Accommodating Unwelcome Guests in Inorganic Layered Hosts: Inclusion of Chloranil in a Layered Double Hydroxide

The host-guest chemistry of most inorganic layered solids is limited to ion-exchange reactions. The guest species are either cations or anions to compensate for the charge deficit, either positive or negative, of the inorganic layers. Here, we outline a strategy to include neutral molecules like ortho- and para-chloranil, that are known to be good acceptors in donor-acceptor or charge-transfer complexes, within the galleries of a layered solid. We have succeeded in including neutral ortho- and para-chloranil molecules within the galleries of an Mg-Al layered double hydroxide (LDH) by using charge-transfer interactions with preintercalated p-aminobenzoate ions as the driving force. The p-aminobenzoate ions are introduced in the Mg-Al LDH via ion exchange. The intercalated LDH can adsorb ortho- and para-chloranil from chloroform solutions by forming charge-transfer complexes with the p-aminobenzoate anions present in the galleries. We use X-ray diffraction, spectroscopy, and molecular dynamics simulations to establish the nature of interactions and arrangement of the charge-transfer complex within the galleries of the layered double hydroxide.

A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning

Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles for experimentally observed plasticity in HCN channels accompanying synaptic plasticity in hippocampal neurons, and uncover potential links between HCN-channel plasticity and calcium influx, dynamic gain control and stable synaptic learning.

Strength of Gamma Rhythm Depends on Normalization

Neuronal assemblies often exhibit stimulus-induced rhythmic activity in the gamma range (30-80 Hz), whose magnitude depends on the attentional load. This has led to the suggestion that gamma rhythms form dynamic communication channels across cortical areas processing the features of behaviorally relevant stimuli. Recently, attention has been linked to a normalization mechanism, in which the response of a neuron is suppressed (normalized) by the overall activity of a large pool of neighboring neurons. In this model, attention increases the excitatory drive received by the neuron, which in turn also increases the strength of normalization, thereby changing the balance of excitation and inhibition. Recent studies have shown that gamma power also depends on such excitatory-inhibitory interactions. Could modulation in gamma power during an attention task be a reflection of the changes in the underlying excitation-inhibition interactions? By manipulating the normalization strength independent of attentional load in macaque monkeys, we show that gamma power increases with increasing normalization, even when the attentional load is fixed. Further, manipulations of attention that increase normalization increase gamma power, even when they decrease the firing rate. Thus, gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control.

A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

Euglena sp as a suitable source of lipids for potential use as biofuel and sustainable wastewater treatment

Prolific algal growth in sewage ponds with high organic loads in the tropical regions can provide cost-effective and efficient wastewater treatment and biofuel production. This work examines the ability of Euglena sp. growing in wastewater ponds for biofuel production and treatment of wastewater. The algae were isolated from the sewage treatment plants and were tested for their nutrient removal capability. Compared to other algae, Euglena sp. showed faster growth rates with high biomass density at elevated concentrations of ammonium nitrogen (NH4-N) and organic carbon (C). Profuse growth of these species was observed in untreated wastewaters with a mean specific growth rate (mu) of 0.28 day(-1) and biomass productivities of 132 mg L-1 day(-1). The algae cultured within a short period of 8 days resulted in the 98 % removal of NH4-N, 93 % of total nitrogen 85 % of ortho-phosphate, 66 % of total phosphate and 92 % total organic carbon. Euglenoids achieved a maximum lipid content of 24.6 % (w/w) with a biomass density of 1.24 g L-1 (dry wt.). Fourier transform infrared spectra showed clear transitions in biochemical compositions with increased lipid/protein ratio at the end of the culture. Gas chromatography and mass spectrometry indicated the presence of high contents of palmitic, linolenic and linoleic acids (46, 23 and 22 %, respectively), adding to the biodiesel quality. Good lipid content (comprised quality fatty acids), efficient nutrient uptake and profuse biomass productivity make the Euglena sp. as a viable source for biofuel production in wastewaters.

Highly regioselective C2-alkenylation of indoles using the N-benzoyl directing group: an efficient Ru-catalyzed coupling reaction

A highly regioselective alkenylation of indole at the C2-position has been achieved using the Ru(II) catalyst by employing a directing group strategy. This strategy offers rare selectivity for the alkenylation N-benzoylindole at the C-2 position in the presence of the more active C3- and C7-position of indole and the ortho-positions of the benzoyl protecting group. A simple deprotection of the benzoyl group has also been exemplified, and the resulting product serves as a useful synthon for natural product syntheses.

The effect of cell size and channel density on neuronal information encoding and energy efficiency

Identifying the determinants of neuronal energy consumption and their relationship to information coding is critical to understanding neuronal function and evolution. Three of the main determinants are cell size, ion channel density, and stimulus statistics. Here we investigate their impact on neuronal energy consumption and information coding by comparing single-compartment spiking neuron models of different sizes with different densities of stochastic voltage-gated Na+ and K+ channels and different statistics of synaptic inputs. The largest compartments have the highest information rates but the lowest energy efficiency for a given voltage-gated ion channel density, and the highest signaling efficiency (bits spike(-1)) for a given firing rate. For a given cell size, our models revealed that the ion channel density that maximizes energy efficiency is lower than that maximizing information rate. Low rates of small synaptic inputs improve energy efficiency but the highest information rates occur with higher rates and larger inputs. These relationships produce a Law of Diminishing Returns that penalizes costly excess information coding capacity, promoting the reduction of cell size, channel density, and input stimuli to the minimum possible, suggesting that the trade-off between energy and information has influenced all aspects of neuronal anatomy and physiology.

An 8-to-1 bit 1-MS/s SAR ADC With VGA and Integrated Data Compression for Neural Recording

Low power consumption per channel and data rate minimization are two key challenges which need to be addressed in future generations of neural recording systems (NRS). Power consumption can be reduced by avoiding unnecessary processing whereas data rate is greatly decreased by sending spike time-stamps along with spike features as opposed to raw digitized data. Dynamic range in NRS can vary with time due to change in electrode-neuron distance or background noise, which demands adaptability. An analog-to-digital converter (ADC) is one of the most important blocks in a NRS. This paper presents an 8-bit SAR ADC in 0.13-mu m CMOS technology along with input and reference buffer. A novel energy efficient digital-to-analog converter switching scheme is proposed, which consumes 37% less energy than the present state-of-the-art. The use of a ping-pong input sampling scheme is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the data rate, the A/D process is only enabled through the in-built background noise rejection logic to ensure that the noise is not processed. The ADC resolution can be adjusted from 8 to 1 bit in 1-bit step based on the input dynamic range. The ADC consumes 8.8 mu W from 1 V supply at 1 MS/s speed. It achieves effective number of bits of 7.7 bits and FoM of 42.3 fJ/conversion-step.

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define `synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in `synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

Thermodynamics of NdRhO3 and phase relations in the system Nd-Rh-O

Phase equilibrium experiments indicate that NdRhO3 is the only ternary oxide in the system Nd-Rh-O at 1273 K; it has orthorhombically-distorted perovskite structure. By employing a solid-state electrochemical cell incorporating calcia-stabilized zirconia as the electrolyte, thermodynamic properties of NdRhO3 are determined. The standard Gibbs energy of formation of NdRhO3 from its component binary oxides in the temperature ranges from 900 to 1300 K can be expressed as: 1/2Rh(2)O(3) (ortho)+1/2Nd(2)O(3)(hex)=NdRhO3(ortho), Delta(f(o,x))G(0)/J mol(-1)( +/- 197) = - 66256+5.64 (T/K). The decomposition temperature of NdRhO3 computed from extrapolated thermodynamic data is 1803 (+/- 4) K in pure oxygen and 1692 (+/- 4) K in air at standard pressure. Oxygen partial pressure-composition diagram and three-dimensional chemical potential diagram at 1273 K are developed from thermodynamic data obtained in this study and auxiliary information from the literature. Equilibrium temperature-composition phase diagrams at constant oxygen partial pressures are also constructed. (C) 2013 Elsevier Ltd. All rights reserved.

Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents

Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of ``how much'' information is conveyed by primary afferents, using the direct method (DM), a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on ``what'' is coded by primary afferents. Amongst the kinematic variables tested-position, velocity, and acceleration-primary afferent spikes encoded velocity best. The other two variables contributed to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e., well separated sets of combinations of the three instantaneous kinematic variables). Secondly, neurons are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the DM. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80-90%. The final 10-20% were found to be due to non-linear coding by spike bursts.

Reach task-associated excitatory overdrive of motor cortical neurons following infusion with ALS-CSF

Converging evidence from transgenic animal models of amyotrophic lateral sclerosis (ALS) and human studies suggest alterations in excitability of the motor neurons in ALS. Specifically, in studies on human subjects with ALS the motor cortex was reported to be hyperexcitable. The present study was designed to test the hypothesis that infusion of cerebrospinal fluid from patients with sporadic ALS (ALS-CSF) into the rat brain ventricle can induce hyperexcitability and structural changes in the motor cortex leading to motor dysfunction. A robust model of sporadic ALS was developed experimentally by infusing ALS-CSF into the rat ventricle. The effects of ALS-CSF at the single neuron level were examined by recording extracellular single unit activity from the motor cortex while rats were performing a reach to grasp task. We observed an increase in the firing rate of the neurons of the motor cortex in rats infused with ALS-CSF compared to control groups. This was associated with impairment in a specific component of reach with alterations in the morphological characteristics of the motor cortex. It is likely that the increased cortical excitability observed in the present study could be the result of changes in the intrinsic properties of motor cortical neurons, a dysfunctional inhibitory mechanism and/or an underlying structural change culminating in a behavioral deficit.

Remarkable role of positional isomers in the design of sensors for the ratiometric detection of copper and mercury ions in water

Cation sensing properties of the three positional isomers of rhodamine based sensors (1-3) are studied in water. The sensors differ only in the position of pyridine's nitrogen. The chemosensor 1, with pyridine nitrogen at ortho-position, showed a selective colorimetric detection of Cu(II) ions in water, at physiological pH 7.4 and also in medium containing BSA (bovine serum albumin) and blood serum. Notably the compound 2 and 3, with pyridine end located at meta-and para-positions did not show any color change with Cu(II) ions, although both the compounds showed turn-on change both in color and fluorescence with Hg(II) ions specifically. All the probes showed ratiometric changes with the specific metal ions. The changing position of nitrogen also changed the complexation pattern of the sensors with the metal ions. Probe 1 showed 2 : 1 complexation with Cu(II), whereas 2 and 3 showed 1 : 1 complexation with Hg(II) ions. The mechanism investigation showed that the change in color upon addition of metal ions is due to the ring-opening of the spirolactam ring of the probes. Cu(II) interacted with ligand 1 through a three-point interaction mode comprising carbonyl oxygen, amido nitrogen and pyridine nitrogen end. But in case of 2 and 3, Hg2+ only interacted through pyridine nitrogen ends. Quantitative estimation of Cu2+ and Hg2+ in complex biological media such as bovine albumin protein (BSA) and human blood serum were performed using these sensors. Rapid on-site detection as well as discrimination of these toxic ions was demonstrated using easily prepared portable test-strips.

Active Dendrites Regulate Spectral Selectivity in Location-Dependent Spike Initiation Dynamics of Hippocampal Model Neurons

How does the presence of plastic active dendrites in a pyramidal neuron alter its spike initiation dynamics? To answer this question, we measured the spike-triggered average (STA) from experimentally constrained, conductance-based hippocampal neuronal models of various morphological complexities. We transformed the STA computed from these models to the spectral and the spectrotemporal domains and found that the spike initiation dynamics exhibited temporally localized selectivity to a characteristic frequency. In the presence of the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, the STA characteristic frequency strongly correlated with the subthreshold resonance frequency in the theta frequency range. Increases in HCN channel density or in input variance increased the STA characteristic frequency and its selectivity strength. In the absence of HCN channels, the STA exhibited weak delta frequency selectivity and the characteristic frequency was related to the repolarization dynamics of the action potentials and the recovery kinetics of sodium channels from inactivation. Comparison of STA obtained with inputs at various dendritic locations revealed that nonspiking and spiking dendrites increased and reduced the spectrotemporal integration window of the STA with increasing distance from the soma as direct consequences of passive filtering and dendritic spike initiation, respectively. Finally, the presence of HCN channels set the STA characteristic frequency in the theta range across the somatodendritic arbor and specific STA measurements were strongly related to equivalent transfer-impedance-related measurements. Our results identify explicit roles for plastic active dendrites in neural coding and strongly recommend a dynamically reconfigurable multi-STA model to characterize location-dependent input feature selectivity in pyramidal neurons.

Phase equilibria in the system Sm-Rh-O and thermodynamic and thermal studies on SmRhO3

Using isothermal equilibration, phase relations are established in the system Sm-Rh-O at 1273 K. SmRhO3 with GdFeO3-type perovskite structure is found to be the only ternary phase. Solid-state electrochemical cells, containing calcia-stabilized zirconia as an electrolyte, are used to measure the thermodynamic properties of SmRhO3 formed from their binary component oxides Rh2O3 (ortho) and Sm2O3 (C-type and B-type) in two different temperature ranges. Results suggest that C-type Sm2O3 with cubic structure transforms to B-type Sm2O3 with monoclinic structure at 1110 K. The standard Gibbs energy of transformation is . Standard Gibbs energy of formation of SmRhO3 from binary component oxides Rh2O3 and Sm2O3 with B-type rare earth oxide structure can be expressed as . The decomposition temperature of SmRhO3 estimated from the extrapolation of electrochemical data is 1665 (+/- 2) K in air and 1773 (+/- 3) K in pure oxygen. Temperature-composition diagrams at constant oxygen pressures are constructed for the system Sm-Rh-O. Employing the thermodynamic data for SmRhO3 from emf measurement and auxiliary data for other phases from the literature, oxygen potential-composition phase diagram and 3-D chemical potential diagram for the system Sm-Rh-O at 1273 K are developed.