Phoneme representation and classification in primary auditory cortex

A controversial issue in neurolinguistics is whether basic neural auditory representations found in many animals can account for human perception of speech. This question was addressed by examining how a population of neurons in the primary auditory cortex (A1) of the naive awake ferret encodes phonemes and whether this representation could account for the human ability to discriminate them. When neural responses were characterized and ordered by spectral tuning and dynamics, perceptually significant features including formant patterns in vowels and place and manner of articulation in consonants, were readily visualized by activity in distinct neural subpopulations. Furthermore, these responses faithfully encoded the similarity between the acoustic features of these phonemes. A simple classifier trained on the neural representation was able to simulate human phoneme confusion when tested with novel exemplars. These results suggest that A1 responses are sufficiently rich to encode and discriminate phoneme classes and that humans and animals may build upon the same general acoustic representations to learn boundaries for categorical and robust sound classification.     

Synthesis of benzodiazepines catalyzed by chitosan functionalized by triacid imide as a superior catalyst

Research on Chemical Intermediates - Chitosan functionalized by triacid imide has been applied as an effective catalyst for the synthesis of benzodiazepines by one-pot reactions of...     

A Comparison of DESI-MS and LC-MS for the Lipidomic Profiling of Human Cancer Tissue

In this study, we make a direct comparison between desorption electrospray ionization-mass spectrometry (DESI-MS) and ultraperformance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) platforms for the profiling of glycerophospholipid (GPL) species in esophageal cancer tissue. In particular, we studied the similarities and differences in the range of GPLs detected and the congruency of their relative abundances as detected by each analytical platform. The main differences between mass spectra of the two modalities were found to be associated with the variance in adduct formation of common GPLs, rather than the presence of different GPL species. Phosphatidylcholines as formate adducts in UPLC-ESI-MS accounted for the majority of differences in negative ion mode and alkali metal adducts of phosphatidylcholines in DESI-MS for positive ion mode. Comparison of the relative abundance of GPLs, normalized to a common peak, revealed a correlation coefficient of 0.70 (P < 0.001). The GPL profile detected by DESI-MS is congruent to UPLC-ESI-MS, which reaffirms the role of DESI-MS for lipidomic profiling and a potential premise for quantification.

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Net demand prediction for power systems by a new neural network‐based forecasting engine

Integration of renewable generations, such as wind and photovoltaic, into electrical power systems is rapidly growing throughout the world. Stochastic and variable nature of these resources makes some operational challenges to power systems. The most effective way to tackle these challenges is short‐term prediction of their available powers. Despite various developed methods to forecast generation of renewable resources, still they have large errors, which may lead to under/over‐commitment of conventional generators in power systems. Prediction of net demand (ND), defined as electrical load minus renewable generations, can provide useful information for accurate scheduling of conventional generators. In this article, characteristics of the time series of electric load, renewable generations and ND are analyzed, and a new hybrid prediction strategy is presented for direct prediction of ND. The training mechanism of the proposed forecasting engine is composed of a new stochastic search method and Levenberg–Marquardt learning algorithm based on an iterative procedure and greedy search. The suggested prediction strategy is tested on different real‐world power systems and its obtained results are compared with the results of several other forecast methods and published literature figures. These comparisons confirm the validity of the developed forecasting strategy. © 2016 Wiley Periodicals, Inc. Complexity 21: 296–308, 2016     

Essential oil composition of three accessions of Dracocephalum heterophyllum Benth. cultivated at Palampur, India

Seeds of three accessions of Dracocephalum heterophyllum growing wild in high altitude locations in the cold desert regions of trans-Himalaya were collected and propagated under polyhouse conditions at IHBT Palampur to evaluate, compare their growth performance and essential oil composition. Three different chemotypes viz., citronellol-rose oxides type, citronellol type and citronellal-trans rose oxide type were identified. Samples of essential oils were distilled separately from leaves and inflorescence of all the three cultivated accessions and characterised by capillary GC and GC-MS techniques, which showed variation in the volatile constituents. In all the three accessions, citronellol was higher in the inflorescence oils as compared to leaf oils. Rose oxides, which are important high odour value cyclic monoterpene ethers were found in the essential oil samples of leaves as well as in the inflorescence of accession II, whereas these constituents were absent in the leaves of other two accessions. The essential oil from the inflorescence part of accession I was devoid of both cis and trans isomers of rose oxide, while accession III contained only one isomer of trans rose oxide. Under polyhouse conditions, seed germination was higher in accession III, whereas aerial biomass was higher in accession I. In comparison to other accessions, yield of volatile oil was higher in both leaf and inflorescence of accessions I, whereas accession II had higher oil yield from inflorescence. This is the first report of volatile oil composition of three accessions of D. heterophyllum cultivated at Palampur having potential as a new source of high valued essential oil for introduction in the perfumery industry.     

Dinuclear copper complexes with cyanamide derivatives as bridging ligands

Three mixed-valence copper complexes [{Cu(phen)₂}₂(μ-L)](PF₆)₂ (where phen = 1,10-phenanthroline, L = 1,4-dicyanamidobenzene (dicyd)), 1,4-dicyanamido-2,5-dimethylbenzene (Me₂dicyd) and 1,4-dicyanamido-2,5-dichlorobenzene (Cl₂dicyd), and one dinuclear Cu(II) complex [{Cu(phen)₂}₂(μ-apc)](PF₆)₃ (where apc = monoanion of 4-azo(phenylcyanamido)benzene) have been prepared and characterized by elemental analysis, IR and electronic absorption spectroscopies and cyclic voltammetry. [{Cu(phen)₂}₂(μ-apc)](PF₆)₃ · 2CH₃COCH₃ crystallized in the triclinic system and both five-coordinate Cu(II) ions in the dinuclear unit are linked through a bridging 4-azo(phenylcyanamido)benzene (apc) ligand. The cyanamide group (NCN) of the bridging ligand is coordinated to Cu(II) ions through the cyano-nitrogen and amido-nitrogen. The bond length between Cu(1) and cyano-nitrogen is slightly larger than that formed by Cu(2) and amido-nitrogen. The angular structural index parameters, τ, for Cu(1) and Cu(2) are 0.9 and 0.5, respectively. The copper(II) atoms display a different geometry with a N₅ chromophore group. The intra Cu?Cu separation is 5.156(1) Å. All of the dicyd dinuclear copper complexes show radical anion absorption.