Probing Aromaticity of Borozene Through Optical and Dielectric Response: A Theoretical Study

In this work, we report electronic structure calculations aimed at computing the linear optical absorption spectrum and static dipole polarizablity of a newly proposed boron-based planar aromatic compound borozene (B ₁₂ H ₆). For the purpose, we use the semiempirical INDO model Hamiltonian, accompanied by large-scale correlation calculations using the multi-reference singles-doubles configuration-interaction (MRSDCI) approach. We present detailed predictions about the energetics, polarization properties, and the nature of many-particle states contributing to various peaks in the linear absorption spectrum. Our results can be used to characterize this material in future optical absorption experiments. We also argue that one can deduce the aromaticity of the cluster from the optical absorption and static polarizability results.     

Comparative study on the growth mechanisms of PAHs

The efficiencies of recently proposed, phenyl addition/cyclization (PAC), methyl addition/cyclization (MAC) and a popular hydrogen abstraction/acetylene addition (HACA) mechanisms have been examined experimentally by detecting the gas phase reaction products of pyrolysis of toluene with/without addition of benzene + acetylene by using vacuum ultraviolet (VUV) single photon ionization (SPI) time of flight mass spectrometry (TOFMS). Besides the observation of verities of large polycyclic aromatic hydrocarbons (PAHs), intense mass peaks of indene, phenylacetylene and propyne confirmed a remarkable quenching of the major active species, benzyl, phenyl and methyl radicals by acetylene. In spite of quenching, only benzyl contributed products were diminished while phenyl and methyl contributed products were enhanced. Uniquely observed symmetrical PAHs; corrannulene/coronene; formed by the active involvement of PAC, HACA and/or MAC mechanisms, reflects the interdependencies of these mechanisms. Individually, PAC was found highly efficient for endless growth, HACA for filling triple fusing site and MAC for expanding cyclotetra/pentafused into benzenoid structure, respectively.     

AC impedance and state-of-charge analysis of alkaline zinc/manganese dioxide primary cells

Alternating current impedance data of alkaline Zn/MnO₂ cells were analysed in view of identification of suitable parameters, which depend on the state-of-charge (SOC) of the cells. The impedance of a slightly discharged cell was found to possess impedance considerably lesser than that of an undischarged cell. The data in the form of Nyquist plot contained an inductance part at very high frequencies, a capacitive semicircle at high frequencies and a diffusion linear spike at low frequencies. The low frequency linear spike gradually transformed into a capacitive semicircle with the decrease of SOC of the cell, which was attributed to the nature of the reactions at the Zn anode. Of several impedance parameters that were examined, equivalent series capacitance (C _s) was found to have a strong dependence on SOC of the alkaline Zn/MnO₂ cells. There was a continuous change in a partially discharged cell during its ageing, which was reflected by transformation of low frequency data into a clear semicircle.     

Nickel-based rechargeable batteries

Nickel–iron (Ni–Fe), nickel–cadmium (Ni–Cd), nickel–hydrogen (Ni–H₂), nickel–metal hydride (Ni–MH) and nickel–zinc (Ni–Zn) batteries employ nickel oxide electrodes as the positive plates, and are hence, categorised as nickel-based batteries. This article highlights the operating principles and advances made in these battery systems during the recent years. In particular, significant improvements have been made in the Ni–MH batteries which are slowly capturing the market occupied by the ubiquitous Ni–Cd batteries.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Investigation into the Probable Cause of Failure of Economizer Tube of a Thermal Power Plant

In this investigation, we examined the probable cause of failure of a welded joint of a economizer tube of a 210 MW thermal power plant. X-ray radiography and chemical analysis along with detailed optical and scanning electron microscopic (SEM) examination of the economizer tube were carried out. It was concluded that severe blowholes, pipings, and porosity along with brittle fracture were responsible for the failure.     

Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomic era

Extension of the traditional pharmacological approach of protein target classification to whole target systems has the potential to relate elements of protein sequence to the structure-activity relationship (SAR) of small molecules that can modulate protein action. Grouping potential drug discovery targets into families based on the relatedness of their SAR provides a means to translate the information from genome-sequencing efforts into knowledge that will aid in the discovery of drugs.     

Dental enamel hypoplasia in children with combined congenital and hereditary defects in the development of the CNS and the locomotor system (infantile cerebral palsy, spinal cord hernias and myopathies)]

The incidence of hypoplasia in children with congenital and hereditary developmental defects of the central nervous system and the locomotor system is high (44.5 +/- 3.5%), much higher than the incidence of hypoplasia in children without neurological disorders (2.0 +/- 2.0%). This is explained by exposure of the fetus and newborn to numerous intensive factors complicating the biological anamnesis because of profound disturbances of the metabolic processes and largely responsible for the underlying neurological disease and defects of hard dental tissues presenting as hypoplasia of the enamel.     

Role of caspases (ICE/CED 3 proteases) in DNA damage and cell death in response to a mitochondrial inhibitor, antimycin A

Caspases (ICE/ Ced3 proteases) are a closely related family of cysteine proteases that play a key role in apoptotic cell death. We examined the role of caspases in DNA damage and cell death in response to the mitochondrial inhibitor, antimycin A. LLC-PK1 cells contain caspase activity that was markedly inhibited by cleavage site-based peptide inhibitors of caspases but not by inhibitors of serine, cysteine, aspartate or metalloproteinases. The caspase activity increased within five minutes of exposure to antimycin A, preceding any evidence of DNA damage and cell death. The specific caspase inhibitors. Ac-Tyr-Val-Ala-Asp-aldehyde (inhibitor I) and Ac-Asp-Glu-Val-Asp-aldehyde (inhibitor II) prevented, in a dose dependent manner, antimycin A-induced DNA strand breaks as determined by DNA unwinding assay (residual double stranded DNA in control, 94 +/- 2%; antimycin A alone, 48 +/- 3%; antimycin A + inhibitor I at 50 microM, 93 +/- 2%; antimycin A + inhibitor II at 50 microM, 89 +/- 5%; N = 3 to 4, P < 0.001). These inhibitors also prevented antimycin A-induced DNA fragmentation as determined by agarose gel electrophoresis and by in situ labeling of cell nuclei by the terminal deoxynucleotidyl transferase (TdT) nick end labeling (TUNEL) method. The caspase inhibitors markedly prevented antimycin A-induced cell death in a dose-dependent manner as measured by trypan blue exclusion (control 6 +/- 1%, antimycin A alone 40 +/- 1%, antimycin A + inhibitor I at 50 microM 16 +/- 1%, antimycin A + inhibitor II at 50 microM 16 +/- 1%; N = 4 to 7, P < 0.001). These data indicate that the caspase family of enzymes play an important role in DNA damage and cell death in response to the mitochondrial inhibitor, antimycin A.