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News Article | February 16, 2017
Site: phys.org

Through experiments with mice, Brazilian and U.S. researchers have demonstrated that a brain region called the central nucleus of the amygdala is responsible for organizing the actions involved in predatory hunting. They have also shown that this process occurs in two distinct neural networks: one that organizes prey pursuit and capture; and another that controls the jaw and neck movements required for the predator to deliver a lethal bite. "The modular way in which control is exerted is relevant. The study provides novel details of the neural control of craniofacial muscles, potentially contributing to an understanding of the pathologies that affect this region. In addition, practical applications are being considered in the field of engineering, especially with regard to the development of robotics algorithms," said Ivan de Araujo, Associate Professor of Psychiatry at the Yale School of Medicine in the United States. Araujo's main focus in his laboratory work is research on the neural basis for the feeding behavior of mammals. He began partnering with Newton Canteras, a professor at the University of São Paulo's Biomedical Science Institute (ICB-USP) in Brazil, because of their shared interest in understanding how the hunt for food is controlled under conditions close to those prevailing in nature. Previous studies by Canteras's group at ICB-USP had shown that the central nucleus of the amygdala is strongly activated when an animal is hunting. "Canteras has ample experience in research on hunting behavior, and after members of his group visited my lab, we decided to apply the insect predation model to genetically modified mice," Araujo recalled. Several experiments and techniques were used with the aim of "interrogating" the neurons of the central amygdala and thereby discovering the pathways involved when an animal is hunting for prey. Motta explained that one of the most important techniques was optogenetics, which uses laser light to activate and deactivate neurons almost instantaneously. "Using a viral vector, we inserted into the neurons in the region of interest a protein that acts as a cellular receptor and makes the neurons respond to light. Depending on the receptor inserted, neurons can be activated or deactivated by the light stimulus," Motta said. "In addition, we inserted optical fibers to transmit the light to the site. The time between switching the laser on or off and the activation or deactivation of neurons is very short, allowing neural function to be correlated with the behavior observed." The same technique, in which neurons are modified by viral vectors, can be used to make activation and deactivation even more specific, distinguishing between glutamatergic neurons (which release the neurotransmitter glutamate) and GABAergic neurons (which secrete gamma aminobutyric acid), for example. "We performed experiments with animals that expressed the enzyme Cre recombinase only in glutamatergic neurons, for example. Next, we inserted a Cre-dependent virus that took the light-sensitive receptor only to neurons marked with the enzyme. In this way, we were able to activate or deactivate only the population of glutamatergic neurons. Our aim was to find out what happens in this case," Motta said. Another possibility is selectively killing a specific group of neurons by injecting a Cre-dependent virus capable of encoding caspases, a family of proteases that convey signals to cells that cause the cells to enter apoptosis (programmed cell death). This series of experiments enabled the researchers to map the two neural pathways that coordinate hunting behavior, both mediated by GABAergic neurons. One extends from the central nucleus of the amygdala to a region of the brainstem called the parvocellular reticular formation (PCRt). The neurons here project to the nucleus ambiguus of the accessory nerve (cranial nerve XI), which controls head movement, and the trigeminal motor nucleus, which is responsible for jaw movement. "The experiments showed, for example, that if we eliminate the neurons that project to the trigeminal motor nucleus, the animal engages in prey pursuit but is unable to deliver a lethal bite," Motta said. "On the other hand, it continues normally chewing the food offered in the lab, showing that feeding behavior is controlled by a different neural circuit." The second pathway runs from the central nucleus of the amygdala to the periaqueductal gray matter (PAG). Located in the midbrain, the PAG projects to the spinal cord and mediates motor responses consistent with fight-or-flight reactions. "When the neurons in this pathway were eliminated, the latency to begin pursuing prey increased significantly, but a killing bite was easily delivered once the prey had been captured, because the PCRt pathway was functioning normally," Motta said. Another experiment measured bite force, which did not change after elimination of PAG pathway neurons but decreased sharply after elimination of PCRt pathway neurons. For Canteras, the results of this research break a longstanding paradigm in neuroscience, which is the idea that the central amygdala is the region responsible for organizing fear-related behavior, such as freezing in the face of a larger predator or rolling over and demonstrating submission to a hierarchically superior member of the same community. The initial experiments showed that when the central nucleus of the amygdala was stimulated by light, instead of behaving defensively, which would indicate fear, the animals began masticating, even without having any food in their mouths. "We've now shown irrefutably that the central amygdala organizes hunting behavior and that in this system, there may be mechanisms that make the animal stop hunting in adverse environmental conditions," Canteras said. "So what was previously interpreted as fear might just be a signal to stop hunting, for lack of favorable conditions." Explore further: Scientists switch on predatory kill instinct in mice More information: Wenfei Han et al. Integrated Control of Predatory Hunting by the Central Nucleus of the Amygdala, Cell (2017). DOI: 10.1016/j.cell.2016.12.027


News Article | February 15, 2017
Site: www.eurekalert.org

Researchers show, in an article in Cell, that the central nucleus of the amygdala is the brain region responsible for articulating the different skills involved in pursuing and killing prey For scientists who study the brain, predatory hunting, upon which many wild animals depend for survival, is a complex behavior involving different skills that must be exercised in an efficient and articulated manner if the predator is to succeed. Through experiments with mice, Brazilian and US researchers have demonstrated that a brain region called the central nucleus of the amygdala is responsible for organizing the actions involved in predatory hunting. They have also shown that this process occurs in two distinct neural networks: one that organizes prey pursuit and capture and another that controls the jaw and neck movements required for the predator to deliver a lethal bite. "The modular way in which control is exerted is relevant. The study provides novel details of the neural control of craniofacial muscles, potentially contributing to an understanding of the pathologies that affect this region. In addition, practical applications are being considered in the field of engineering, especially with regard to the development of robotics algorithms," said Ivan de Araujo, Associate Professor of Psychiatry at the Yale School of Medicine in the United States. Araujo's main focus in his laboratory work is research on the neural basis for the feeding behavior of mammals. He began partnering with Newton Canteras, a professor at the University of São Paulo's Biomedical Science Institute (ICB-USP) in Brazil, because of their shared interest in understanding how the hunt for food is controlled under conditions close to those prevailing in nature. Previous studies by Canteras's group at ICB-USP had shown that the central nucleus of the amygdala is strongly activated when an animal is hunting. "Canteras has ample experience in research on hunting behavior, and after members of his group visited my lab, we decided to apply the insect predation model to genetically modified mice," Araujo recalled. Several experiments and techniques were used with the aim of "interrogating" the neurons of the central amygdala and thereby discovering the pathways involved when an animal is hunting for prey. Motta explained that one of the most important techniques was optogenetics, which uses laser light to activate and deactivate neurons almost instantaneously. "Using a viral vector, we inserted into the neurons in the region of interest a protein that acts as a cellular receptor and makes the neurons respond to light. Depending on the receptor inserted, neurons can be activated or deactivated by the light stimulus," Motta said. "In addition, we inserted optical fibers to transmit the light to the site. The time between switching the laser on or off and the activation or deactivation of neurons is very short, allowing neural function to be correlated with the behavior observed." The same technique, in which neurons are modified by viral vectors, can be used to make activation and deactivation even more specific, distinguishing between glutamatergic neurons (which release the neurotransmitter glutamate) and GABAergic neurons (which secrete gamma aminobutyric acid), for example. "We performed experiments with animals that expressed the enzyme Cre recombinase only in glutamatergic neurons, for example. Next, we inserted a Cre-dependent virus that took the light-sensitive receptor only to neurons marked with the enzyme. In this way, we were able to activate or deactivate only the population of glutamatergic neurons. Our aim was to find out what happens in this case," Motta said. Another possibility is selectively killing a specific group of neurons by injecting a Cre-dependent virus capable of encoding caspases, a family of proteases that convey signals to cells that cause the cells to enter apoptosis (programmed cell death). This series of experiments enabled the researchers to map the two different neural pathways that together coordinate hunting behavior, both mediated by GABAergic neurons. One extends from the central nucleus of the amygdala to a region of the brainstem called the parvocellular reticular formation (PCRt). The neurons here project to the nucleus ambiguus of the accessory nerve (cranial nerve XI), which controls head movement, and the trigeminal motor nucleus, which is responsible for jaw movement. "The experiments showed, for example, that if we eliminate the neurons that project to the trigeminal motor nucleus, the animal engages in prey pursuit but is unable to deliver a lethal bite," Motta said. "On the other hand, it continues normally chewing the food offered in the lab, showing that feeding behavior is controlled by a different neural circuit." The second pathway runs from the central nucleus of the amygdala to the periaqueductal gray matter (PAG). Located in the midbrain, the PAG projects to the spinal cord and mediates motor responses consistent with fight-or-flight reactions. "When the neurons in this pathway were eliminated, the latency to begin pursuing prey increased significantly, but a killing bite was easily delivered once the prey had been captured, because the PCRt pathway was functioning normally," Motta said. Another experiment measured bite force, which did not change after elimination of PAG pathway neurons but decreased sharply after elimination of PCRt pathway neurons. For Canteras, the results of this research break a longstanding paradigm in neuroscience, which is the idea that the central amygdala is the region responsible for organizing fear-related behavior, such as freezing in the face of a larger predator or rolling over and demonstrating submission to a hierarchically superior member of the same community. The initial experiments showed that when the central nucleus of the amygdala was stimulated by light, instead of behaving defensively, which would indicate fear, the animals began masticating, even without having any food in their mouths. "We've now shown irrefutably that the central amygdala organizes hunting behavior and that in this system, there may be mechanisms that make the animal stop hunting in adverse environmental conditions," Canteras said. "So what was previously interpreted as fear might just be a signal to stop hunting, for lack of favorable conditions."


News Article | February 21, 2017
Site: phys.org

What are they going to be? Hematopoietic stem cells under the microscope: New methods are helping the Helmholtz scientists to predict how they will develop. Credit: Helmholtz Zentrum München Autonomous driving, automatic speech recognition, and the game Go: Deep Learning is generating more and more public awareness. Scientists at the Helmholtz Zentrum München and their partners at ETH Zurich and the Technical University of Munich (TUM) have now used it to determine the development of hematopoietic stem cells in advance. In 'Nature Methods' they describe how their software predicts the future cell type based on microscopy images. Today, cell biology is no longer limited to static states but also attempts to understand the dynamic development of cell populations. One example is the generation of different types of blood cells from their precursors, the hematopoietic stem cells. "A hematopoietic stem cell's decision to become a certain cell type cannot be observed. At this time, it is only possible to verify the decision retrospectively with cell surface markers," explains Dr. Carsten Marr, head of the Quantitative Single Cell Dynamics Research Group at the Helmholtz Zentrum München's Institute of Computational Biology (ICB). He and his team have now developed an algorithm that can predict the decision in advance. So-called Deep Learning is the key. "Deep Neural Networks play a major role in our method," says Marr. "Our algorithm classifies light microscopic images and videos of individual cells by comparing these data with past experience from the development of such cells. In this way, the algorithm 'learns' how certain cells behave." Specifically, the researchers examined hematopoietic stem cells that were filmed under the microscope in the lab of Timm Schroeder at ETH Zurich. Using the information on appearance and speed, the software was able to 'memorize' the corresponding behaviour patterns and then make its prediction. "Compared to conventional methods, such as fluorescent antibodies against certain surface proteins, we know how the cells will decide three cell generations earlier," reports ICB scientist Dr. Felix Buggenthin, joint first author of the study together with Dr. Florian Büttner. But what is the benefit of this look into the future? As study leader Marr explains, "Since we now know which cells will develop in which way, we can isolate them earlier than before and examine how they differ at a molecular level. We want to use this information to understand how the choices are made for particular developmental traits." In the future, the focus will expand beyond hematopoietic stem cells. "We are using Deep Learning for very different problems with sufficiently large data records," explains Prof. Dr. Dr. Fabian Theis, ICB director and holder of the Mathematical Modelling of Biological Systems Chair at the TUM, who led the study together with Carsten Marr. "For example, we use very similar algorithms to analyse disease-associated patterns in the genome and identify biomarkers in clinical cell screens." Explore further: Enough is enough—stem cell factor Nanog knows when to slow down More information: Buggenthin, F. et al. (2017): Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods, DOI: 10.1038/nmeth.4182


News Article | February 27, 2017
Site: globenewswire.com

TIEDOTE,  27.2.2017  OSAKKEET SUOMEN HOIVATILAT OYJ NASDAQ HELSINGIN PÖRSSILISTALLE 1.3.2017 Suomen Hoivatilat Oyj listautuu Nasdaq Helsingin pörssilistalle keskiviikkona 1.3.2017. Suomen Hoivatilat Oyj:n osake on viimeistä kertaa kaupankäynnin kohteena First North Finland –markkinapaikalla tiistaina 28.2.2017. Suomen Hoivatilat Oyj:n perustiedot 1.3.2017: Kaupankäyntitunnus: HOIVA Liikkeeseenlaskijatunnus: HOIVA ISIN-koodi: FI4000148648 id: 119806 Segmentti: OMX HEL Equities intraday cross CCP / 201 Tikkivälitaulukko: XHEL other Equities / 228 MIC: XHEL Osakkeiden lukumäärä: 20.788.859 Kaupankäynti pörssilistalla alkaa: 1.3.2017 Toimiala: 8000 Rahoitus ICB Ylätoimialaluokka: 8600 Kiinteistöyhtiöt Markkina-arvoluokka: Keskisuuret yhtiöt Toimitusjohtaja: Jussi Karjula Osoite: Lentokatu 2         90460 Oulunsalo Puhelin: 0207 349 100 Internet: www.hoivatilat.fi      Nasdaq Helsinki Oy, Issuer Surveillance, survo@nasdaq.com, +358 9 6166 7260


News Article | February 27, 2017
Site: globenewswire.com

TIEDOTE,  27.2.2017  OSAKKEET SUOMEN HOIVATILAT OYJ NASDAQ HELSINGIN PÖRSSILISTALLE 1.3.2017 Suomen Hoivatilat Oyj listautuu Nasdaq Helsingin pörssilistalle keskiviikkona 1.3.2017. Suomen Hoivatilat Oyj:n osake on viimeistä kertaa kaupankäynnin kohteena First North Finland –markkinapaikalla tiistaina 28.2.2017. Suomen Hoivatilat Oyj:n perustiedot 1.3.2017: Kaupankäyntitunnus: HOIVA Liikkeeseenlaskijatunnus: HOIVA ISIN-koodi: FI4000148648 id: 119806 Segmentti: OMX HEL Equities intraday cross CCP / 201 Tikkivälitaulukko: XHEL other Equities / 228 MIC: XHEL Osakkeiden lukumäärä: 20.788.859 Kaupankäynti pörssilistalla alkaa: 1.3.2017 Toimiala: 8000 Rahoitus ICB Ylätoimialaluokka: 8600 Kiinteistöyhtiöt Markkina-arvoluokka: Keskisuuret yhtiöt Toimitusjohtaja: Jussi Karjula Osoite: Lentokatu 2         90460 Oulunsalo Puhelin: 0207 349 100 Internet: www.hoivatilat.fi      Nasdaq Helsinki Oy, Issuer Surveillance, survo@nasdaq.com, +358 9 6166 7260


News Article | February 27, 2017
Site: globenewswire.com

TIEDOTE,  27.2.2017  OSAKKEET SUOMEN HOIVATILAT OYJ NASDAQ HELSINGIN PÖRSSILISTALLE 1.3.2017 Suomen Hoivatilat Oyj listautuu Nasdaq Helsingin pörssilistalle keskiviikkona 1.3.2017. Suomen Hoivatilat Oyj:n osake on viimeistä kertaa kaupankäynnin kohteena First North Finland –markkinapaikalla tiistaina 28.2.2017. Suomen Hoivatilat Oyj:n perustiedot 1.3.2017: Kaupankäyntitunnus: HOIVA Liikkeeseenlaskijatunnus: HOIVA ISIN-koodi: FI4000148648 id: 119806 Segmentti: OMX HEL Equities intraday cross CCP / 201 Tikkivälitaulukko: XHEL other Equities / 228 MIC: XHEL Osakkeiden lukumäärä: 20.788.859 Kaupankäynti pörssilistalla alkaa: 1.3.2017 Toimiala: 8000 Rahoitus ICB Ylätoimialaluokka: 8600 Kiinteistöyhtiöt Markkina-arvoluokka: Keskisuuret yhtiöt Toimitusjohtaja: Jussi Karjula Osoite: Lentokatu 2         90460 Oulunsalo Puhelin: 0207 349 100 Internet: www.hoivatilat.fi      Nasdaq Helsinki Oy, Issuer Surveillance, survo@nasdaq.com, +358 9 6166 7260


News Article | February 27, 2017
Site: globenewswire.com

EXCHANGE NOTICE,  27 FEBRUARY 2017  SHARES LISTING ON THE OFFICIAL LIST OF NASDAQ HELSINKI: SUOMEN HOIVATILAT OYJ ON 1 MARCH 2017 The shares of Suomen Hoivatilat Oyj will be listed on the Official List of Nasdaq Helsinki on Wednesday 1 March 2017. The shares of Suomen Hoivatilat Oyj will be traded for the last time on First North Finland on Tuesday 28 February 2017. Basic information on Suomen Hoivatilat Oyj as of 1 March 2017: Trading code: HOIVA Issuer code: HOIVA ISIN-code: FI4000148648 Orderbook id: 119806 Market Segment: OMX HEL Equities intraday cross CCP / 201 Tick Size Table: XHEL other Equities / 228 MIC: XHEL Number of shares: 20 788 859 Listing date on the Official List: 1 March 2017 Industry: 8000 Financials ICB Supersector: 8600 Real Estate Market Cap Segment: Mid cap Managing director: Jussi Karjula Address: Lentokatu 2          FI-90460 Oulunsalo          FINLAND Phone: +358 207 349 100 Internet: www.hoivatilat.fi Nasdaq Helsinki Oy, Issuer Surveillance, survo@nasdaq.com, +358 9 6166 7260 * * * * * * * * * * * * * * * * * * TIEDOTE,  27.2.2017  OSAKKEET SUOMEN HOIVATILAT OYJ NASDAQ HELSINGIN PÖRSSILISTALLE 1.3.2017 Suomen Hoivatilat Oyj listautuu Nasdaq Helsingin pörssilistalle keskiviikkona 1.3.2017. Suomen Hoivatilat Oyj:n osake on viimeistä kertaa kaupankäynnin kohteena First North Finland –markkinapaikalla tiistaina 28.2.2017. Suomen Hoivatilat Oyj:n perustiedot 1.3.2017: Kaupankäyntitunnus: HOIVA Liikkeeseenlaskijatunnus: HOIVA ISIN-koodi: FI4000148648 id: 119806 Segmentti: OMX HEL Equities intraday cross CCP / 201 Tikkivälitaulukko: XHEL other Equities / 228 MIC: XHEL Osakkeiden lukumäärä: 20.788.859 Kaupankäynti pörssilistalla alkaa: 1.3.2017 Toimiala: 8000 Rahoitus ICB Ylätoimialaluokka: 8600 Kiinteistöyhtiöt Markkina-arvoluokka: Keskisuuret yhtiöt Toimitusjohtaja: Jussi Karjula Osoite: Lentokatu 2         90460 Oulunsalo Puhelin: 0207 349 100 Internet: www.hoivatilat.fi      Nasdaq Helsinki Oy, Issuer Surveillance, survo@nasdaq.com, +358 9 6166 7260


News Article | February 21, 2017
Site: www.eurekalert.org

Today, cell biology is no longer limited to static states but also attempts to understand the dynamic development of cell populations. One example is the generation of different types of blood cells from their precursors, the hematopoietic stem cells. "A hematopoietic stem cell's decision to become a certain cell type cannot be observed. At this time, it is only possible to verify the decision retrospectively with cell surface markers," explains Dr. Carsten Marr, head of the Quantitative Single Cell Dynamics Research Group at the Helmholtz Zentrum München's Institute of Computational Biology (ICB). He and his team have now developed an algorithm that can predict the decision in advance. So-called Deep Learning is the key. "Deep Neural Networks play a major role in our method," says Marr. "Our algorithm classifies light microscopic images and videos of individual cells by comparing these data with past experience from the development of such cells. In this way, the algorithm 'learns' how certain cells behave." Specifically, the researchers examined hematopoietic stem cells that were filmed under the microscope in the lab of Timm Schroeder at ETH Zurich.* Using the information on appearance and speed, the software was able to 'memorize' the corresponding behaviour patterns and then make its prediction. "Compared to conventional methods, such as fluorescent antibodies against certain surface proteins, we know how the cells will decide three cell generations earlier," reports ICB scientist Dr. Felix Buggenthin, joint first author of the study together with Dr. Florian Büttner. But what is the benefit of this look into the future? As study leader Marr explains, "Since we now know which cells will develop in which way, we can isolate them earlier than before and examine how they differ at a molecular level. We want to use this information to understand how the choices are made for particular developmental traits." In the future, the focus will expand beyond hematopoietic stem cells. "We are using Deep Learning for very different problems with sufficiently large data records," explains Prof. Dr. Dr. Fabian Theis, ICB director and holder of the Mathematical Modelling of Biological Systems Chair at the TUM, who led the study together with Carsten Marr. "For example, we use very similar algorithms to analyse disease-associated patterns in the genome and identify biomarkers in clinical cell screens." * The study described here is the latest result of a close cooperation between the ICB scientists and Prof. Dr. Timm Schroeder from the Department of Biosystems Science and Engineering at ETH Zurich in Basel, who previously worked at the Helmholtz Zentrum in Munich. In July 2016, the scientists jointly introduced a software in 'Nature Biotechnolgy' that allows to observe individual cells over many days and simultaneously measure their molecular properties. They also published a study in 'Nature' that already dealt with the development of hematopoietic stem cells. Using time-lapse microscopy, the researchers were able to observe the maturation of living hematopoietic stem cells with high precision while also quantifying certain proteins. Deep Learning algorithms simulate the learning processes in people using artificial neural networks. The principle functions particularly well when large quantities of data (Big Data) are available for training. Image recognition is one of Deep Learning's strengths. More decision layers are placed between the input (here, the cell image data) and the output (here, the prediction of the cell development) than usually found in neuronal networks, which is why the term "deep" is used. Buggenthin, F. et al. (2017): Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods, DOI: 10.1038/nmeth.4182 The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www. The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases. http://www. Technical University of Munich (TUM) is one of Europe's leading research universities, with more than 500 professors, around 10,000 academic and non-academic staff, and 40,000 students. Its focus areas are the engineering sciences, natural sciences, life sciences and medicine, com-bined with economic and social sciences. TUM acts as an entrepreneurial university that promotes talents and creates value for society. In that it profits from having strong partners in science and industry. It is represented worldwide with a campus in Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco, and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won recognition as a German "Excellence University." In international rankings, TUM regularly places among the best universities in Germany. http://www. Freedom and individual responsibility, entrepreneurial spirit and open-mindedness: ETH Zurich stands on a bedrock of true Swiss values. Our university for science and technology dates back to the year 1855, when the founders of modern-day Switzerland created it as a centre of innovation and knowledge. At ETH Zurich, students discover an ideal environment for independent thinking, researchers a climate which inspires top performance. Situated in the heart of Europe, yet forging connections all over the world, ETH Zurich is pioneering effective solutions to the global challenges of today and tomorrow. Some 500 professors teach around 20,000 students - including 4,000 doctoral students - from over 120 countries. Their collective research embraces many disciplines: natural sciences and engineering sciences, architecture, mathematics, system-oriented natural sciences, as well as management and social sciences. The results and innovations produced by ETH researchers are channelled into some of Switzerland's most high-tech sectors: from computer science through to micro- and nanotechnology and cutting-edge medicine. Every year ETH registers around 90 patents and 200 inventions on average. Since 1996, the university has produced a total of 330 commercial spin-offs. ETH also has an excellent reputation in scientific circles: 21 Nobel laureates have studied, taught or researched here, and in international league tables ETH Zurich regularly ranks as one of the world's top universities. http://www.


Research and Markets has announced the addition of the "Global Advanced Energy Storage Systems Market Analysis & Trends - Industry Forecast to 2025" report to their offering. The Global Advanced Energy Storage Systems Market is poised to grow at a CAGR of around 16.6% over the next decade to reach approximately $11.4 billion by 2025. This industry report analyzes the market estimates and forecasts for all the given segments on global as well as regional levels presented in the research scope. The study provides historical market data for 2013, 2014 revenue estimations are presented for 2015 and forecasts from 2016 till 2025. The study focuses on market trends, leading players, supply chain trends, technological innovations, key developments, and future strategies. Some of the prominent trends that the market is witnessing include growth in adoption of renewable energy; the current trend in the automotive sector is electric mobility, which creates the demand for the energy storage systems and growth in adoption of energy storage systems in transportation. Based on Technology the market is categorized into thermal technology, mechanical systems, electrochemical systems, electrical systems, chemical systems, biological systems and other technologies. Depending on Application the market is categorized into transportation, grid storage, fuel production, fuel delivery, electricity generation, electricity delivery and management, automotive and other applications. Depending on the end users the market is segmented by utility, residential & non-residential, industrial, electric vehicles, buildings and other end users. - The report provides a detailed analysis on current and future market trends to identify the investment opportunities - Market forecasts till 2025, using estimated market values as the base numbers - Key market trends across the business segments, Regions and Countries - Key developments and strategies observed in the market - Market Dynamics such as Drivers, Restraints, Opportunities and other trends - In-depth company profiles of key players and upcoming prominent players - Growth prospects among the emerging nations through 2025 - Market opportunities and recommendations for new investments 3 Market Overview 3.1 Current Trends 3.1.1 Growth in adoption of renewable energy 3.1.2 The current trend in the automotive sector is electric mobility, which creates the demand for the energy storage systems 3.1.3 Growth in adoption of energy storage systems in transportation 3.2 Drivers 3.3 Constraints 3.4 Industry Attractiveness 4 Advanced Energy Storage Systems Market, By Technology 4.1 Thermal technology 4.1.1.1 Thermal energy storage 4.1.1.2 Steam accumulator 4.1.1.3 Solar pond 4.1.1.4 Seasonal thermal energy storage 4.1.1.5 Molten salt 4.1.1.6 Ice storage 4.1.1.7 Pumped Heat Electrical Storage (PHES) 4.1.1.8 Eutectic system 4.1.1.9 Concentrated Solar Power (CSP) 4.2 Mechanical systems 4.2.1 Mechanical systems 4.2.1.1 Locomotive 4.2.1.2 Hydroelectric energy storage 4.2.1.3 Hydraulic accumulator 4.2.1.4 Flywheel 4.2.1.5 Fireless 4.2.1.6 Liquid Air Energy Storage (LAES) 4.2.1.7 Compressed Air Energy Storage (CAES) 4.3 Electrochemical systems 4.3.1.1 Rechargeable batteries 4.3.1.1.1.1 Sodium Sulphur (NaS) Battery 4.3.1.1.1.2 Nickel Cadmium 4.3.1.1.1.3 Lithium ion Battery 4.3.1.1.1.4 Lead Acid battery 4.3.1.2 Flow batteries 4.3.1.2.1.1 Zinc-Bromine battery (ZNBR) 4.3.1.2.1.2 Vanadium redox battery (VRB) 4.3.1.2.1.3 Redox-Flow 4.3.1.2.1.4 Iron- Chromium (ICB) 4.4.1 Electrical systems Market Forecast to 2025 (US$ MN) 4.4.1.1 Supercapacitors 4.4.1.2 Superconducting Magnets (SMES) 4.5 Chemical systems 4.5.1.1 Solar Power to gas 4.5.1.2 Liquid nitrogen 4.5.1.3 Hydrogen 4.5.1.4 Biofuels 4.5.1.5 Hydrated salts 4.6 Biological systems 4.6.1.1 Starch 4.6.1.2 Glycogen 4.7 Other Technologies 4.7.1.1 Synthetic Gas 4.7.1.2 Fuel cells 4.7.1.3 Pumped Hydro Storage 4.7.1.4 Super magnets 4.7.1.5 Advanced battery energy storage 5 Advanced Energy Storage Systems Market, By Application 5.1 Transportation 5.2 Grid Storage 5.3 Fuel production 5.4 Fuel delivery 5.5 Electricity generation 5.6 Electricity delivery and Management 5.7 Automotive 5.8 Other Applications 6 Advanced Energy Storage Systems Market, By End User 6.1 Utility 6.2 Residential & Non-Residential 6.3 Industrial 6.4 Electric Vehicles 6.5 Buildings 6.6 Other End Users 6.6.1 Other End Users Market Forecast to 2025 (US$ MN) 9 Leading Companies 9.1 ZBB systems 9.2 Toshiba Corporation 9.3 Samsung SDI, Co., Ltd 9.4 Saft 9.5 Nippon Chemi-Con Corporation 9.6 NGK Insulators Ltd 9.7 Maxwell Technologies Inc 9.8 LG Chem. Ltd 9.9 Hitachi Ltd 9.10 GS Yuasa Corporation 9.11 Exide Technologies 9.12 Enersys 9.13 China BAK Batteries, Inc 9.14 Calmac Manufacturing Corp 9.15 BYD Company Ltd. 9.16 Beacon Power LLC 9.17 Alevo 9.18 AES Energy Storage 9.19 ABB Limited 9.20 A123 Systems LLC For more information about this report visit http://www.researchandmarkets.com/research/jrs5m7/global_advanced


News Article | February 24, 2017
Site: globenewswire.com

As from February 27, 2017, the ICB Classification for SaltX Technology Holding AB will change. For further information about this exchange notice please contact Issuer Surveillance on +46 8 405 60 00 or iss@nasdaq.com.

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