Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magneti

Single microtubules and small networks become significantly stiffer on short time-scales upon mechanical stimulation

The transfer of mechanical signals through cells is a complex phenomenon. To uncover a new mechanotransduction pathway, we study the frequency-dependent transport of mechanical stimuli by single microtubules and small networks in a bottom-up approach using optically trapped beads as anchor points. We interconnected microtubules to linear and triangular geometries to perform micro-rheology by defined oscillations of the beads relative to each other. We found a substantial stiffening of single filaments above a characteristic transition frequency of 1-30 Hz depending on the filament's molecular composition. Below this frequency, filament elasticity only depends on its contour and persistence length. Interestingly, this elastic behavior is transferable to small networks, where we found the surprising effect that linear two filament connections act as transistor-like, angle dependent momentum filters, whereas triangular networks act as stabilizing elements. These observations implicate that cells can tune mechanical signals by temporal and spatial filtering stronger and more flexibly than expected

Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magnetic

Reversible conductance and surface polarity switching with synthetic molecular switches

In recent years, the self-assembly of organic molecules has been employed to create complex smart materials. The self-assembly of smart organic molecules, such as molecular machines, could be a feasible bottom-up approach to build a variety of electronic devices. Spiropyran based molecular switches play a key role in the development of photo-responsive smart materials. However, despite research for over a hundred years in the literature on spiropyrans and related compounds, the development of functional electronic devices is still a challenge. The key issue with such functional molecules is to translate the changes that occur in a single molecule to the macroscopic world so that we can experience the result and put it to use. In this thesis, the proof of principle was demonstrated through the development and characterization of self-assembled monolayers of spiropyran and azobenzene based molecular switches. The structure of these molecules and the mechanism of their switching behavior was studied by Photo-electron spectroscopy, and water contact angle measurements. The electronic properties of the spiropyran SAMs were measured in molecular tunneling junctions comprising Eutectic Gallium-Indium as top contact. We optimized the photo-switchable conductivity by using the co-adsorption of different molecules with spiropyran on the surface. Afterwards, the optimized systems were used for the development of a proof-of-concept non-volatile memory device, where we could successfully write and erase information on the surface by using acid and base. At the end of the thesis, we also demonstrate the photo-switchable wettability of a gold surface by using host-guest chemistry

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Electron transport in nanotube--molecular wire hybrids

We study contact effects on electron transport across a molecular wire sandwiched between two semi-infinite (carbon) nanotube leads as a model for nanoelectrodes. Employing the Landauer scattering matrix approach we find that the conductance is very sensitive to parameters such as the coupling strength and geometry of the contact. The conductance exhibits markedly different behavior in the two limiting scenarios of single contact and multiple contacts between the molecular wire and the nanotube interfacial atoms. In contrast to a single contact the multiple-contact configuration acts as a filter selecting single transport channels. It exhibits a scaling law for the conductance as a function of coupling strength and tube diameter. We also observe an unusual narrow-to-broad-to-narrow behavior of conductance resonances upon decreasing the coupling.Comment: 4 pages, figures include