A network that really works - the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia

Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results indicate that the GA-BP-NN technique offers a slightly better yield correlation. Our main conclusion is that this method would offer growers more confidence in making a better nitrogen decision during the season than currently available models due to more precise yield predictions. It also can also contribute to possibly better grain marketing decisions later in the season

Integrating soil and plant tissue tests and using an artificial intelligence method for data modelling is likely to improve decisions for in-season nitrogen management

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis by about 2-3 months, depending when plant testing occurred. A positive side-effect of plant testing is to check whether any other nutrients, apart from nitrogen, are limiting yield or an N response. Secondly, this paper explores an AI method as a comparison to the traditional modelling technique to further improve the accuracy and to turn the model into a self-calibrating model. Unlike a statistical autoregression technique, the tested AI method has dynamic functions that can be used not only on time series data but also on data such as used here

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Surface Chemistry of the Molecular Solar Thermal Energy Storage System 2,3-Dicyano-Norbornadiene/Quadricyclane on Ni(111)

Molecular solar thermal (MOST) systems are a promising approach for the introduction of sustainable energy storage solutions. We investigated the feasibility of the dicyano-substituted norbornadiene/quadricyclane molecule pair on Ni(111) for catalytic model studies. This derivatization is known to lead to a desired bathochromic shift of the absorption maximum of the parent compound. In our experiments further favorable properties were found: At low temperatures, both molecules adsorb intact without any dissociation. In situ temperature-programmed HR-XPS experiments reveal the conversion of (CN)2-quadricyclane to (CN)2-norbornadiene under energy release between 175 and 260 K. The absence of other surface species due to side reactions indicates full isomerization. Further heating leads to the decomposition of the molecular framework into smaller carbonaceous fragments above 290 K and finally to amorphous structures, carbide and nitride above 400 K. DFT calculations gave insights into the adsorption geometries. (CN)2-norbornadiene is expected to interact stronger with the surface, with flat configurations being favorable. (CN)2-quadricyclane exhibits smaller adsorption energies with negligible differences for flat and side-on geometries. Simulated XP spectra are in good agreement with experimental findings further supporting the specific spectroscopic fingerprints for both valence isomers

Surface Studies on the Energy Release of the MOST System 2-Carbethoxy-3-Phenyl-Norbornadiene/Quadricyclane (PENBD/PEQC) on Pt(111) and Ni(111)

Novel energy-storage solutions are necessary for the transition from fossil to renewable energy sources. Auspicious candidates are so-called molecular solar thermal (MOST) systems. In our study, we investigate the surface chemistry of a derivatized norbornadiene/quadricyclane molecule pair. By using suitable push–pull substituents, a bathochromic shift of the absorption onset is achieved, allowing a greater overlap with the solar spectrum. Specifically, the adsorption and thermally induced reactions of 2-carbethoxy-3-phenyl-norbornadiene/quadricyclane are assessed on Pt(111) and Ni(111) as model catalyst surfaces by synchrotron radiation-based X-ray photoelectron spectroscopy (XPS). Comparison of the respective XP spectra enables the distinction of the energy-rich molecule from its energy-lean counterpart and allows qualitative information on the adsorption motifs to be derived. Monitoring the quantitative cycloreversion between 140 and 230 K spectroscopically demonstrates the release of the stored energy to be successfully triggered on Pt(111). Heating to above 300 K leads to fragmentation of the molecular framework. On Ni(111), no conversion of the energy-rich compound takes place. The individual decomposition pathways of the two isomers begin at 160 and 180 K, respectively. Pronounced desorption of almost the entire surface coverage only occurs for the energy-lean molecule on Ni(111) above 280 K; this suggests weakly bound species. The correlation between adsorption motif and desorption behavior is important for applications of MOST systems in heterogeneously catalyzed processes

Au-Catalyzed Energy Release in a Molecular Solar Thermal (MOST) System: A Combined Liquid-Phase and Surface Science Study

Molecular solar thermal systems (MOSTs) are molecular systems based on couples of photoisomers (photoswitches), which combine solar energy conversion, storage, and release. In this work, we address the catalytically triggered energy release in the promising MOST couple phenylethylesternorbornadiene/quadricyclane (PENBD/PEQC) on a Au(111) surface in a combined liquid-phase and surface science study. We investigated the system by photoelectrochemical infrared reflection absorption spectroscopy (PEC-IRRAS) in the liquid phase, conventional IRRAS and synchrotron radiation photoelectron spectroscopy (SRPES) in ultra-high vacuum (UHV). Au(111) is highly active towards catalytically triggered energy release. In the liquid phase, we did not observe any decomposition of the photoswitch, no deactivation of the catalyst within 100 conversion cycles and we could tune the energy release rate of the heterogeneously catalyzed process by applying an external potential. In UHV, submonolayers of PEQC on Au(111) are back-converted to PENBD instantaneously, even at 110 K. Multilayers of PEQC are stable up to ~220 K. Above this temperature, the intrinsic mobility of the film is high enough that PEQC molecules come into direct contact with the Au(111) surface, which catalyzes the back-conversion. We suggest that this process occurs via a singlet–triplet mechanism induced by electronic coupling between the PEQC molecules and the Au(111) surface

Myocardial-specific R-spondin3 drives proliferation of the coronary stems primarily through the Leucine Rich Repeat G Protein coupled receptor LGR4

Coronary artery anomalies are common congenital disorders with serious consequences in adult life. Coronary circulation begins when the coronary stems form connections between the aorta and the developing vascular plexus. We recently identified the WNT signaling modulator R-spondin 3 (Rspo3), as a crucial regulator of coronary stem proliferation. Using expression analysis and tissue-specific deletion we now demonstrate that Rspo3 is primarily produced by cardiomyocytes. Moreover, we have employed CRISPR/Cas9 technology to generate novel Lgr4-null alleles that showed a significant decrease in coronary stem proliferation and thus phenocopied the coronary artery defects seen in Rspo3 mutants. Interestingly, Lgr4 mutants displayed slightly hypomorphic right ventricles, an observation also made after myocardial specific deletion of Rspo3. These results shed new light on the role of Rspo3 in heart development and demonstrate that LGR4 is the principal Rspondin 3 receptor in the heart

Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks

Brains need to deal with an uncertain world. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult. We propose that cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as sampling-based probabilistic inference, memory replay, multisensory cue combination and place cell flickering.Comment: 30 pages, 11 figure

Structural water as an essential comonomer in supramolecular polymerization

Water is an essential comonomer in a supramolecular polymer that is used as a recyclable, water-activated glue.</jats:p

Reconstructing Circumpolar Deep Water: A new Mg/Ca-Temperature calibration for the benthic foraminifer Trifarina angulosa around Antarctica

The West Antarctic Ice Sheet (WAIS) represents a large potential source of sea level rise. Observations of ice sheet instabilities in the region have increased in recent decades, with a 77% recorded increase in the net loss of glaciers the Amundsen Sea Embayment (ASE) sector of the WAIS since 1973. This has been attributed to increasing basal melting of floating ice shelves caused by warmer Circumpolar Deep Water (CDW) upwelling onto the shelf. Understanding the role of CDW in glacial retreat in the ASE over longer timescales is key to reducing the uncertainty of future sea level predictions. The aim of this research is to reconstruct CDW incursions onto the ASE continental shelf and correlate them to the glacial history of the area since the Last Glacial Maximum. To achieve this, it is crucial to develop a proxy for detecting the presence or absence of CDW. Whilst foraminiferal preservation is rare in this locality due to the corrosive nature of water masses around the Antarctic Peninsula, several cores from the ASE contain specimens including the benthic species Trifarina angulosa, which is a shallow infaunal species therefore ideal for Mg/Ca temperature reconstructions. Here we present a core-top calibration for T. angulosa for temperatures between -1.75°C and +1.5°C from sites situated in the Southern Ocean. We apply this Mg/Ca temperature calibration to down-core archives at several sites, which are well-dated using radiocarbon. The results are presented here along with benthic and planktonic foraminiferal stable isotope data and complementary trace metal data. Keywords: Circumpolar deep water, foraminifera, Mg/C