A text-based user interface scheme for low-tier embedded systems: an object-oriented approach

Cluster Computing - Nowadays, touchable displays are widely used in most embedded systems, including mobile phones and other hand-held devices. Although interactive user interfaces are intuitively...     

Dynamical modeling of drug effect using hybrid systems

Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results.     

Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid deep learning

The accurate identification of plant species is crucial for the conservation of biodiversity. However, traditional methods for identifying plant species are often complicated, time-consuming, and prone to errors. Therefore, it is essential to address these challenges and develop automated identification methods to enhance the efficiency and accuracy of plant species identification. In this study, a step-by-step method was utilized to identify and classify plant species. The dataset was first loaded, and then preprocessing was performed to remove noisy data. Following that, data augmentation was carried out to improve model accuracy. The deep convolutional neural network (CNN) and visual geometry group-16 (VGG-16) were then employed to extract only the relevant features, owing to their efficient learning capabilities. Feature-level fusion was accomplished by utilizing dimensionality reduction, and enhanced Spearman's principal component analysis (ESPCA) was employed to address the overfitting problem, eliminate redundant data, and reduce storage space and training time requirements. For classification, the hyperparameter-tuned batch-updated stochastic gradient descent (HP-BSGD) method was utilized. The Flavia and Swedish datasets were utilized in the experiments. The proposed hybrid classifier yielded excellent results due to its high convergence speed, good computational effectiveness, and high flexibility. To validate the experimental results, performance and comparative analyses were carried out using standard metrics. The analytical results demonstrated the superior efficiency and suitability of the proposed method in the classification of plant species over existing methods. The hybrid method achieved approximately 97% and 98.85% accuracy in the Flavia and Swedish datasets, respectively, when considering combined features. The performance of the proposed method was further enhanced by considering leaves at different stages, such as seedlings, tiny, mature, and dried leaves.     

Analysis of antimicrobial peptide interactions with hybrid bilayer membrane systems using surface plasmon resonance

The lipid binding behaviour of the antimicrobial peptides magainin 1, melittin and the C-terminally truncated analogue of melittin (21Q) was studied with a hybrid bilayer membrane system using surface plasmon resonance. In particular, the hydrophobic association chip was used which is composed of long chain alkanethiol molecules upon which liposomes adsorb spontaneously to create a hybrid bilayer membrane surface. Multiple sets of sensorgrams with different peptide concentrations were generated. Linearisation analysis and curve fitting using numerical integration analysis were performed to derive estimates for the association (k(a)) and dissociation (k(d)) rate constants. The results demonstrated that magainin 1 preferentially interacted with negatively charged dimyristoyl-L-alpha-phosphatidyl-DL-glycerol (DMPG), while melittin interacted with both zwitterionic dimyristoyl-L-alpha-phosphatidylcholine and anionic DMPG. In contrast, the C-terminally truncated melittin analogue, 21Q, exhibited lower binding affinity for both lipids, showing that the positively charged C-terminus of melittin greatly influences its membrane binding properties. Furthermore the results also demonstrated that these antimicrobial peptides bind to the lipids initially via electrostatic interactions which then enhances the subsequent hydrophobic binding. The biosensor results were correlated with the conformation of the peptides determined by circular dichroism analysis, which indicated that high alpha-helicity was associated with high binding affinity. Overall, the results demonstrated that biosensor technology provides a new experimental approach to the study of peptide-membrane interactions through the rapid determination of the binding affinity of bioactive peptides for phospholipids.     

Benchmarking yeast two‐hybrid systems using the interactions of bacterial motility proteins

Yeast two‐hybrid screens often produce vastly non‐overlapping interaction data when the screens are conducted in different laboratories, or use different vectors, strains, or reporter genes. Here we investigate the underlying reasons for such inconsistencies and compare the effect of seven different vectors and their yeast two‐hybrid interactions. Genome‐wide array screens with 49 motility‐related baits from Treponema pallidum yielded 77 and 165 interactions with bait vectors pLP‐GBKT7 and pAS1‐LP, respectively, including 21 overlapping interactions. In addition, 90 motility‐related proteins from Escherichia coli were tested in all pairwise combinations and yielded 140 interactions when tested with pGBKT7g/pGADT7g vectors but only 47 when tested with pDEST32/pDEST22. We discuss the factors that determine these effects, including copy number, the nature of the fusion protein, and species‐specific differences that explain non‐conserved interactions among species. The pDEST22/pDEST32 vectors produce a higher fraction of interactions that are conserved and that are biologically relevant when compared with the pGBKT7/pGADT7‐related vectors, but the latter appear to be more sensitive and thus detect more interactions overall.     

Modelling and optimization of a recombinant BHK-21 cultivation process using hybrid grey-box systems

In this work a model-based optimization study of fed-batch BHK-21 cultures expressing the human fusion glycoprotein IgG1-IL2 was performed. It was concluded that due to the complexity of the BHK metabolism it is rather difficult to develop a kinetic model with sufficient accuracy for optimization studies. Many kinetic expressions and a large number of parameters are involved resulting in a complex identification problem. For this reason, an alternative more cost-effective methodology based on hybrid grey-box models was adopted. Several model structures combining the a priori reliable first principles knowledge with black-box models were investigated using data from batch and fed-batch experiments. It has been reported in previous studies that the BHK metabolism exhibits modulation particularities when compared to other mammalian cell lines. It was concluded that these mechanisms were effectively captured by the hybrid model, this being of crucial importance for the successful optimization of the process operation. A method was proposed to monitor the risk of hybrid model unreliability and to constraint the optimization results to acceptable risk levels. From the optimization study it was concluded that the process productivity may be considerably increased if the glutamine and glucose concentrations are maintained at low levels during the growth phase and then glutamine feeding is increased.     

Simulink-based HW/SW codesign of embedded neuro-fuzzy systems

We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures.     

Computing the direction of heading from affine image flow

Observers moving through a three-dimensional environment can use optic flow to determine their direction of heading. Existing heading algorithms use cartesian flow fields in which image flow is the displacement of image features over time. I explore a heading algorithm that uses affine flow instead. The affine flow at an image feature is its displacement modulo an affine transformation defined by its neighborhood. Modeling the observer's instantaneous motion by a translation and a rotation about an axis through its eye, affine flow is tangent to the translational field lines on the observer's viewing sphere. These field lines form a radial flow field whose center is the direction of heading. The affine flow heading algorithm has characteristics that can be used to determine whether the human visual system relies on it. The algorithm is immune to observer rotation and arbitrary affine transformations of its input images; its accuracy improves with increasing variation in environmental depth; and it cannot recover heading in an environment consisting of a single plane because affine flow vanishes in this case. Translational field lines can also be approximated through differential cartesian motion. I compare the performance of heading algorithms based on affine flow, differential cartesian flow, and least-squares search.     

Computational modeling of the fluid flow in type B aortic dissection using a modified finite element embedded formulation

Biomechanics and Modeling in Mechanobiology - This work explores the use of an embedded computational fluid dynamics method to study the type B aortic dissection. The use of the proposed technique...     

Asymmetrical gene flow in a hybrid zone of Hawaiian Schiedea (Caryophyllaceae) species with contrasting mating systems

Asymmetrical gene flow, which has frequently been documented in naturally occurring hybrid zones, can result from various genetic and demographic factors. Understanding these factors is important for determining the ecological conditions that permitted hybridization and the evolutionary potential inherent in hybrids. Here, we characterized morphological, nuclear, and chloroplast variation in a putative hybrid zone between Schiedea menziesii and S. salicaria, endemic Hawaiian species with contrasting breeding systems. Schiedea menziesii is hermaphroditic with moderate selfing; S. salicaria is gynodioecious and wind-pollinated, with partially selfing hermaphrodites and largely outcrossed females. We tested three hypotheses: 1) putative hybrids were derived from natural crosses between S. menziesii and S. salicaria, 2) gene flow via pollen is unidirectional from S. salicaria to S. menziesii and 3) in the hybrid zone, traits associated with wind pollination would be favored as a result of pollen-swamping by S. salicaria. Schiedea menziesii and S. salicaria have distinct morphologies and chloroplast genomes but are less differentiated at the nuclear loci. Hybrids are most similar to S. menziesii at chloroplast loci, exhibit nuclear allele frequencies in common with both parental species, and resemble S. salicaria in pollen production and pollen size, traits important to wind pollination. Additionally, unlike S. menziesii, the hybrid zone contains many females, suggesting that the nuclear gene responsible for male sterility in S. salicaria has been transferred to hybrid plants. Continued selection of nuclear genes in the hybrid zone may result in a population that resembles S. salicaria, but retains chloroplast lineage(s) of S. menziesii.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Detection of organics using porphyrin embedded nanoporous organosilicas

Molecularly imprinted polymers and silica have been studied as receptor binding site mimics for use in a wide range of separation, catalysis, and detection applications employing transduction mechanisms including conductometric, amperometric, and capacitance. Porphyrins are also well known as sensor components due to the extreme sensitivity of their spectrophotometric characteristics to changes in their immediate environment. We have developed periodic mesoporous organosilicas (PMO) which incorporate a porphyrin into the material for use as an optical indicator of target binding. This material combines the stability, selectivity, and high density of binding sites characteristic of the molecularly imprinted PMO with the sensitivity and selectivity of the porphyrin. We demonstrate binding of p-nitrophenol, p-cresol, 2,4,6-trinitrotoluene, and RDX by the porphyrin-embedded PMOs with selective adsorption of TNT over the other analytes. In addition, the binding of each of the organics by the PMO results in unique changes in the spectrophotometric characteristics of the incorporated porphyrin. These changes can be observed by visual inspection or through the use of fluorescence spectra collected in 96-well format.     

Thermal detection of embedded tumors using infrared imaging

Breast cancer is the most common cancer among women. Thermography, also known as thermal or infrared imaging, is a procedure to determine if an abnormality is present in the breast tissue temperature distribution. This abnormality in temperature distribution might indicate the presence of an embedded tumor. Although thermography is currently used to indicate the presence of an abnormality, there are no standard procedures to interpret these and determine the location of an embedded tumor. This research is a first step towards this direction. It explores the relationship between the characteristics (location and power) of an embedded heat source and the resulting temperature distribution on the surface. Experiments were conducted using a resistance heater that was embedded in agar in order to simulate the heat produced by a tumor in the biological tissue. The resulting temperature distribution on the surface was imaged using an infrared camera. In order to estimate the location and heat generation rate of the source from these temperature distributions, a genetic algorithm was used as the estimation method. The genetic algorithm utilizes a finite difference scheme for the direct solution of the Pennes bioheat equation. It was determined that a genetic algorithm based approach is well suited for the estimation problem since both the depth and the heat generation rate of the heat source were accurately predicted.     

Thermophilic treatment of bulk drug pharmaceutical industrial wastewaters by using hybrid up flow anaerobic sludge blanket reactor

The hybrid up flow anaerobic sludge blanket reactor was evaluated for efficacy in reduction of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) of bulk drug pharmaceutical wastewater under different operational conditions. The start-up of the reactor feed came entirely with glucose, applied at an organic loading rate (OLR) 1 kg COD/m³ d. Then the reactor was studied at different OLRs ranging from 2 to 11 kg COD/m³ d with pharmaceutical wastewater. The optimum OLR was found to be 9 kg COD/m³ d, where we found 65–75% COD and 80–94% of BOD reduction with biogas production containing 60–70% of methane and specific methanogenic activity was 320 ml CH₄/g-VSS d. By the characterization studies of effluent using GC–MS, the hazardous compounds like phenol, l,2-methoxy phenol, 2,4,6-trichloro phenol, dibutyl phthalate, 1-bromo naphthalene, carbamazepine and antipyrine were present. After the treatment, these compounds degraded almost completely except carbamazepine. Thermophilic methanothrix and methanosaetae like bacteria are present in the granular sludge.     

Patterns of introgression across an expanding hybrid zone: analysing historical patterns of gene flow using nonequilibrium approaches

Previous studies suggest that the hybrid zone between two taxa in the Piriqueta caroliniana complex in central Florida (south-eastern North America) has recently expanded with hybrids replacing parental genotypes across a broad region of the Florida peninsula. Here I use patterns of genetic disequilibria and levels of differentiation among populations to infer historical patterns of introgression across this broad hybrid zone. There were strong positive associations among taxon-specific alleles at the southern extent of hybridization, but disequilibria values were close to zero across the central and northern portions of the hybrid zone. Levels of among-population differentiation in the central portion of the hybrid zone were relatively low, and increased towards the northern extent of hybridization. The high levels of disequilibria are coincident with the sharp clines at the southern end of the hybrid zone, suggesting that there is a tension zone in this region that is maintained by selection against hybrids and dispersal from parental regions. The levels of disequilibria within populations and patterns of differentiation among populations are consistent with historical introgression and northward expansion of this hybrid zone, which may have slowed or ceased in recent generations.     

Multilocus sequence typing scheme for bacteria of the Bacillus cereus group

In this study we developed a multilocus sequence typing (MLST) scheme for bacteria of the Bacillus cereus group. This group, which includes the species B. cereus, B. thuringiensis, B. weihenstephanensis, and B. anthracis, is known to be genetically very diverse. It is also very important because it comprises pathogenic organisms as well as bacteria with industrial applications. The MLST system was established by using 77 strains having various origins, including humans, animals, food, and soil. A total of 67 of these strains had been analyzed previously by multilocus enzyme electrophoresis, and they were selected to represent the genetic diversity of this group of bacteria. Primers were designed for conserved regions of housekeeping genes, and 330- to 504-bp internal fragments of seven such genes, adk, ccpA, ftsA, glpT, pyrE, recF, and sucC, were sequenced for all strains. The number of alleles at individual loci ranged from 25 to 40, and a total of 53 allelic profiles or sequence types (STs) were distinguished. Analysis of the sequence data showed that the population structure of the B. cereus group is weakly clonal. In particular, all five B. anthracis isolates analyzed had the same ST. The MLST scheme which we developed has a high level of resolution and should be an excellent tool for studying the population structure and epidemiology of the B. cereus group.     

Organic synthesis using polymer-supported reagents,catalysts and scavengers in simple laboratory flow systems

The use of appropriate combinations of polymer-supported reagents, catalysts and/or scavengers is a powerful approach, both for the synthesis of single organic compounds and for parallel syntheses. A further stage of development is the use of such reactants in flow systems. So far, it has been shown that a variety of flow formats afford excellent chemical yields and, where relevant, excellent enantiomeric excesses. The supports have a longer lifetime than in batch systems. 'Flow cascades' promise to be important in future.     

Inferences about information flow and dispersal for spatially extended population systems using time-series data

This work explores an information-theoretic approach to drawing inferences about coupling of spatially extended ecological populations based solely on time-series of abundances. The efficacy of the approach, time-delayed mutual information, was explored using a spatially extended predator-prey model system in which populations at different patches were coupled via diffusive movement. The approach identified the relative magnitude and direction of information flow resulting from animal movement between populations, the change in information flow as a function of distance separating populations, and the diffusive nature of the information flow. In addition, when the diffusive movement was eliminated from the model, mutual information correctly provided no evidence of information flow, even when population synchrony was generated by a common environmental driving function. Thus, for this model system, time-delayed mutual information was useful in discriminating between the Moran effect and animal movement as causes of population synchrony, as well as in characterizing dispersal in terms of direction, relative speed and diffusive nature.     

Recovery modeling of tangential flow systems

The demand for increased formulation concentrations for protein therapeutics puts a significant strain on already existing tangential flow filtration (TFF) systems that were constructed with lower protein concentration targets as part of their design criteria. TFF is commonly used to buffer exchange and concentrate the product to the appropriate drug substance concentration. Analyzing the ability of an existing TFF system to process under conditions outside its original design specifications can be challenging. In this analysis, we present a systematic approach to assess the operational limits of a TFF process with consideration of system performance parameters for changing process targets. In two new engineering diagrams, the recovery efficiency diagram and the operating space plot, all relevant operational constraints and parameters are related to allow rapid process fit evaluation. The engineering assessment of TFF systems presented in this article allows a rational review of system limitations during process fit evaluations of existing TFF systems. It also provides a rational basis for targeted system upgrades and setting system design specifications for the design of new systems if existing systems are found inadequate. Biotechnol. Bioeng. 2012; 109: 3084–3092. © 2012 Wiley Periodicals, Inc.