A Scalable Parallel Algorithm for Reachability Analysis of Very Large Circuits

This paper presents a scalable method for parallelizing symbolic reachability analysis on a distributed-memory environment of workstations. We have developed an adaptive partitioning algorithm that significantly reduces space requirements. The memory balance is maintained by dynamically repartitioning the state space throughout the computation. A compact BDD representation allows coordination by shipping BDDs from one machine to another. This representation allows for different variable orders in the sending and receiving processes. The algorithm uses a distributed termination protocol, with none of the memory modules preserving a complete image of the set of reachable states. No external storage is used on the disk. Rather, we make use of the network, which is much faster.We implemented our method on a standard, loosely-connected environment of workstations, using a high-performance model checker. Initial performance evaluation of several large circuits shows that our method can handle models too large to fit in the memory of a single node. The partitioning algorithm achieves reduction in space, which is linear in the number of workstations employed. A corresponding decrease in space requirements is measured throughout the reachability analysis. Our results show that the relatively slow network does not become a bottleneck, and that computation time is kept reasonably small.     

Towards a re-programmable DNA computer

Microreactors lend themselves to a relatively simple implementation of DNA computing. Not only is the design of the DNA library critical for the success of the system but also the architecture of the microfluidic structure. Microreactors can be configured as Boolean operators. This paper will show that biomolecular computing can be performed with elementary building blocks, analogous to electronic logic gates. These logical operations will be performed using negative selection. Furthermore, an alternative bead barrier is introduced which can render the computer re-programmable and shows an principle architecture for selection and analysis.     

The next generation models for crops and agro-ecosystems

Growth in population, decrease in arable land area, and change in climate are endangering our food security. Precision agriculture has the potential to increase crop productivity thorough tailored agricultural practices for different growing areas. Many models of crops and agro-ecosystems capable of predicting interaction between plants and environments have been developed for precision agriculture. Currently, there are several representative categories of crop and agro-ecosystem models, including the de Wi...     

Shifting gears: Changing algorithms on the fly to expedite Byzantine agreement

We describe several new algorithms for Byzantine agreement. The first of these is a simplification of the original exponential-time Byzantine agreement algorithm due to Pease, Shostak, and Lamport, and is of comparable complexity to their algorithm. However, its proof is very intuitively appealing. A technique of shifting between algorithms for solving the Byzantine agreement problem is then studied. We present two families of algorithms obtained by applying a shift operator to our first algorithm. These families obtain the same rounds to message length trade-off as do Coan's families but do not require the exponential local computation time (and space) of his algorithms. We also describe a modification of an -resilient algorithm for Byzantine agreement of Dolev, Reischuk, and Strong. Finally, we obtain a hybrid algorithm that dominates all our others, by beginning execution of an algorithm in one family, first shifting into an algorithm of the second family, and finally shifting into an execution of the adaptation of the Dolev, Reischuk, and Strong algorithm.     

On-line coupling of high-performance liquid chromatography to a continuous-flow enzyme assay based on electrospray ionization mass spectrometry

Liquid chromatography (LC) was coupled on-line to a continuous-flow enzymatic assay using electrospray ionization mass spectrometry (ESI-MS) as readout for the screening of enzyme inhibitors in complex samples. Inhibitors were detected by changes in the concentration of the enzymatic reaction products, indicating the inhibition of enzymatic activity. The molecular masses of the inhibitors were determined with high certainty by using retention time matching and peak shape comparison. Due to the high matching accuracy, baseline separation of coeluting analytes was not necessary in order to identify the correct masses of the bioactive compounds. The continuous-flow system was successfully applied for the screening of complex samples, such as natural extracts. For a red clover extract, detection limits of 0.3-0.8 micromol/L were obtained. System validation was performed by determining the IC(50) values of four inhibitors in the flow-injection mode. The IC(50) values were in the 0.11-5.6 micromol/L range and correspond closely to data obtained by microtiter plate assays. Detection limits were in the range of 0.018-0.35 micromol/L in the flow-injection mode, and 0.075-0.75 micromol/L in the LC mode. These values are well below the typical compound concentrations (1-10 micromol/L) used in high-throughput screening. Together with an interday precision of 12.6%, these results demonstrate the applicability of the system for bioactivity screening of complex mixtures, generating both chemical and biological information on bioactive compounds in a single run.     

In silico design of small RNA switches

The discovery of natural RNA sensors that respond to a change in the environment by a conformational switch can be utilized for various biotechnological and nanobiotechnological advances. One class of RNA sensors is the riboswitch: an RNA genetic control element that is capable of sensing small molecules, responding to a deviation in ligand concentration with a structural change. Riboswitches are modularly built from smaller components. Computational methods can potentially be utilized in assembling these building block components and offering improvements in the biochemical design process. We describe a computational procedure to design RNA switches from building blocks with favorable properties. To achieve maximal throughput for genetic control purposes, future designer RNA switches can be assembled based on a computerized preprocessing buildup of the constituent domains, namely the aptamer and the expression platform in the case of a synthetic riboswitch. Conformational switching is enabled by the RNA versatility to possess two highly stable states that are energetically close to each other but topologically distinct, separated by an energy barrier between them. Initially, computer simulations can produce a list of short sequences that switch between two conformers when trigerred by point mutations or temperature. The short sequences should possess an additional desirable property; when these selected small RNA switch segments are attached to various aptamers, the ligand binding mechanism should replace the aforementioned event triggers, which will no longer be effective for crossing the energy barrier. In the assembled RNA sequence, energy minimization folding predictions should then show no difference between the folded structure of the entire sequence relative to the folded structure of each of its constituents. Moreover, energy minimization methods applied on the entire sequence could aid at this preprocessing stage by exhibiting high mutational robustness to capture the stability of the formed hairpin in the expression platform. The above computer-assisted assembly procedure together with application specific considerations may further be tailored for therapeutic gene regulation. Index Terms-Design of RNA switches, energy minimization methods, RNA folding predictions.     

Processing of Boron Carbide-Aluminum Composites

The processing problems associated with boron carbide and the limitations of its mechanical properties can be significantly reduced when a metal phase (e.g., aluminum) is added. Lower densification temperatures and higher fracture toughness will result. Based on fundamental capillarity ther modynamics, reaction thermodynamics, and densification kinetics, we have established reliable criteria for fabricating B₄C–Al particulate composites. Because chemical reactions cannot be eliminated, it is necessary to process B₄C–AI by rapidly heating to near 1200°C (to ensure wetting) and subsequently heat-treating below 1200°C (for microstructural development).