Pioneer Venus Sounder Probe Neutral Gas Mass Spectrometer

A neutral gas mass spectrometer was flown to Venus as part of the Pioneer Venus Multiprobe to measure the composition of its lower atmosphere. Encounter occurred on December 9, 1978. The instrument, mounted in the Sounder Probe, was activated after the probe entered the top of the atmosphere, and it obtained data during the descent from 62 km to the surface. Atmospheric gases were sampled through a pair of microleaks, the effluent from which was pumped by a combination of ion and getter pumping. A pneumatically operated valve, controlled by the ambient atmospheric pressure, maintained the ion source pressure at a nearly constant value during descent while the atmospheric pressure varied by 3 orders of magnitude. This action preserved the more than 6-decade dynamic range of the instrument throughout the descent. A single focusing magnetic sector field mass spectrometer with mass resolution sufficient to reasonably separate argon from C3H4 at 40 amu provided the mass analysis and relative abundance measurements. A microprocessor controlled the operation of the mass spectrometer through a highly efficient peak-top stepping routine and data compression algorithm that effected a scan of the mass spectrum from 1 to 208 amu in 64 s while requiring an information rate of only 40 b/s to return the data to earth. A subscale height altitude resolution was thus obtained. Weight, size, and power requirements were minimized to be consistent with interplanetary flight constraints.     

Self-organizing neural systems based on predictive learning

The ability to predict future events based on the past is an important attribute of organisms that engage in adaptive behaviour. One prominent computational method for learning to predict is called temporal-difference (TD) learning. It is so named because it uses the difference between successive predictions to learn to predict correctly. TD learning is well suited to modelling the biological phenomenon of conditioning, wherein an organism learns to predict a reward even though the reward may occur later in time. We review a model for conditioning in bees based on TD learning. The model illustrates how the TD-learning algorithm allows an organism to learn an appropriate sequence of actions leading up to a reward, based solely on reinforcement signals. The second part of the paper describes how TD learning can be used at the cellular level to model the recently discovered phenomenon of spike-timing-dependent plasticity. Using a biophysical model of a neocortical neuron, we demonstrate that the shape of the spike-timing-dependent learning windows found in biology can be interpreted as a form of TD learning occurring at the cellular level. We conclude by showing that such spike-based TD-learning mechanisms can produce direction selectivity in visual-motion-sensitive cells and can endow recurrent neocortical circuits with the powerful ability to predict their inputs at the millisecond time-scale.     

Resampling methods for ranked set samples

When measuring units are expensive or time consuming, while ranking them can be done easily, it is known that ranked set sampling (RSS) is preferred to simple random sampling (SRS). Available results for RSS are developed under specific parametric assumptions or are asymptotic in nature, with few results available for finite size samples when the underlying distribution of the observed data is unknown. We investigate the use of resampling techniques to draw inferences on population characteristics. To obtain standard error and confidence interval estimates we discuss and compare three methods of resampling a given ranked set sample. Chen et al. (2004. Ranked Set Sampling: Theory and Applications. Springer, New York) suggest a natural method to obtain bootstrap samples from each row of a RSS. We prove that this method is consistent for a location estimator. We propose two other methods that are designed to obtain more stratified resamples from the given sample. Algorithms are provided for these methods. We recommend a method that obtains a bootstrap RSS from the observations. We prove several properties of this method, including consistency for a location parameter. We define two types of L-estimators for RSS and obtain expressions for their exact moments. We discuss an application to obtain confidence intervals for the Winsorized mean of a RSS.     

Multicomponent and multidimensional carbon nanotube micropatterns by dry contact transfer

One of the critical aspects of nanotechnology is to assemble different nanoscale components into a single system. In such a multicomponent system, the overall functionality depends strongly on the precise location and structural characteristics of each of the constituent components. In this context, we have prepared multicomponent micropatterns of silica particles interposed within the discrete areas of aligned multiwall carbon nanotubes. The patterns were fabricated by dry contact transferring aligned carbon nanotubes onto a tape pre-patterned with a thin layer of gold structure, followed by region-specific adsorption of thiol-modified silica particles onto the gold surface from solution. The dry contact transfer technique has further enabled us to develop micropatterns of aligned single-wall carbon nanotubes with interdispersed non-aligned multiwall carbon nanotubes and microsized carbon fibers sheathed with micropatterned aligned carbon nanotubes.     

Environmental and nutritional factors affecting geosmin synthesis by Anabaena sp

A cyanobacterium isolated from a source-water reservoir during a spring odor and taste episode and identified as Anabaena sp. consistently produced geosmin during laboratory culture on modified BG-11 liquid medium. Maximal geosmin/biomass occurred at 20 degrees C and a light intensity of 17 microE/m2/s; geosmin/chla values directly correlated with increasing light intensity (r2 = 0.95, P < 0.01). It was concluded that at 20 degrees C, increasing light intensity favors less chla synthesis and higher geosmin synthesis; at 17 microE/m2/s, increasing temperature stimulates chla production (to 25 degrees C) while repressing geosmin synthesis (above 20 degrees C). Nutritional factors promoting biomass, chla, and geosmin synthesis by Anabaena sp. were also investigated. For cultures grown at 17 microE/m2/s and 20 degrees C for 20 days, both ammonium-N and nitrate-N generally enhanced the growth of Anabaena sp. Nitrate-N promoted more chla production (r2 = 0.99) than ammonium-N. Geosmin synthesis was directly correlated with ammonium-N concentrations (r2 = 0.89), with low nitrate-N (123.5 micrograms/l) favoring maximal geosmin production (2.8 micrograms/l). Increasing nitrate-N concentrations promoted a three-fold increase in chla content with geosmin synthesis decreased by two-fold. Geosmin/mg biomass was directly related to ammonium-N concentration; high nitrate-N levels suppressed geosmin production. No geosmin was detected at or below 118 micrograms phosphate-phosphorus/l. Geosmin, dry weight biomass, and chla production were correlated with increasing phosphorus (P) concentration (r2 = 0.76, 0.96 and 0.98, respectively). No geosmin was detected when copper was present in growth media at or above 6.92 micrograms Cu2+/l (CuSO4.5H2O). Dry weight biomass and chla production were negatively correlated with Cu2+ ion concentrations.     

Discovery of Novel Trace Amine-Associated Receptor 5 (TAAR5) Antagonists Using a Deep Convolutional Neural Network

Trace amine-associated receptor 5 (TAAR5) is a G protein-coupled receptor that belongs to the TAARs family (TAAR1-TAAR9). TAAR5 is expressed in the olfactory epithelium and is responsible for sensing 3-methylamine (TMA). However, recent studies showed that TAAR5 is also expressed in the limbic brain regions and is involved in the regulation of emotional behaviour and adult neurogenesis, suggesting that TAAR5 antagonism may represent a novel therapeutic strategy for anxiety and depression. We used the AtomNet^® model, the first deep learning neural network for structure-based drug discovery, to identify putative TAAR5 ligands and tested them in an in vitro BRET assay. We found two mTAAR5 antagonists with low to submicromolar activity that are able to inhibit the cAMP production induced by TMA. Moreover, these two compounds also inhibited the mTAAR5 downstream signalling, such as the phosphorylation of CREB and ERK. These two hits exhibit drug-like properties and could be used to further develop more potent TAAR5 ligands with putative anxiolytic and antidepressant activity.     

Russian–EU collaboration via the mega-transect approach for large-scale projects: cases of RF Federal target Programme and SIWA JPI Climate EU Programme

The mega-transect approach is a unique infrastructure which was developed in Tomsk State University for environmental monitoring and landscape-ecological research. The approach can be followed in all seasons, for field sampling, ground-based research on the field stations in combination with remote sensing and ecosystem manipulations. The mega-transect was established as a framework for the concept of Western Siberia as a natural mega-facility, a kind of natural equivalent to CERN’s Large Hadron Collider, to attract leading international research groups. The paper describes cases of Russian Federation Federal target Programme and Siberian Inland Waters Joint Programming Initiative Climate EU Programme as examples of the large-scale international projects which are now resulting.     

Energy-Harvesting Systems for Green Computing

Energy harvesting technologies provide a promising alternative to battery-powered systems and create an opportunity to achieve sustainable computing for the exploitation of ambient energy sources. However, energy harvesting devices and power generators encompass a number of non-classical system behaviors or characteristics, such as delivering nondeterministic power density, and these would create hindrance for effectively utilizing the harvested energy. Previously, we have investigated new design methods and tools that are used to enable power adaptive computing and, particularly, catering non-deterministic voltage, which can efficiently utilize ambient energy sources. Also, we developed a co-optimization approach to maximize the computational efficiency from the harvested ambient energy. This paper will provide a review of these methods. Emerging technologies, such as 3D-IC, which would also enable new paradigm of green and high-performance computing, will be also discussed.