Identification of the Catalytic Residues in the Cyclase Domain of the Class IV Lanthipeptide Synthetase SgbL

Lanthipeptides belong to the family of ribosomally synthesized and post-translationally modified peptides (RiPPs) and are subdivided into different classes based on their processing enzymes. The three-domain class IV lanthipeptide synthetases (LanL enzymes) consist of N-terminal lyase, central kinase, and C-terminal cyclase domains. While the catalytic residues of the kinase domains (mediating ATP-dependent Ser/Thr phosphorylations) and the lyase domains (carrying out subsequent phosphoserine/phosphothreonine (pSer/pThr) eliminations to yield dehydroalanine/dehydrobutyrine (Dha/Dhb) residues) have been characterized previously, such studies are missing for LanL cyclase domains. To close this gap of knowledge, this study reports on the identification and validation of the catalytic residues in the cyclase domain of the class IV lanthipeptide synthetase SgbL, which facilitate the nucleophilic attacks by Cys thiols on Dha/Dhb residues for the formation of β-thioether crosslinks.     

Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation

The Nelder-Mead simplex method is an optimization routine that works well with irregular objective functions. For a function of $n$ parameters, it compares the objective function at the $n+1$ vertices of a simplex and updates the worst vertex through simplex search steps. However, a standard serial implementation can be prohibitively expensive for optimizations over a large number of parameters. We describe an implementation of the Nelder-Mead method in parallel using a distributed memory. For $p$ processors, each processor is assigned $(n+1)/p$ vertices at each iteration. Each processor then updates its worst local vertices, communicates the results, and a new simplex is formed with the vertices from all processors. We also describe how the algorithm can be implemented with only two MPI commands. In simulations, our implementation exhibits large speedups and is scalable to large problem sizes.     

Direct Ink-Jet Printing of Vertical Walls

Direct jet printing can assemble ceramic powder into a three dimensional shape by firing droplets of ink through a nozzle to build a multiple layered structure. As with stereolithography and selective laser sintering, the surface texture is expected to witness the layered assembly. The ability to create vertical walls by direct ink-jet printing was explored using a test piece based on a maze. The structure and topography are discussed in terms of droplet spreading and drying.     

Modelling of membrane nanofiltration—pore size distribution effects

The effects of log-normal pore size distributions on the rejection of uncharged solutes and NaCl at hypothetical nanofiltration membranes have been assessed theoretically. The importance of pore radius-dependent properties such as solvent viscosity and dielectric constant is increased by the introduction of a pore size distribution in calculations. However, the effect of porewise variation in viscosity is less apparent when considered at a defined applied pressure rather than at a defined flux, showing a further advantage of basing theoretical analysis of nanofiltration in terms of applied pressure.Truncated pore size distributions gave better agreement than full distributions with experimental rejection data for a Desal-DK nanofiltration membrane. Such truncation is in agreement with the findings of atomic force microscopy (AFM). Analysis of uncharged solute rejection data alone could not give useful information about membrane pore size distribution. Neither could such a distribution be obtained quantitatively directly from AFM images. However, use of the shape of the distribution obtained by AFM in conjunction with experimental rejection data for an uncharged solute allows calculation of corrected distributions. Importantly, incorporation of such a corrected pore size distribution in calculations of NaCl rejection gave better agreement with experimental data, compared to calculations assuming uniform pores, at high pressure, the region of industrial interest.     

Kinetically controlled siloxane ring-opening polymerization

Some basic aspects of the kinetics and mechanisms of anionic and cationic ring-opening polymerization of cyclic siloxanes are discussed in connection with their use in polymer synthesis. The emphasis is put on the polymerization of strained ring monomers, such as cyclic trisiloxanes, since these provide the possibility of tailoring the polymer structure. Much attention is devoted to association phenomena and to oligomer formation processes.This review is from the Second International Topical Workshop, Advances in Silicon-Based Polymer Science.     

Evolution-in-materio: solving computational problems using carbon nanotube–polymer composites

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.     

Graph rigidity, cyclic belief propagation, and point pattern matching

A recent paper \cite{CaeCaeSchBar06} proposed a provably optimal, polynomial time method for performing near-isometric point pattern matching by means of exact probabilistic inference in a chordal graphical model. Its fundamental result is that the chordal graph in question is shown to be \emph{globally rigid}, implying that exact inference provides the \emph{same} matching solution as exact inference in a complete graphical model. This implies that the algorithm is optimal when there is no noise in the point patterns. In this paper, we present a new graph which is also globally rigid but has an advantage over the graph proposed in \cite{CaeCaeSchBar06}: its maximal clique size is smaller, rendering inference significantly more efficient. However, this graph is not chordal and thus standard Junction Tree algorithms cannot be directly applied. Nevertheless, we show that loopy belief propagation in such a graph converges to the optimal solution. This allows us to retain the optimality guarantee in the noiseless case, while substantially reducing both memory requirements and processing time. Our experimental results show that the accuracy of the proposed solution is indistinguishable from that of \cite{CaeCaeSchBar06} when there is noise in the point patterns.     

Parallel evolution using multi-chromosome cartesian genetic programming

Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes, where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution (MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.     

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.