Dielectric and crystal-optic properties of lithium ammonium tartrate mono-hydrate ferro-elastic crystal under mechanic stress: Piezo-dielectric and piezooptic effects at the phase transition

Dielectric constants, birefringence and rotation of optical and dielectric ellipsoids in LAT vs temperature and mechanic stress are studied     

Superhydrophobicity and superhydrophilicity of regular nanopatterns

The hydrophilicity, hydrophobicity, and sliding behavior of water droplets on nanoasperities of controlled dimensions were investigated experimentally. We show that the "hemi-wicking" theory for hydrophilic SiO(2) samples successfully predicts the experimental advancing angles and that the same patterns, after silanization, become superhydrophobic in agreement with the Cassie-Baxter and Wenzel theories. Our model topographies have the same dimensional scale of some naturally occurring structures that exhibit similar wetting properties. Our results confirm that a forest of hydrophilic/hydrophobic slender pillars is the most effective superwettable/water-repellent configuration. It is shown that the shape and curvature of the edges of the asperities play an important role in determining the advancing angles.     

Prediction of proprotein convertase cleavage sites

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.     

Feature-based prediction of non-classical and leaderless protein secretion

We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.     

Flexible Inserts for Injection Molding of Complex Micro-Structured Polymer Components

Mass production of microfluidic devices commonly relies on injection molding. Injection molding requires a master surface made using micro or nanofabrication. Conventionally, electroplating from a silicon master is used for mold insert production, but this is expensive and cannot be used with masters produced via the Bosch process as interlocking of the scalloping between polymer and metal insert hinders part ejection. Here, an alternative to the electroplating process is developed by adapting a nanoimprint route to produce flexible micro-structured polymer inserts capable of molding using a Bosch process-produced master. An optimized fabrication approach using silicon masters with smooth sidewalls (produced using the mixed process) is used to characterize the limits of the process. Aspect ratios of 1 and below are successfully replicated. Feature spacings down to 20 µm are successfully produced with minimal variation between repeated parts. Masters produced using two different Bosch etches exhibiting both coarse and fine nanometer scalloping are also studied. Parts are successfully ejected with retained nanometer scalloping in all samples although inlay damage occurred after >10 replicas. A proof-of-concept microfluidic device is successfully produced vindicating the use of this approach as an efficient and cost-effective approach for the rapid prototyping of complex micro-structured designs.     

Skopus: Mining top-<Emphasis Type="Italic">k</Emphasis> sequential patterns under leverage

This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern—a concept on which most interestingness measures directly rely—with (2) Skopus: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed. The larger the support compared to the expectation under independence, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art.     

Maintaining AUC and H-measure over time

Machine Learning - Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline...     

Laser Sintering of SiO2 Powder Compacts

Regularities of sintering compacted SiO₂ powder under the action of a CO₂-laser are studied by experiment. Features are considered for rebuilding the structure of a powder body (particle regrouping, formation of interparticle contacts, joining of particles into conglomerates, evolution of the porous structure) in different stages of sintering (with different laser irradiation duration). Sintering is accomplished by a liquid-phase mechanism (by melting particles and joining solid unmelted cores by the liquid phase formed). The results obtained may be used for developing laser sintering methods for ceramic powders.     

Newton's method revisited: How accurate do we have to be?

We analyze the convergence of quasi-Newton methods in exact and finite precision arithmetic using three different techniques. We derive an upper bound for the stagnation level and we show that any sufficiently exact quasi-Newton method will converge quadratically until stagnation. In the absence of sufficient accuracy, we are likely to retain rapid linear convergence. We confirm our analysis by computing square roots and solving bond constraint equations in the context of molecular dynamics. In particular, we apply both a symmetric variant and Forsgren's variant of the simplified Newton method. This work has implications for the implementation of quasi-Newton methods regardless of the scale of the calculation or the machine.     

Engineering a substrate-specific cold-adapted subtilisin

One region predicted to be highly flexible for a psychrophilic enzyme, TA39 subtilisin (S39), was transferred in silico to the mesophilic subtilisin, savinase (EC 3.4.21.62), from Bacillus lentus (clausii). The engineered hybrid and savinase were initially investigated by molecular dynamic simulations at 300 K to show binding region and global flexibility. The predicted S39 region consists of 12 residues, which due to homology between the subtilisins, results in a total change of eight residues. By site-directed modifications, the region was transferred to the binding region of savinase, thus a savinase-S39 hybrid, named H5, was constructed. The designed hybrid showed the same temperature optimum and pH profile as savinase, but H5 had higher specific activity on the synthetic substrate N-succinyl-L-Ala-L-Ala-L-Pro-L-Phe-p-nitroanilide (AAPF) at all temperatures measured and, at the same time, H5 showed a decrease in thermostability. The H5 hybrid showed broader substrate specificity, measured at room temperature, due to an increase in catalytic efficiency on AAPF, AAPA and FAAF compared with savinase (N-succinyl-XXXX-pNA; XXXX = AAPF, AAPA and FAAF). The H5 hybrid showed increased activity at low temperature, increased binding region and global flexibility, as investigated by molecular dynamic simulations, and global destabilization from differential scanning calorimetry measurements. These psychrophilic characteristics indicated an increase in binding site flexibility, probably due to the modifications P129S, S130G, P131E, and thus we show that it is possible to increase low temperature activity and global flexibility by engineered flexibility in the binding region.