Inkham C.,Chiang Mai University |
Sueyoshi K.,Niigata University |
Ohtake N.,Niigata University |
Ohyama T.,Niigata University |
Ruamrungsri S.,Institute for Science and Technology
European Journal of Horticultural Science | Year: 2011
We determined the critical nitrogen (N) level in fieldgrown Curcuma alismatifolia Gagnep. by applying N at a rate of 3.75, 7.5, 15, 30, and 60 g N plant-1 as urea. Plant growth and N critical levels were determined 105 d after planting (flowering stage). To establish critical N levels in plant tissue, the relationship between rhizome yield and the N concentrations in the first fully expanded leaf from the bottom of 1st order shoots as investigated. The rate of N application had a significant effect on growth, plant dry weight, leaf N concentrations, and leaf chlorophyll content. Leaf N concentrations were similar for all treatments up to 30 g N plant-1, but increased when N was applied at 60 g N plant-1. Rhizome yields increased with increasing leaf N up to about 1-1.5% N, but were relatively constant at a higher leaf N. The critical leaf N level response to 90% of the maximum yield was 1.51%. Most N concentrations in the various tissues increased with increasing N supply. The method for determining critical N levels provides an accurate description of the relationship between leaf N and rhizome yield and may be used by growers to predict the N requirement of Curcuma plants.
Inkham C.,Chiang Mai University |
Sueyoshi K.,Niigata University |
Ohtake N.,Niigata University |
Ohyama T.,Niigata University |
And 2 more authors.
Thai Journal of Agricultural Science | Year: 2011
The effects of different temperatures (30/24°C and 30/18°C day/night temperatures) and N sources; nitrate (NO 3 -), ammonium (NH 4 +), mixed-N source (50 NO 3 -:50 NH 4 +) in the nutrient solution on growth and N assimilation of Curcuma alismatifolia was studied in a soil-less culture medium. Plant grew taller when grown under the 30/24°C treatment and flower quality in terms of stalk length, size of inflorescence and number of bracts declined at low night temperatures. The highest nitrogen concentration (mg plant -1) in leaves was obtained when plants were supplied with NO 3 -as the nitrogen source. Most nitrogen assimilation occurred in leaves at 30/24°C, and in fibrous roots at 30/18°C. There were positive linear relationships between the NO 3 - content and NR activity in leaves, when plants were grown under both 30/24°C (R 2= 0.99) and 30/18°C (R 2= 0.96) treatments. The total amino acid concentration in NH 4 +-fed plants was significantly higher than that of NO 3 --fed plants. Glutamic acid (Glu) was recognized as a major form of accumulated N in old rhizomes, particularly in plants supplied with NO 3 -N at 30/18°C, while asparagine (Asn) and aspartic acid (Asp) were the major form of the accumulated N in fibrous roots when plants were cultivated with low night temperature (30/18°C).
News Article | October 5, 2016
Corals first appeared on earth nearly half a billion years ago during the Cambrian Period of the Paleozoic Era. The ancient Greek philosopher Aristotle categorized corals as zoophyta, or "plant-animals", due to their plant-like appearance. Closer examination of corals revealed that they are not plants at all, but instead belong to a group of animals called cnidarians, which includes sea anemones and jelly fish. Corals are generally immobile and colonial; the plant-like structure of corals is actually a colony of multiple coral polyps. Each coral polyp is an individual animal with its own tentacles, digestive filaments and mouth. All animals begin their lives as fertilized eggs. From there, the egg divides and eventually forms a blastula, or hollow single-layered sphere of cells. Then, the embryo becomes multilayer of cells through the dynamic cell movement process called gastrulation. Each of embryonic layers are responsible for the formation of specific organs and tissues within an animal. Two layered animals, or diploblasts, contain an endoderm and an ectoderm from which all body structures are derived. Triploblasts, such as humans and insects, contain three layers. In triploblasts, the endoderm is comprised of the innermost internal organs such intestines and lungs, while skin and epithelial tissues form the ectoderm. The mesoderm includes bone, muscle, cardiac tissue and blood. Each layer is formed by interactive functions of various genes during early development. Triploblasts diverged from diploblasts 600 million years ago. Very little research has been done to determine the evolutionary origin of the triploblastic mesoderm. Scientists had uncovered which genes are expressed in both diploblasts and the mesoderm of triploblasts, but a comparison of the functions of those genes in each type of animal had not yet been performed. Researchers from the Marine Genomics Unit at the Okinawa Institute for Science and Technology Graduate University (OIST) are the first group to successfully manipulate the coral genes for comparison. A researcher from the Unit, Dr. Yuuri Yasuoka, developed a new technology to investigate the role of the mesodermal gene brachyury in coral development. The results of this study provide insight into the evolutionary development of the mesoderm and are published in Current Biology. The research team at OIST focused on brachyury, a gene that plays a crucial role in mesoderm formation during vertebrate development. To distinguish the mesoderm from other layers, brachyury activates mesodermal genes while repressing ectodermal and endodermal genes. Brachyury is also important for the development of mesodermal tissues from pluripotent stem cells, such as embryonic stem cells. This gene exists in all animals but its function in diploblasts is poorly understood. Researchers from the Marine Genomics Unit, Dr. Yasuoka and Dr. Chuya Shinzato, performed a series of experiments using coral embryos and uncovered a piece of the evolutionary puzzle. Because corals are one of the most primitive and successful cnidarian group, they are useful for understanding animal evolution. The OIST team studied the function of the brachyury gene in Acropora digitifera, the most common coral in Okinawa. "This study was achieved on the basis of the availability of corals in Okinawa, careful planning of experiments, and the amazing DNA sequencing facilities at OIST. Because the annual spawning day of corals is unpredictable and the quality of eggs is different each year, it took five years to obtain the results through trial and error." Dr. Yuuri Yasuoka explains. Researchers at OIST have previously decoded the Acropora genome, making it suitable for these studies. However, because Acropora eggs float on sea water, it is difficult to manipulate them. To overcome this problem, the Dr. Yasuoka developed a method to retain the eggs between glass materials by surface tension (Figure 1). This allowed the scientists to then use microinjection to insert a material that inhibits the brachyury gene into Acropora eggs and examine the results of this inhibition in coral embryos. The brachyury gene is expressed in the ectoderm around the blastopore, which is the boundary between ectoderm and endoderm. Corals have a bag-like digestive organ with a hole, derived from the blastopore, that functions as both a mouth and an anus. When the brachyury gene was inhibited using the microinjection method, coral larvae lost their mouth structures (Figure 2). This result suggests that brachyury is necessary for mouth formation in corals. In vertebrates, the mouth opens on the opposite side from the blastopore, which becomes the anus. Brachyury inhibition in vertebrates produces a similar appearance to that seen in corals. Mice with only one copy of the brachyury gene, rather than the usual two, are born tail-less. Furthermore, comprehensive gene expression profiles of coral embryos were surveyed using a next generation sequencer at OIST. The result demonstrated that, in corals, brachyury activates ectodermal genes while it represses endodermal genes. The comparison of brachyury functions in corals and vertebrates suggests that ectoderm expressing brachyury in corals corresponds to mesoderm in vertebrates. In terms of gene functions, cells around the mouth in corals share common origins with mesodermal cells in vertebrates that produce bone and muscle. In other words, the origin of mesoderm for vertebrates is likely to be ectoderm of their diploblastic ancestor. This is a plausible answer to a long unsolved question. Dr. Noriyuki Satoh, leader of the Marine Genomics Unit, further explained: "One of our unit's research topics is to explain evolutionary processes of animal body plans at molecular levels. The finding that brachyury plays a role for blastopore formation during coral development has very high impact because corals lack a mesoderm and brachyury functions to form the mesoderm in vertebrates. We will continue to investigate brachyury functions in various animals". Further studies using corals are warranted to answer more scientific questions. Corals play a vital role in the marine ecosystem, making them an important animal to study. "Coral reefs are crucial to supporting the biodiversity of the earth and also possess economic values for fishery and tourism. While the whole genome sequence of a coral species was decoded previously, the molecular basis of the coral ecosystem remains unknown. To find real roles of coral genes, we have waited for coral spawning every night. This study marks a huge progress in the study of corals and hopefully contributes to preservation of corals in future", revealed Dr. Chuya Shinzato.
Thongchai W.,Chiang Mai University |
Liawruangrath B.,Chiang Mai University |
Liawruangrath B.,Institute for Science and Technology |
Liawruangrath S.,Center for Innovation in Chemistry |
Liawruangrath S.,Institute for Science and Technology
Talanta | Year: 2010
The development of sequential injection analysis with lab-at-valve (LAV) semi-automated system on-line liquid-liquid extraction is demonstrated for spectrophotometric determination of solasodine in various Solanum species fruits. The main proposed is semi-automated extractive determination of solasodine using methyl orange as colorimetric reagent. After optimization of the system, sample, reagent and organic solvent were sequentially aspirated into an extraction coil connected to the center of a selection valve, where extraction took place by flow reversal. The aqueous and organic phases were separated in a lab-at-valve unit attracted to one of the ports of the selection valve. The absorption of ion-pair solasodine-methyl orange complex in the organic phase was measured spectrophotometrically at 420 nm. The method performances, including reproducibility, linearity, sensitivity and accuracy, were also evaluated. The proposed method is simple, reproducible and accurate. It was successfully applied to the determination of solasodine in Solanum aculeatissimum Jacq., Solanum violaceum Ortega., Solanum melongena Linn. and Solanum indicum Linn. fruits in Solanaceae family. Results obtained were in good agreement with those obtained by batch wise spectrophotometric method. It is also suitable and useful for determination of solasodine in other medicinal plants. © 2009 Elsevier B.V. All rights reserved.
Norfun P.,Institute for Science and Technology |
Pojanakaroon T.,Institute for Science and Technology |
Liawraungrath S.,Institute for Science and Technology
Talanta | Year: 2010
A reverse flow injection analysis (rFIA) spectrophotometric method has been developed for the determination of aluminium(III). The method was based on the reaction of Al(III), quercetin and cetyltrimethylammonium bromide (CTAB), yielding a yellow colored complex in an acetate buffer medium (pH 5.5) with absorption maximum at 428 nm. The rFIA parameters that influence the FIA peak height have been optimized in order to obtain the best sensitivity and minimum reagent consumption. A linear relationship between the relative peak height and Al(III) concentrations were obtained over the concentration range of 0.02-0.50 mg L-1 with a correlation coefficient of 0.9998. The limit of detection (LOD, defined as 3σ) and limit of quantification (LOQ, defined as 10σ) were 0.007 and 0.024 mg L-1, respectively. The repeatability was 1.10% (n = 11) for 0.2 mg L-1 Al(III). The proposed method was applied to the determination of Al(III) in tap water samples with a sampling rate of 60 h-1. Results obtained were in good agreement with those obtained by the official ICP-MS method at the 95% confidence level. © 2010 Elsevier B.V. All rights reserved.
Cimadom A.,University of Vienna |
Ulloa A.,Charles Darwin Foundation |
Meidl P.,Institute for Science and Technology |
Zottl M.,University of Cambridge |
And 6 more authors.
PLoS ONE | Year: 2014
Invasive alien parasites and pathogens are a growing threat to biodiversity worldwide, which can contribute to the extinction of endemic species. On the Galápagos Islands, the invasive parasitic fly Philornis downsi poses a major threat to the endemic avifauna. Here, we investigated the influence of this parasite on the breeding success of two Darwin's finch species, the warbler finch (Certhidea olivacea) and the sympatric small tree finch (Camarhynchus parvulus), on Santa Cruz Island in 2010 and 2012. While the population of the small tree finch appeared to be stable, the warbler finch has experienced a dramatic decline in population size on Santa Cruz Island since 1997. We aimed to identify whether warbler finches are particularly vulnerable during different stages of the breeding cycle. Contrary to our prediction, breeding success was lower in the small tree finch than in the warbler finch. In both species P. downsi had a strong negative impact on breeding success and our data suggest that heavy rain events also lowered the fledging success. On the one hand parents might be less efficient in compensating their chicks' energy loss due to parasitism as they might be less efficient in foraging on days of heavy rain. On the other hand, intense rainfalls might lead to increased humidity and more rapid cooling of the nests. In the case of the warbler finch we found that the control of invasive plant species with herbicides had a significant additive negative impact on the breeding success. It is very likely that the availability of insects (i.e. food abundance) is lower in such controlled areas, as herbicide usage led to the removal of the entire understory. Predation seems to be a minor factor in brood loss. © 2014 Cimadom et al.
PubMed | University of Vienna, Charles Darwin Foundation, BirdLife Austria, Institute for Science and Technology and University of Cambridge
Type: Journal Article | Journal: PloS one | Year: 2014
Invasive alien parasites and pathogens are a growing threat to biodiversity worldwide, which can contribute to the extinction of endemic species. On the Galpagos Islands, the invasive parasitic fly Philornis downsi poses a major threat to the endemic avifauna. Here, we investigated the influence of this parasite on the breeding success of two Darwins finch species, the warbler finch (Certhidea olivacea) and the sympatric small tree finch (Camarhynchus parvulus), on Santa Cruz Island in 2010 and 2012. While the population of the small tree finch appeared to be stable, the warbler finch has experienced a dramatic decline in population size on Santa Cruz Island since 1997. We aimed to identify whether warbler finches are particularly vulnerable during different stages of the breeding cycle. Contrary to our prediction, breeding success was lower in the small tree finch than in the warbler finch. In both species P. downsi had a strong negative impact on breeding success and our data suggest that heavy rain events also lowered the fledging success. On the one hand parents might be less efficient in compensating their chicks energy loss due to parasitism as they might be less efficient in foraging on days of heavy rain. On the other hand, intense rainfalls might lead to increased humidity and more rapid cooling of the nests. In the case of the warbler finch we found that the control of invasive plant species with herbicides had a significant additive negative impact on the breeding success. It is very likely that the availability of insects (i.e. food abundance)is lower in such controlled areas, as herbicide usage led to the removal of the entire understory. Predation seems to be a minor factor in brood loss.
News Article | October 27, 2015
It is fascinating to observe a robot exploring its physical possibilities and surroundings, and subsequently developing different self-taught behaviors without any instructions. In their paper published on October, 26, 2015 in PNAS (Proceedings of the National Academy of Sciences), Professor Ralf Der from the Max Planck Institute for Mathematics in the Sciences, und Georg Martius, Postdoc and Fellow at the Institute for Science and Technology (IST Austria), demonstrate the emergence of sensorimotor intelligence in robots based on their proposed learning rule.
News Article | November 25, 2015
Robots could learn a lot from babies, for example, how the latter acquire new movements. Children explore the world through play. In the process, they not only discover their environment but also their own bodies. As Ralf Der of the Max Planck Institute for Mathematics in the Sciences and Georg Martius from the Institute for Science and Technology in Klosterneuburg, Austria have now shown in simulations with robots, that their brains, made of artificial neurons, do not need a higher-level control center for generating curiosity. Curiosity arises solely from feedback loops between sensors that provide stimuli about interactions of the robot’s body with the environment on the one hand and motion commands on the other. The robot’s control unit generates commands for new movements based purely on sensory signals. From initially small, and even passive movements, the robot develops a motor repertoire without specific higher-level instructions. Until now, robots capable of learning have been given specific goals and have then been rewarded when they achieve them. Or researchers try to program curiosity into the robots. “What fires together, wires together”, a rule formulated by Canadian psychologist Donald Hebb, is well known to neuroscientists. This law states that the more often two neurons are active simultaneously, the more likely they link together and form complex networks. Hebb’s law is able to explain the formation of memory but not the development of movements. To learn to crawl, grasp or walk, people and adaptive robots need playful curiosity that prompts them to learn new movements. Ralf Der from the Max Planck Institute for Mathematics in the Sciences and Georg Martius, who until recently researched at the same Max Planck Institute and is now continuing his work at the Institute for Science and Technology in Klosterneuburg, Austria, have now demonstrated, that no higher-level command center is required, which is in contrast to what many researchers believe. “We’ve found that robots, at least, can develop motor skills without the need for a specific program for curiosity, meaning the explicit drive for increasing information in their artificial neural network,” said Georg Martius. Together with Ralf Der, he has formulated a new sensorimotor learning rule, according to which links form in artificial neural networks and possibly also in the brains of babies which allow robots and young children to learn new movements depending on the situation. Neuronal networks form when the body and environment interact The learning rule is based on a model that mimics the dynamic interaction between three components: the body, the brain and the environment, i.e. in robots an artificial neural network. Initially there are no structures in the brain of the robot that control movements. The relevant neural networks form when the body interacts with the environment and the limbs are bent somewhat because they encounter an obstacle. This lies at the root of sensorimotor learning, the robot learns how to move. For the learning process to begin at all, an initial impetus from the outside is required in this model, Martius explains: “First, nothing happens at all. If the system is at rest, the neurons do not receive any signals.“ The researchers therefore triggered a passive sensory stimulus in their robot, for example by leading it around on a virtual thread or simply by letting it sink to the floor so that its trunk, arms or legs are bent. Much as in humans, who after a stroke, for example, initially regain control of their arms or legs by passive movements, passive sensory stimuli in the robotic brain give rise to an initial learning signal. Though this is very weak, the sensorimotor control center uses it to generate a small, but slightly modified motion that generates a new sensory stimulus, which in turn is converted into a movement. Thus, stimuli and motor commands mutually propel each other to generate a coordinated pattern of movements. Feedback between sensor signals and movement produces curiosity The robot then performs the movement pattern until it is disturbed. For example, the robot crawls up to an obstacle. At the obstacle, the robot develops new movement patterns. One of them will eventually allow it to overcome or bypass the obstacle. “In this way our robots exhibit a certain curiosity since they learn more and more movements,” explains Georg Martius. “However, their curiosity arises solely from feedback between sensory stimuli and movement command as their body interacts with the environment.” In computer simulations, the researchers applied their learning rule to simple neural networks of virtual hexapods and humanoid robots that learned to locomote this way. They even were able to cooperate with other robots. For example, after a while two humanoid robots joined forces to turn a wheel in a coordinated manner. Martius emphasizes that their system quickly adapts to new situations that are determined by the environment. This is important because “it would be futile to try all possible movements and combinations. They are infinite in number, and that approach would take far too long.” The model therefore does not use random decisions. On the contrary, a specific sensory stimulus is only translated into a single motor command. The same stimulus therefore always gives rise to the same movement. Hence, the robot’s movements are derived directly from its past actions. “However, small changes in the sensory signals can have a large impact on the development of a movement pattern,” said Georg Martius. Initial tests with a real robot were promising In the long term, the researchers want to combine multiple movement patterns from a large repertoire to allow complex actions. Ralf Der and Georg Martius will first test their learning rules on real robots. Initial experiments with an artificial arm have been promising, as the artificial limb developed some skills of a real arm. The experiments confirm that robots and potentially also the human brain need no high-level curiosity center and no specific goals to develop new movements that can ultimately be used in a practical way. Instead, the required neural networks appear to form solely because neurons that respond to external stimuli in the same way form tighter associations. Ralf Der and Georg Martius have therefore formulated a new rule based on Hebb’s law: “Chaining together what changes together”.
News Article | November 3, 2016
Algorithms usually need thousands of examples to learn something. Researchers at Google DeepMind found a way around that. Most of us can recognize an object after seeing it once or twice. But the algorithms that power computer vision and voice recognition need thousands of examples to become familiar with each new image or word. Researchers at Google DeepMind now have a way around this. They made a few clever tweaks to a deep-learning algorithm that allows it to recognize objects in images and other things from a single example—something known as "one-shot learning." The team demonstrated the trick on a large database of tagged images, as well as on handwriting and language. The best algorithms can recognize things reliably, but their need for data makes building them time-consuming and expensive. An algorithm trained to spot cars on the road, for instance, needs to ingest many thousands of examples to work reliably in a driverless car. Gathering so much data is often impractical—a robot that needs to navigate an unfamiliar home, for instance, can’t spend countless hours wandering around learning. Oriol Vinyals, a research scientist at Google DeepMind, a U.K.-based subsidiary of Alphabet that’s focused on artificial intelligence, added a memory component to a deep-learning system—a type of large neural network that’s trained to recognize things by adjusting the sensitivity of many layers of interconnected components roughly analogous to the neurons in a brain. Such systems need to see lots of images to fine-tune the connections between these virtual neurons. The team demonstrated the capabilities of the system on a database of labeled photographs called ImageNet. The software still needs to analyze several hundred categories of images, but after that it can learn to recognize new objects—say, a dog—from just one picture. It effectively learns to recognize the characteristics in images that make them unique. The algorithm was able to recognize images of dogs with an accuracy close to that of a conventional data-hungry system after seeing just one example. Vinyals says the work could be especially useful if it could quickly recognize the meaning of a new word. This could be important for Google, Vinyals says, since it could allow a system to quickly learn the meaning of a new search term. Others have developed one-shot learning systems, but these are usually not compatible with deep-learning systems. An academic project last year used probabilistic programming techniques to enable this kind of very efficient learning (see "This Algorithm Learns Tasks As Fast As We Do"). But deep-learning systems are becoming more capable, especially with the addition of memory mechanisms. Another group at Google DeepMind recently developed a network with a flexible kind of memory, making it capable of performing simple reasoning tasks—for example, learning how to navigate a subway system after analyzing several much simpler network diagrams (see "What Happens When You Give a Computer a Working Memory?"). "I think this is a very interesting approach, providing a novel way of doing one-shot learning on such large-scale data sets," says Sang Wan Lee, who leads the Laboratory for Brain and Machine Intelligence at the Korean Advanced Institute for Science and Technology in Daejeon, South Korea. "This is a technical contribution to the AI community, which is something that computer vision researchers might fully appreciate." Others are more skeptical about its usefulness, given how different it still is from human learning. For one thing, says Sam Gershman, an assistant professor in Harvard's Department for Brain Science, humans generally learn by understanding the components that make up an image, which may require some real-world, or commonsense, knowledge. For example, "a Segway might look very different from a bicycle or motorcycle, but it can be composed from the same parts." According to both Gershman and Wan Lee, it will be some time yet before machines match human learning. "We still remain far from revealing humans’ secret of performing one-shot learning," Wan Lee says, "but this proposal clearly poses new challenges that merit further study."