prediction mode the effective matric potential parameter was allowed to vary from storm to storm, however, in the f
Published: 1988

34. Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images

Author: Oriani, F, Ohana-Levi, N, Marra, F, Straubhaar, J, Mariethoz, Gregoire, Renard, Philippe, Karnieli, A, Morin, E, Oriani, F, Ohana-Levi, N, Marra, F, Straubhaar, J, Mariethoz, Gregoire, Renard, Philippe, Karnieli, A, and Morin, E
Abstract: The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high-resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple-point simulation techniques dealing with extremely nonstationary heterogeneities and extensive data sets.

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34 results on '"Karnieli, A."'

1. Designing Nonlinear Photonic Crystals for High-Dimensional Quantum State Engineering

2. Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection

3. DropCompute: simple and more robust distributed synchronous training via compute variance reduction

4. Remote sensing of geomorphodiversity linked to biodiversity — Part III: Traits, processes and remote sensing characteristics

5. Sustainability challenges for the social-environmental systems across the Asian Drylands Belt

6. DeepShadow: Neural Shape from Shadow

7. Deepening the teaching and learning of clinical communication: The importance of reflection and feedback in health education

8. SPDCinv: Inverse Quantum-Optical Design of High-Dimensional Qudits

9. Inverse Design of Quantum Holograms in Three-Dimensional Nonlinear Photonic Crystals

10. Linking remote sensing and geodiversity and their traits relevant to biodiversity—Part I: Soil characteristics

11. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

12. Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images

13. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

14. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

15. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

16. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

17. Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

18. Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

19. Saxagliptin and Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus

20. Saxagliptin and Cardiovascular Outcomes in Patients with Type 2 Diabetes Mellitus

21. Retrieval of seasonal dynamics of forest understory reflectance from semiarid to boreal forests using MODIS BRDF data

22. Rationale, design, and baseline characteristics in Evaluation of LIXisenatide in Acute Coronary Syndrome, a long-term cardiovascular end point trial of lixisenatide versus placebo

23. Mapping the spectral soil quality index (SSQI) using airborne imaging spectroscopy

24. Comparing the effect of preprocessing transformations on methods of land-use classification derived from spectral soil measurements

25. The effect of large-scale conservation programs on the vegetative development of China's Loess Plateau

26. Initiation of once daily insulin detemir is not associated with weight gain in patients with type 2 diabetes mellitus: results from an observational study

27. Comparison of UV-absorbing Nets in Pepper Crops: Spectral Properties, Effects on Plants and Pest Control

28. Cloud formation and rainfalls generated by an artificial solar setting: a weather engineering project for fighting aridity

29. Cloud formation and rainfalls generated by an artificial solar setting: a weather engineering project for fighting aridity

30. Weather modification by artificial ground temperature inhomogeneity in semi arid regions

31. Weather modification by artificial ground temperature inhomogeneity in semi arid regions

32. Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements

33. Storm runoff forecasting model incorporating spatial data

34. Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images

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34 results on '"Karnieli, A."'

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