Ionic Liquids and Electrolytes with Flexible Aromatic Anions.

Five new n-tetrabutylphosphonium (P4444 )+ cation-based ionic liquids (ILs) with oligoether substituted aromatic carboxylate anions have been synthesized. The nature and position of the oligoether chain affect thermal stability (up to 330 °C), phase behaviour (Tg <-55 °C) and ion transport. Furthermore, with the aim of application in lithium batteries, electrolytes were created for two of the ILs by 10 mol% doping using the corresponding Li-salts. This affects the ion diffusion negatively, from being higher and equal for cations and anions to lower for all ions and unequal. This is due to the stronger ionic interactions and formation of aggregates, primarily between the Li+ ions and the carboxylate group of the anions. Electrochemically, the electrolytes have electrochemical stability windows up to 3.5 V, giving some promise for battery application.

Structurally flexible pyrrolidinium- and morpholinium-based ionic liquid electrolytes.

Ion transport measures and details as well as physico-chemical and electrochemical properties are presented for a small set of structurally flexible pyrrolidinium (Pyrr) and morpholinium (Morph) cation-based ionic liquids (ILs), all with oligoether phosphate-based anions. All have high thermal stabilities, low glass transition temperatures, and wide electrochemical stability windows, but rather moderate ionic conductivities, where both the anions and the cations of the Pyrr-based ILs diffuse faster than those of the Morph-based ILs. Overall, the Pyrr-based ILs show significantly more promise as high-temperature supercapacitor electrolytes, rendering a specific capacitance of 164 F g-1 at 1 mV s-1, a power density of 609 W kg-1 and a specific energy density of 27 W h kg-1 at 90 °C in a symmetric graphite supercapacitor.

Physical and electrochemical properties of new structurally flexible imidazolium phosphate ionic liquids.

New structurally flexible 1-methyl- and 1,2-dimethyl-imidazolium phosphate ionic liquids (ILs) bearing oligoethers have been synthesized and thoroughly characterized. These novel ILs revealed high thermal stabilities, low glass transitions, high conductivity and wide electrochemical stability windows up to 6 V. Both the cations and anions of 1-methyl-imidazolium ILs diffuse faster than the ions of 1,2-dimethyl-imidazolium ILs, as determined by pulsed field gradient nuclear magnetic resonance (PFG-NMR). The 1-methyl-imidazolium phosphate ILs showed relatively higher ionic conductivities and ion diffusivity as compared with the 1,2-dimethyl-imidazolium phosphate ILs. As expected, the diffusivity of all the ions increases with an increase in the temperature. The 1-methyl-imidazolium phosphate ILs formed hydrogen bonds with the phosphate anions, the strength of which is decreased with increasing temperature, as confirmed by variable temperature 1H and 31P NMR spectroscopy. One of the representative IL, [EmDMIm][DEEP], presented promising elevated temperature performance as an electrolyte in a supercapacitor composed of multiwall carbon nanotubes and activated charcoal (MWCNT/AC) composite electrodes.

TROVE: a user-friendly tool for visualizing and analyzing cancer hallmarks in signaling networks.

SUMMARY: Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, an user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. AVAILABILITY AND IMPLEMENTATION: Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove.

Coordination-Driven Self-Assembly of Ionic Irregular Hexagonal Metallamacrocycles via an Organometallic Clip and Their Cytotoxicity Potency.

Two new irregular hexagons (6 and 7) were synthesized from a pyrazine motif containing an organometallic acceptor clip [bearing platinum(II) centers] and different neutral donor ligands (4,4'-bipyridine or pyrazine) using a coordination-driven self-assembly protocol. The two-dimensional supramolecules were characterized by multinuclear NMR, mass spectrometry, and elemental analyses. Additionally, one of the macrocycles (6) was characterized by single-crystal X-ray analyses. Macrocycles are unique examples of [2 + 2] self-assembled ensembles that are hexagonal but irregular in shape. These hexagon frameworks require the assembly of only four tectons/subunits. The cytotoxicity of platinum(II)-based macrocycles was studied using various cell lines such as A549 (human lung carcinoma), KB (human oral cancer), MCF7 (human breast cancer), and HaCaT (human skin keratinocyte) cell lines, and the results were compared with those of cisplatin. The smaller macrocycle (7) exhibited a higher cytotoxic effect against all cell types, and its sensitivity was found to be comparable with that of cisplatin for A549 and MCF7 cells. Cell cycle analysis and live propidium iodide staining suggest that the macrocycles 6 and 7 induced a loss of membrane integrity that ultimately might lead to necrotic cell death.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches.

TENET: topological feature-based target characterization in signalling networks.

MOTIVATION: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features. RESULTS: Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet CONTACT: hechua@ntu.edu.sg or assourav@ntu.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DualAligner: a dual alignment-based strategy to align protein interaction networks.

MOTIVATION: Given the growth of large-scale protein-protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein-protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity. RESULTS: DualAligner performs dual network alignment, in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach. AVAILABILITY AND IMPLEMENTATION: http://www.cais.ntu.edu.sg/∼assourav/DualAligner/.

Pyrazine motif containing hexagonal macrocycles: synthesis, characterization, and host-guest chemistry with nitro aromatics.

The synthesis and characterization of cationic two-dimensional metallamacrocycles having a hexagonal shape and cavity are described. Both macrocycles utilize a pyrazine motif containing an organometallic acceptor tecton with platinum(II) centers along with different donor ligands. While one macrocycle is a relatively larger [6 + 6], the other is a relatively smaller [2 + 2] polygon. A unique feature of the smaller ensemble is that it is an irregular polygon in which all six edges are not of equal length. Molecular modeling of these macrocycles confirmed the presence of hexagonal cavities. The ability of these π-electron rich macrocycles to act as potential hosts for relatively electron deficient nitroaromatics (DNT = 2,4-dinitrotoluene and PA = picric acid) has been studied using isothermal titration calorimetry (ITC) as a tool. Molecular dynamics simulation studies were subsequently performed to gain critical insight into the binding interactions between the nitroaromatic guest molecules (PA/DNT) and the ionic macrocycles reported herein.

Pyrazine-based organometallic complex: synthesis, characterization, and supramolecular chemistry.

The design, synthesis, and characterization of a new pyrazine-based ditopic platinum(II) organometallic complex are reported. The molecular structure of the organoplatinum pyrazine dipod was determined by single-crystal X-ray crystallography. The potential utility of this organometallic ditopic acceptor as a building block in the construction of neutral metallasupramolecular macrocycles containing the pyrazine motif was explored. Pyrazine motifs containing supramolecules were characterized by multinuclear NMR (including (1)H DOSY), mass spectrometry, and elemental analysis. The geometry of each supramolecular framework was optimized by employing the PM6 semiempirical molecular orbital method to predict its shape and size. The ability of the pyrazine-based organoplatinum complex to act as a host for nitroaromatic guest (2,4-dinitrotoluene and PA) molecules was explored by isothermal titration calorimetry (ITC). The binding stoichiometry and thermodynamic parameters of these host-guest complexation reactions were evaluated using ITC. Theoretical calculations were performed to obtain insight into the binding pattern between the organometallic host and nitroaromatic guests. The preferable binding propensity of the binding sites of complex 1 for both nitroaromatics (PA and 2,4-dinitrotoluene) determined by molecular simulation studies corroborates well with the experimental results as obtained by ITC experiments.

DiffNet: automatic differential functional summarization of dE-MAP networks.

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility.

Binding and interaction of di- and tri-substituted organometallic triptycene palladium complexes with DNA.

Two triptycene-based ligands with pendant bromophenyl units have been prepared. These triptycene derivatives have been used as synthons for the synthesis of di and tri nuclear palladium complexes. The organic molecules and their corresponding organometallic complexes have been fully characterized using nuclear magnetic resonance (NMR), infrared (IR) spectroscopy and mass spectrometry. The mode of binding and effect of the complexes on pUC19 plasmid, calf thymus DNA and oligomer duplex DNA have been investigated by a host of analytical methods. The complexes brought about unwinding of supercoiled plasmid and the unwinding angle was found to be related to the binding affinity of the complexes with DNA, where both these parameters were guided by the structure of the complexes. Concentration-dependent inhibition of endonuclease activity of SspI and BamHI by the complexes indicates preference for G/C sequence for binding to DNA. However, neither the complexes did not introduce any cleavage at abasic site in oligomer duplex DNA, nor they created linear form of the plasmid upon co-incubation with the DNA samples. The interactions of the complexes with DNA were found to be strongly guided by the structure of the complexes, where intercalation as well as groove binding was observed, without inflicting any damage to the DNA. The mode of interaction of the complexes with DNA was further confirmed by isothermal calorimetry.

Effect of thiobencarb and pretilachlor on microorganisms in relation to mineralization of C and N in the Gangetic alluvial soil of West Bengal.

An experiment was conducted under laboratory conditions to investigate the effect of two pre-emergence herbicides, viz., thiobencarb (at 1.5 and 4.5 kg active ingredient (a.i.) ha(-1)) and pretilachlor (at 0.5 and 1.5 kg a.i. ha(-1)), on the growth and multiplication of some microorganisms (bacteria, actinomycetes and fungi) in relation to transformations and availability of C and N in the Gangetic alluvial soil (Typic Haplustept) of West Bengal, India. Application of both the herbicides, in general, significantly increased microbial biomass, resulting in greater retention, mineralization and availability of oxidizable organic C and N in soil, and the stimulations were more pronounced when the herbicides were applied at their lower concentrations (recommended field application rates), more so with thiobencarb, as compared to pretilachlor. Compared to untreated control soil, the application of thiobencarb at lower concentration increased the proliferation of total bacteria, actinomycetes and fungi by 57.3, 36.6 and 55.2%, respectively, and released the highest amount (40.2%) of soluble NO3(-) in soil, while pretilachlor at field application rate induced the growth and multiplication of bacteria and fungi by 58.3 and 17.6%, respectively. Irrespective of the concentrations, the stimulations were at par for both the herbicides towards the retention of oxidizable organic C, total N and exchangeable NH4(+) in soil.

Synthesis, characterization and DNA interaction studies of new triptycene derivatives.

A facile and efficient synthesis of a new series of triptycene-based tripods is being reported. Using 2,6,14- or 2,7,14-triaminotriptycenes as synthons, the corresponding triazidotriptycenes were prepared in high yield. Additionally, we report the transformation of 2,6,14- or 2,7,14-triaminotriptycenes to the corresponding ethynyl-substituted triptycenes via their tribromo derivatives. Subsequently, derivatization of ethynyl-substituted triptycenes was studied to yield the respective propiolic acid and ethynylphosphine derivatives. Characterization of the newly functionalized triptycene derivatives and their regioisomers were carried out using FTIR and multinuclear NMR spectroscopy, mass spectrometry, and elemental analyses techniques. The study of the interaction of these trisubstituted triptycenes with various forms of DNA revealed interesting dependency on the functional groups of the triptycene core to initiate damage or conformational changes in DNA.

FACETS: multi-faceted functional decomposition of protein interaction networks.

MOTIVATION: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. RESULTS: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. SUPPLEMENTARY INFORMATION: Supplementary data are available at the Bioinformatics online. AVAILABILITY: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

How to induce multiple delays in coupled chaotic oscillators?

Lag synchronization is a basic phenomenon in mismatched coupled systems, delay coupled systems, and time-delayed systems. It is characterized by a lag configuration that identifies a unique time shift between all pairs of similar state variables of the coupled systems. In this report, an attempt is made how to induce multiple lag configurations in coupled systems when different pairs of state variables attain different time shift. A design of coupling is presented to realize this multiple lag synchronization. Numerical illustration is given using examples of the Rössler system and the slow-fast Hindmarsh-Rose neuron model. The multiple lag scenario is physically realized in an electronic circuit of two Sprott systems.

Vanadium Oxide Nanosheet-Infused Functionalized Polysulfone Bipolar Membrane for an Efficient Water Dissociation Reaction.

A high-performing bipolar membrane (BPM) was fabricated using functionalized polysulfones as the ion-exchange layers (IELs) and two-dimensional (2D) V2O5-nanosheets blended with polyvinyl alcohol (PVA) as the water dissociation catalyst (WDC) at the interfacial layer. The composite BPM showed a low resistance of 0.79 Ω cm2, confirming the good contact between the IEL and WDC, much needed for the ionic conductivity. It also demonstrated high water dissociation performance with a water dissociation voltage of 1.11 V corresponding to a current density of 1.02 mA/cm2 in the presence of a 1 M NaCl electrolytic solution. The functionalization of the polysulfone with -SO3- and R4N+ groups successfully resulted in the increase of hydrophilicity of the polymer, thereby increasing the water uptake capacity of the membranes. The blending of 2D V2O5 nanosheets with PVA proved to be an effective WDC, as confirmed by the increased conductivity and efficiency of the water dissociation (WD) reaction. The 2D V2O5-ns have great potential toward water adsorption onto its surface, thereby interacting with the water molecules, weakening the bonding force of water, and dissociating it into H+ and OH-. The transportation of coions across the membranes and generation of protons and hydroxyl ions at the interfacial layer are correlated with the change in the pH of the catholyte and anolyte as a function of current density during the WD reaction. The high performance of the composite BPM (BPM_VO-ns) was demonstrated at a higher current density of 100 mA/cm2 with a WD resistance of 0.027 Ω cm2. The durability was tested by subjecting it to 45 h of run at lower (1.02 mA/cm2) and higher (100 mA/cm2) current densities which display a negligible change in the interlayer voltage. Thus, the fabricated composite BPMs pave the way to be utilized for efficient and durable WD reactions under neutral electrolytic conditions.

Magnetically Triggered Interplay of Capacitive and Diffusion Contributions for Boosted Supercapacitor Performance.

The chemistry involved in supercapacitors in terms of their mechanistic contributions leading to improved specific capacity will aid in easy commercialization. The main contributory factors in a supercapacitor are either capacitive (non-diffusion controlled) or ion-diffusion behavior, which results in enhanced charge-discharge characteristics of a supercapacitor reflected in its power density, whereas ion-diffusion behavior will lead to the enhanced energy density of a supercapacitor. In this context, the present article attempts to understand the use of external magnetic fields, leading to the interplay between capacitive and ion-diffusion behavior in a high-performance supercapacitor. The model system chosen in the present study, nickel cobalt copper carbonate hydroxide (NiCoCuCH), can effectively address the interplay between capacitive and ion diffusion contributions by varying total magnetic effects involving magnetic dilution. The magneto-enhancement of the electrodes nickel cobalt copper carbonate hydroxide (NiCoCuCH) and aluminum-doped nickel cobalt copper carbonate hydroxide (Al-NiCoCuCH) was demonstrated under the magnetic field from 0 to 250 mT. Both NiCoCuCH and Al-NiCoCuCH show dominant non-diffusion-controlled (capacitive) and diffusion-controlled behavior as a function of the applied external magnetic field. Under the influence of the external magnetic field, ferromagnetic coupling between metal-oxygen-metal centers via oxygen 2p orbitals enhances, leading to a facile redox pathway. To further control the charge-discharge behavior of the electrode via the interplay between diffusive and capacitive, a non-magnetic ion, Al3+, was doped into the bare metal carbonate hydroxide crystal lattice. The Al3+ ion not only alters the crystal symmetry but also restricts the alignment of the magnetic domains in the electrode, leading to a sluggish redox pathway, effectively increasing the capacitive contribution, and leading to improved charge-discharge characteristics at the expense of energy density. We have constructed an asymmetric device with the best-performing (110 mT) NiCoCuCH electrode as a positive electrode and activated carbon as a negative electrode. The NiCoCuCH/AC ASC device at 110 mT has the largest specific capacity (1100 C g-1 at 2 A g-1) at 110 mT, leading to a high energy density (250 W h kg-1) and a power density (1.7 kW kg-1) of the electrode.

Spectator-metal-ion-guided redox-dominant cobalt oxyhydroxide as a high-performance supercapacitor.

The ability of spectator metal ions such as vanadium to enhance the electrochemical performance of supercapacitors has been explained. Vanadium-incorporated CoO(OH) combined with NiMn-layered double hydroxide (LDH) yields a specific capacitance of 1700 F g-1 at 1 A g-1 with 96% retention after 5000 cycles. The assembled asymmetric supercapacitor exhibits an energy density of 45.93 W h Kg-1 and a power density of 752 W kg-1@1 A g-1.

Ambient temperature liquid salt electrolytes.

Alkali metal salts usually have high melting points due to strong electrostatic interactions and solvents are needed to create ambient temperature liquid electrolytes. Here, we report on six phosphate-anion-based alkali metal salts, Li/Na/K, all of which are liquids at room temperature, with glass transition temperatures ranging from -61 to -29 °C, and are thermally stable up to at least 225 °C. While the focus herein is on various physico-chemical properties, these salts also exhibit high anodic stabilities, up to 6 V vs. M/M+ (M = Li/Na/K), and deliver some battery performance - at elevated temperatures as there are severe viscosity limitations at room-temperature. While the battery performance arguably is sub-par, solvent-free electrolytes based on alkali metal salts such as these should pave the way for conceptually different Li/Na/K-batteries, either by refined anion design or by using several salts to create eutectic mixtures.