Dynamic input/output linearization using recurrent neural networks

A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.

Recognition of greater diversity of Bacillus species and related bacteria in human faeces

In a study looking at the culturable, aerobic Actinobacteria associated with the human gastrointestinal tract, the vast majority of isolates obtained from dried human faeces belonged to the genus Bacillus and related bacteria. A total of 124 isolates were recovered from the faeces of 10 healthy adult donors. 16S rRNA gene sequence analyses showed the majority belonged to the families Bacillaceae (n = 81) and Paenibacillaceae (n = 3), with Bacillus species isolated from all donors. Isolates tentatively identified as Bacillus clausii (n = 32) and B. licheniformis (n = 28) were recovered most frequently, with the genera Lysinibacillus, Ureibacillus, Oceanobacillus, Ornithinibacillus and Virgibacillus represented in some donors. Phenotypic data confirmed the identities of isolates belonging to well-characterized species. Representatives of the phylum Actinobacteria were recovered in much lower numbers (n = 11). Many of the bacilli exhibited antimicrobial activity against one or more strains of Clostridium difficile, C. perfringens, Listeria monocytogenes and Staphylococcus aureus, with some (n = 12) found to have no detectable cytopathic effect on HEp-2 cells. This study has revealed greater diversity within gut-associated aerobic spore-formers than previous studies, and suggests that bacilli with potential as probiotics could be isolated from the human gut.

Conformational modulation of sequence recognition in synthetic macromolecules

The different triplet sequences in high molecular weight aromatic copolyimides comprising pyromellitimide units ("I") flanked by either ether-ketone ("K") or ether-sulfone residues ("S") show different binding strengths for pyrene-based tweezer-molecules. Such molecules bind primarily to the diimide unit through complementary π-π-stacking and hydrogen bonding. However, as shown by the magnitudes of 1H NMR complexation shifts and tweezer-polymer binding constants, the triplet "SIS" binds tweezer-molecules more strongly than "KIS" which in turn bind such molecules more strongly than "KIK". Computational models for tweezer-polymer binding, together with single-crystal X-ray analyses of tweezer-complexes with macrocyclic ether-imides, reveal that the variations in binding strength between the different triplet sequences arise from the different conformational preferences of aromatic rings at diarylketone and diarylsulfone linkages. These preferences determine whether or not chain-folding and secondary π−π-stacking occurs between the arms of the tweezermolecule and the 4,4'-biphenylene units which flank the central diimide residue.

Using cursor measures to determine appropriate computer input assistance for motion-impaired users

People with motion-impairments can often have difficulty with accurate control of standard pointing devices for computer input. The nature of the difficulties may vary, so to be most effective, methods of assisting cursor control must be suited to each user's needs. The work presented here involves a study of cursor trajectories as a means of assessing the requirements of motion-impaired computer users. A new cursor characteristic is proposed that attempts to capture difficulties with moving the cursor in a smooth trajectory. A study was conducted to see if haptic tunnels could improve performance in "point and click" tasks. Results indicate that the tunnels reduced times to target for those users identified by the new characteristic as having the most difficulty moving in a smooth trajectory. This suggests that cursor characteristics have potential applications in performing assessments of a user's cursor control capabilities which can then be used to determine appropriate methods of assistance.

Improving sequence-based fold recognition by using 3D model quality assessment

Motivation: The ability of a simple method (MODCHECK) to determine the sequence–structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAPs) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detect native-likemodels. Results: We show that compared with the other three methods tested MODCHECK is the most reliable method for consistently performing the best top model selection and for ranking the models. In addition, we show that the choice of model similarity score used to assess a model's similarity to the experimental structure can influence the overall performance of these tools. Although these MQAP methods fail to improve the model selection performance for methods that already incorporate protein three dimension (3D) structural information, an improvement is observed for methods that are purely sequence-based, including the best profile–profile methods. This suggests that even the best sequence-based fold recognition methods can still be improved by taking into account the 3D structural information.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM-based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

What are the baselines for protein fold recognition?

What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?