Template Selection Research Articles

Optimization of polydopamine imprinted polymer for label free sensitive potentiometric determination of proteins: Application to recombinant human erythropoietin sensing in different matrices

The integration of molecular imprinted polymers (MIPs) with ion selective electrodes (ISE) has been an ideal platform for sensing of biological products. In this work, a facile MIP was constructed over the surface of PVC polymeric membrane using self-assembled dopamine (DA) as a monomer around recombinant human Erythropoietin (rhEPO) as a template. A label free passive ion flux MIP potentiometric sensor was fabricated using tetra butyl ammonium bromide as a marker ion. MIP preparation was optimized for critical factors (monomer concentration and polymerization time) that affect the thickness of the imprinted layer by central composite response surface design. The optimum condition for DA-MIP preparation was found to be 3.79 mg mL−1 DA for 18.14 h with desirability of 0.9919. A highly selective and sensitive potentiometric sensor was obtained by combining membrane surface minimization with superficial imprinting process strategy. The sensor has elicited selectivity for template molecule as confirmed by imprinting factor exceeding 11-fold the values obtained from closely related rhEPO analogues. The imprinted layer surface was characterized using scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The quantitative determination of rhEPO was established over the range of 1.00–100.00 ng mL−1 (10-9 to 10-11 M) with a limit of detection of 0.33 ng mL−1. The sensor could classify rhEPO-α products in a similar attitude of natural antibodies. The potentiometric sensor was able to quantitate rhEPO in human serum with a limit of detection of 0.50 ng mL−1 and the bioanalytical assay was successfully validated. The proposed MIP biosensor elicited specificity for template recognition and selectivity in biological fluids without the need of immunoaffinity purification. Such approach shall plan the direction towards a portable sensor for doping disclosure in sports cheating.

Flogging a Dead Salmon? Reduced Dose Posterior to Prostate Correlates With Increased PSA Progression in Voxel-Based Analysis of 3 Randomized Phase 3 Trials

Flogging a Dead Salmon? Reduced Dose Posterior to Prostate Correlates With Increased PSA Progression in Voxel-Based Analysis of 3 Randomized Phase 3 Trials

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling.

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques such as X-ray crystallography, NMR spectroscopy, and cryogenic electron microscopy can resolve protein structures but are costly and time-consuming and do not work for all proteins. Computational protein structure prediction tries to overcome these problems by predicting the structure of a new protein using existing protein structures as a resource. Here we present TopSuite, a web server for protein model quality assessment (TopScore) and template-based protein structure prediction (TopModel). TopScore provides meta-predictions for global and residue-wise model quality estimation using deep neural networks. TopModel predicts protein structures using a top-down consensus approach to aid the template selection and subsequently uses TopScore to refine and assess the predicted structures. The TopSuite Web server is freely available at https://cpclab.uni-duesseldorf.de/topsuite/.

Homology Modeling Using GPCRM Web Service.

Homology modeling methods are commonly used for quick and precise construction of a desired protein or its mutant using protein templates, which were determined by crystallography, cryo-EM, or NMR. Due to the increasing number of such structures, the obtained models are precise even in the case of small similarity between sequences of template and modeled proteins. The reason for that is a high evolutionary conservation in the structure regions responsible for keeping the function of proteins. This is also the case for G protein-coupled receptors (GPCRs), which constitute the largest family of membrane receptors with nearly 800 proteins. The GPCRM web service ( https://gpcrm.biomodellab.eu/ ) was set up for the nearly automatic generation of high-quality structures of modeled GPCRs. The three possible paths: "High similarity," "Quick path," and "Long path" allow the user to choose between a fast but less reliable path, up to more reliable but longer procedures. In the Advanced mode the service allows for user modifications including selection of template(s) and a manual adjustment of the sequence alignment.

Homology Modeling of Class A G-Protein-Coupled Receptors in the Age of the Structure Boom.

With 700 members, G protein-coupled receptors (GPCRs) of the rhodopsin family (class A) form the largest membrane receptor family in humans and are the target of about 30% of presently available pharmaceutical drugs. The recent boom in GPCR structures led to the structural resolution of 57 unique receptors in different states (39 receptors in inactive state only, 2 receptors in active state only and 16 receptors in different activation states). In spite of these tremendous advances, most computational studies on GPCRs, including molecular dynamics simulations, virtual screening and drug design, rely on GPCR models obtained by homology modeling. In this protocol, we detail the different steps of homology modeling with the MODELLER software, from template selection to model evaluation. The present structure boom provides closely related templates for most receptors. If, in these templates, some of the loops are not resolved, in most cases, the numerous available structures enable to find loop templates with similar length for equivalent loops. However, simultaneously, the large number of putative templates leads to model ambiguities that may require additional information based on multiple sequence alignments or molecular dynamics simulations to be resolved. Using the modeling of the human bradykinin receptor B1 as a case study, we show how several templates are managed by MODELLER, and how the choice of template(s) and of template fragments can improve the quality of the models. We also give examples of how additional information and tools help the user to resolve ambiguities in GPCR modeling.

Template-based prediction of protein structure with deep learning

BackgroundAccurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available.ResultsWe propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively.ConclusionsThese results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

Hand size representation in healthy children and young adults

Whereas we experience our body as a coherent volumetric object, the brain appears to maintain highly fragmented representations of individual body parts. Little is known about how body representations of hand size and shape are built and evolve during infancy and young adulthood. This study aimed to investigate the effect of hand side, handedness, and age on the development of central hand size representation. The observational study with comparison groups was conducted with 90 typically developing Belgian school children and young adults (48 male and 42 female; age range = 5.0–23.0 years; 49 left-handed and 41 right-handed). Participants estimated their hand size and shape using two different tasks. In the localization task, participants were verbally cued to judge the locations of 10 anatomical landmarks of an occluded hand. An implicit hand size map was constructed and compared with actual hand dimensions. In the template selection task, the explicit hand shape was measured with a depictive method. Hand shape indexes were calculated and compared for the actual, implicit, and explicit conditions. Participants were divided into four age groups (5–8 years, 9–10 years, 11–16 years, and 17–23 years). Implicit hand maps featured underestimation of finger length and overestimation of hand width, which is already present in the youngest children. Linear mixed modeling revealed no influence of hand side on finger length underestimation; nonetheless, a significant main effect of age (p = .001) was exposed. Sinistrals aged 11 to 16 years showed significantly less underestimation (p = .03) than dextrals of the same age. As for the hand shape, the implicit condition differed significantly with the actual and explicit conditions (p < .001). Again, the implicit shape index was subjected to handedness and age effects, with significant differences being found between sinistrals and dextrals in the age groups of 9 and 10 years (p = .029) and 11 to 16 years (p < .001). In conclusion, the implicit metric component of the hand representation in children and young adults is misperceived, featuring shortened fingers and broadened hands since a very young age. Crucially, the finger length underestimation increases with age and shows a different developmental trajectory for sinistrals and dextrals. In contrast, the explicit hand shape is approximately veridical and seems immune from age and handedness effects. This study confirms the dual character of somatoperception and establishes a point of reference for children and young adults.

Carbon quantum dots embedded silica molecular imprinted polymer as a novel and sensitive fluorescent nanoprobe for reproducible enantioselective quantification of naproxen enantiomers

The nanoparticles of carbon quantum dots embedded silica molecular imprinted polymer (CQDs-SiO2@MIP) were utilized as novel, sensitive, repeatable, and reproducible enantioselective fluorescent nanoprobes for enantioselective quantification of naproxen molecules. The combination of MIP and CQDs was the basis for selectivity enhancement in chiral recognition of naproxen enantiomers due to template selectivity of the synthesized CQDs-SiO2@MIP. Introducing naproxen molecules to the system, selectively induced a considerable quenching of the fluorescence of the CQDs-SiO2@MIP with an efficient distinction between S- and R-enantiomers. The method showed a wide linear working range of 0.5–35.0 µM for S-naproxen and 0.5–30.0 µM for R-naproxen. However, the calibration sensitivity for S-naproxen was estimated at 2.0-fold higher than R-naproxen. Also, the detection limits were calculated to be 0.20 and 0.37 µM for S- and R-naproxen, respectively. The relative standard deviations (RSD %) as an index for repeatability and reproducibility of the developed method were estimated to be 1.4% and 4.0% for intra-day and inter-day assays, respectively.

Bio-Template Synthesis of Strontium Titanate and Application for Heavy Metal Adsorption in Wastewater

Heavy metal ions, such as chromium, cadmium, mercury, nickel, copper, and zinc, cause severe pollution to water bodies as well as enormous harm to people and the biosphere. Adsorption and removal of heavy metal pollution in water is a low-cost and straightforward method. Researchers are currently looking for materials that are cheap and readily available, and have high adsorption and enrichment properties for heavy metal ions. Strontium titanate is a new type of ceramic material, and there have been few related research reports. The work reported a new method for the synthesis of strontium titanate adsorbent by the sol-gel biotemplate method. A sol-gel method, using different types of straws as biological templates, was selected to prepare nano-strontium titanate adsorption materials. In this experiment, chelate formed using tetra-n-butyl titanate and strontium nitrate as raw materials, and acetic acid as a chelating agent, thereby forming a sol. Then, corn straw and sunflower straw immersed in the sol and aged for a period. After drying and calcination, a strontium titanate adsorbent with a biofilm microstructure was obtained. Besides, the structure of strontium titanate synthesized was characterized by SEM and XRD. In this experiment, zinc ions were used as adsorbed ions to investigate the adsorption capacity of the synthesized strontium titanate adsorbents for heavy metal ions in water. Studies have shown that strontium titanate synthesized by the biological template method was a porous material retaining some of the natural tissue structure characteristics of the biological template. Compared with the strontium titanate obtained without using the biological template, the adsorption capacity of the strontium titanate powder synthesized by the biological template method to zinc ion was significantly improved. We studied the influence of the selection and adsorption of biological templates and pH conditions on the ability of strontium titanate to adsorb zinc ions.

Homology Modelling and in silico Characterization of Laccase from Lentinula edodes

Introduction: Laccases are phenol oxidases which belong to the superfamily of multicopper oxidases. Laccases are found in almost all wood-rotting fungi. There is evidence that laccases can play an important role in lignin degradation, fruiting body formation, pigment formation during asexual development, competitor interactions and pathogenesis. Laccase from Lentinula edodes is used in variety of applications like to reduce toxicity, partial decolourization of effluent water and decolourization of chemically different dyes like Remazole brilliant blue R, bromophenol blue, methyl red and naphtol blue black. The objectives of this study include prediction of three dimensional (3D) structure of laccase from Lentinula edodes using homology modelling, in silico characterization and analysis of laccase from this organism using computational methods. Methods: The sequence of laccase from Lentinula edodes was retrieved from UniProt database and sequence analysis was carried out using BLAST for the selection of template. The protein 3D structure was modelled using ModWeb server. The obtained 3D model of the laccase from Lentinula edodes was visualized and analyzed using RasMol. The quality of the 3D structure of protein was verified by its energy and stereochemical properties. The erred regions were remodelled by loop modelling using SWISS PDB viewer. Further, the in sillico characterization of the laccase from Lentinula edodes was computed. Results and Discussion: The 3KW7 A of Trametes Sp AH28-2 is used as template for model building of laccase from L. edodes. The atom model obtained in PDB format showed unstable region in the model. These unstable regions were selected and remodelled by loop modelling. The remodelled structure was further evaluated by its stereochemical quality and energy. The quality of the remodelled structure was found to be improved. Conclusion: Evaluated 3D structure of laccase from L. edodes shows that predicted model is of good quality because maximum residues are present in favoured region of Ramachandran plot, which indicates that the stereochemical quality of predicted 3D structure is reasonably good. It suggests that this model can be used to understand molecular interaction of this laccase with the other proteins.

A memory network based end-to-end personalized task-oriented dialogue generation

Building a personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved in the template selection responses. However, preparing a massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on memory networks for response generation in a personalized task-oriented dialogue system. Our model consists of three parts: a retrieval module, a memory encoder network and a memory decoder network. Retrieval module employs the user utterances and user attributes to collect relevant responses from other users. Memory encoder is trained with textual features to obtain dialogue representation. Memory decoder is composed of an RNN and a rule-memory network for response generation. Experiments on the benchmark dataset show that our model achieves better performance than strong baselines in personalized task-oriented dialogue generation.

Crystallization profile and morphological study of SAPO-5 templated by imidazolium cations of different functional groups

1-Propyl-2,3-dimethyl-1H-imidazole-3-ium ([pmIm] + ), 3-(2,3-dihydroxypropyl)-1,2-dimethyl-1H-imidazole-3-ium ([dhmIm] + ) and 1-benzyl-2,3-dimethyl-1H-imidazole-3-ium ([bzmIm] + ) cations were used as organic templates for crystallizing SAPO-5 with AFI microporous framework. The physico-chemical influences of the functional groups of the templates (e.g. polarity/hydrophilicity, molecular dimension and size) on SAPO-5 formation were investigated. The results revealed that the crystallization rate, crystallinity and solid yield could be enhanced by increasing the polarity/hydrophilicity of the templates. In addition, the isomorphous Si substitution, morphology and particle size of the resulted SAPO-5 were also affected by the substituted groups of the imidazolium templates. The thermogravimetry results further supported that the selection of organic template with proper polarity/hydrophilicity could be crucial to obtain SAPO-5 with high porosity (S BET , S mic , S ext and V total ) as high polar/hydrophilic template exhibited stronger chemical interaction with the AFI framework during template removal process via calcination process. • SAPO-5 is synthesized using imidazolium-based templates. • Template's functional group affects crystallization and morphological profiles. • Proper hydrophobicity is essential to control host-guest chemical interactions. • Polar template interacts strongly with SAPO-5, hence high temperature calcination is needed.

Benchmarking GPCR homology model templateselection in combination with de novo loop generation.

G protein-coupled receptors (GPCR) comprise the largest family of membrane proteins and are of considerable interest as targets for drug development. However, many GPCR structures remain unsolved. To address the structural ambiguity of these receptors, computational tools such as homology modeling and loop modeling are often employed to generate predictive receptor structures. Here we combined both methods to benchmark a protocol incorporating homology modeling based on a locally selected template and extracellular loop modeling that additionally evaluates the presence of template ligands during these modeling steps. Ligands were also docked using three docking methods and two pose selection methods to elucidate an optimal ligand pose selection method. Results suggest that local template-based homology models followed by loop modeling produce more accurate and predictive receptor models than models produced without loop modeling, with decreases in average receptor and ligand RMSD of 0.54Å and 2.91Å, respectively. Ligand docking results showcased the ability of MOE induced fit docking to produce ligand poses with atom root-mean-square deviation (RMSD) values at least 0.20Å lower (on average) than the other two methods benchmarked in this study. In addition, pose selection methods (software-based scoring, ligand complementation) selected lower RMSD poses with MOE induced fit docking than either of the other methods (averaging at least 1.57Å lower), indicating that MOE induced fit docking is most suited for docking into GPCR homology models in our hands. In addition, target receptor models produced with a template ligand present throughout the modeling process most often produced target ligand poses with RMSD values ≤ 4.5Å and Tanimoto coefficients > 0.6 after selection based on ligand complementation than target receptor models produced in the absence of template ligands. Overall, the findings produced by this study support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions.

Synthesis and characterization of calcium carbonate on three kinds of microbial cells templates

As is known, the formation of nanocrystals is influenced by the cells with which they interact. In particular, the same materials could exhibit different properties depending on the type of cells. In this work, we used three different types of microbial cells (Escherichia coli, Staphylococcus aureus, and Beer Yeast) as biological templates to control the crystallization of calcium carbonate particles before or after coated with several layers of polyelectrolyte films via Layer-by-Layer assembly and biomineralization process. The results showed that the resulting crystal types using different naked cells as templates all include calcite and vaterite, and the ratio of vaterite is higher. After encapsulating with polyelectrolyte, polyelectrolyte did not change the ratio of calcite to vaterite on the surface of E. coli and Staphylococcus aureus but yeast. Furthermore, it can weaken the intensity of vaterite and increase the proportion of calcite using the yeast as template. Differences in calcium carbonate crystal forms are related to the differences in cell wall components and cell secretion. So we mineralized calcium carbonate with the main components of the yeast cell wall (mannose oligosaccharide, β-glucan) and the Gram bacterial wall (peptidoglycan, lipopolysaccharide) directly, it was found that the composition of the cell wall did affect the crystal form and morphology of the calcium carbonate. This provides a reference for further study of the biomineralization mechanism and the selection of mineralization templates. Furthermore, this study provides some supplementary for understanding the natural mineralization process of CaCO3.

Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques.

The presence of organic structure directing agents (templates) in the synthesis of zeolites allows the synthesis to be directed, in many cases, toward structures in which there is a large stabilization between the template and the zeolite micropore due to dispersion interactions. Although other factors are also important (temperature, pH, Si/Al ratio, etc.), systems with strong zeolite-template interactions are good candidates for an application of new computational algorithms, for instance those based in molecular topology (MT), that can be used in combination with large databases of organic molecules. Computational design of new templates allows the synthesis of existing and new zeolites to be expanded and refined. Three zeolites with similar 3-D large pore systems, BEA, BEC, and ISV, were selected with the aim of finding new templates for their selective syntheses. Using a training set of active and inactive templates (obtained from the literature) for the synthesis of target zeolites, it was possible to select chemical descriptors related to activity, meaning a good candidate template. With a discriminant function defined upon MT, the screening through a database of organic molecules led to a small subset (preselection) of candidate templates for the synthesis of BEA, BEC, and ISV. As far as we know, this is the first time that topological/topochemical descriptors, which do not consider 3-D information on the molecules, have been used to predict the activity of zeolite structure directing agents (SDAs). Following the prediction of SDAs using MT, an automated approach of sequential template filling of micropores based on a combination of Monte Carlo and lattice energy minimization was applied for all the candidate templates in the three zeolites. Two results can be obtained from this: an evaluation of the quality of the molecular topology QSAR models leading to the preselection of templates, and a final selection of candidate templates for the selective synthesis of BEA, BEC, and ISV. Regarding the latter, a good template will be that which maximizes the zeolite-template dispersion interactions with one, and only one, of the three zeolites. The presented methodology can be used to find alternative (maybe cheaper or perhaps more selective) templates than those already known.

Optimal spectral templates for triggered feedback experiments.

In the field of songbird neuroscience, researchers have used playback of aversive noise bursts to drive changes in song behavior for specific syllables within a bird's song. Typically, a short (~5-10 msec) slice of the syllable is selected for targeting and the average spectrum of the slice is used as a template. Sounds that are sufficiently close to the template are considered a match. If other syllables have portions that are spectrally similar to the target, false positive errors will weaken the operant contingency. We present a gradient descent method for template optimization that increases the separation in distance between target and distractors slices, greatly improving targeting accuracy. Applied to songs from five adult Bengalese finches, the fractional reduction in errors for sub-syllabic slices was 51.54±22.92%. At the level of song syllables, we use an error metric that controls for the vastly greater number of distractors vs. target syllables. Setting 5% average error (misses + false positives) as a minimal performance criterion, the number of targetable syllables increased from 3 to 16 out of 61 syllables. At 10% error, targetable syllables increased from 11 to 26. By using simple and robust linear discriminant methods, the algorithm reaches near asymptotic performance when using 10 songs as training data, and the error increases by <2.3% on average when using only a single song for training. Targeting is temporally precise, with average jitter of 3.33 msec for the 16 accurately targeted syllables. Because the algorithm is concerned only with the problem of template selection, it can be used as a simple and robust front end for existing hardware and software implementations for triggered feedback.

Multilayer capsules made of weak polyelectrolytes: a review on the preparation, functionalization and applications in drug delivery.

Multilayer capsules have been of great interest for scientists and medical communities in multidisciplinary fields of research, such as drug delivery, sensing, biomedicine, theranostics and gene therapy. The most essential attributes of a drug delivery system are considered to be multi-functionality and stimuli responsiveness against a range of external and internal stimuli. Apart from the highly explored strong polyelectrolytes, weak polyelectrolytes offer great versatility with a highly controllable architecture, unique stimuli responsiveness and easy tuning of the properties for intracellular delivery of cargo. This review describes the progress in the preparation, functionalization and applications of capsules made of weak polyelectrolytes or their combination with biopolymers. The selection of a sacrificial template for capsule formation, the driving forces involved, the encapsulation of a variety of cargo and release based on different internal and external stimuli have also been addressed. We describe recent perspectives and obstacles of weak polyelectrolyte/biopolymer systems in applications such as therapeutics, biosensing, bioimaging, bioreactors, vaccination, tissue engineering and gene delivery. This review gives an emerging outlook on the advantages and unique responsiveness of weak polyelectrolyte based systems that can enable their widespread use in potential applications.

Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation

Various types of theoretical algorithms have been proposed for 6D pose estimation, e.g., the point pair method, template matching method, Hough forest method, and deep learning method. However, they are still far from the performance of our natural biological systems, which can undertake 6D pose estimation of multi-objects efficiently, especially with severe occlusion. With the inspiration of the Müller-Lyer illusion in the biological visual system, in this paper, we propose a cognitive template-clustering improved LineMod (CT-LineMod) model. The model uses a 7D cognitive feature vector to replace standard 3D spatial points in the clustering procedure of Patch-LineMod, in which the cognitive distance of different 3D spatial points will be further influenced by the additional 4D information related with direction and magnitude of features in the Müller-Lyer illusion. The 7D vector will be dimensionally reduced into the 3D vector by the gradient-descent method, and then further clustered by K-means to aggregately match templates and automatically eliminate superfluous clusters, which makes the template matching possible on both holistic and part-based scales. The model has been verified on the standard Doumanoglou dataset and demonstrates a state-of-the-art performance, which shows the accuracy and efficiency of the proposed model on cognitive feature distance measurement and template selection on multiple pose estimation under severe occlusion. The powerful feature representation in the biological visual system also includes characteristics of the Müller-Lyer illusion, which, to some extent, will provide guidance towards a biologically plausible algorithm for efficient 6D pose estimation under severe occlusion.

Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device

BackgroundUser interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device.MethodsThe evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method.ResultsPerformance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes.ConclusionsThe performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.