Attachment of Salmonella strains to a plant cell wall model is modulated by surface characteristics and not by specific carbohydrate interactions

Background: Processing of fresh produce exposes cut surfaces of plant cell walls that then become vulnerable to human foodborne pathogen attachment and contamination, particularly by Salmonella enterica. Plant cell walls are mainly composed of the polysaccharides cellulose, pectin and hemicelluloses (predominantly xyloglucan). Our previous work used bacterial cellulose-based plant cell wall models to study the interaction between Salmonella and the various plant cell wall components. We demonstrated that Salmonella attachment was favoured in the presence of pectin while xyloglucan had no effect on its attachment. Xyloglucan significantly increased the attachment of Salmonella cells to the plant cell wall model only when it was in association with pectin. In this study, we investigate whether the plant cell wall polysaccharides mediate Salmonella attachment to the bacterial cellulose-based plant cell wall models through specific carbohydrate interactions or through the effects of carbohydrates on the physical characteristics of the attachment surface. Results: We found that none of the monosaccharides that make up the plant cell wall polysaccharides specifically inhibit Salmonella attachment to the bacterial cellulose-based plant cell wall models. Confocal laser scanning microscopy showed that Salmonella cells can penetrate and attach within the tightly arranged bacterial cellulose network. Analysis of images obtained from atomic force microscopy revealed that the bacterial cellulose-pectin-xyloglucan composite with 0.3 % (w/v) xyloglucan, previously shown to have the highest number of Salmonella cells attached to it, had significantly thicker cellulose fibrils compared to other composites. Scanning electron microscopy images also showed that the bacterial cellulose and bacterial cellulose-xyloglucan composites were more porous when compared to the other composites containing pectin. Conclusions: Our study found that the attachment of Salmonella cells to cut plant cell walls was not mediated by specific carbohydrate interactions. This suggests that the attachment of Salmonella strains to the plant cell wall models were more dependent on the structural characteristics of the attachment surface. Pectin reduces the porosity and space between cellulose fibrils, which then forms a matrix that is able to retain Salmonella cells within the bacterial cellulose network. When present with pectin, xyloglucan provides a greater surface for Salmonella cells to attach through the thickening of cellulose fibrils

Feedback Motion Prediction for Safe Unicycle Robot Navigation

As a simple and robust mobile robot base, differential drive robots that can be modelled as a kinematic unicycle find significant applications in logistics and service robotics in both industrial and domestic settings. Safe robot navigation around obstacles is an essential skill for such unicycle robots to perform diverse useful tasks in complex cluttered environments, especially around people and other robots. Fast and accurate safety assessment plays a key role in reactive and safe robot motion design. In this paper, as a more accurate and still simple alternative to the standard circular Lyapunov level sets, we introduce novel conic feedback motion prediction methods for bounding the close-loop motion trajectory of the kinematic unicycle robot model under a standard unicycle motion control approach. We present an application of unicycle feedback motion prediction for safe robot navigation around obstacles using reference governors, where the safety of a unicycle robot is continuously monitored based on the predicted future robot motion. We investigate the role of motion prediction on robot behaviour in numerical simulations and conclude that fast and accurate feedback motion prediction is key for fast, reactive, and safe robot navigation around obstacles.Comment: 11 pages, 5 figures, extended version of a paper submitted to a conference publicatio

Bayesian Monitoring of Linear Profiles Using DEWMA Control Structures with Random X

The process structures of manufacturing industry are efficiently modeled using linear profiles. Classical and Bayesian set-ups are two well-appreciated schemes for designing control charts for the monitoring of process structures. Mostly in profiles monitoring the independent variables along with the process parameters are assumed fixed. There are manufacturing processes where these conditions may not hold. The advancement in technology and day-to-day changes in process structures caused the parametric uncertainty along with variability in explanatory variables. This paper considered the case of random X and assumes different conjugate and non-conjugate priors to handle parametric uncertainty using double exponentially weighted moving average (DEWMA) control charts. Three univariate DEWMA charts are designed for the monitoring of Y-intercepts, slopes, and error variances. The average run length criterion has been used to evaluate the proposed and competing charts. The wide spread relative study identifies that the proposed Bayesian DEWMA control charts are better than the competing charts based on early detection of out-of-control profiles, particularly for smaller value shifts. The Bayesian DEWMA charts using conjugate priors are the quickest in all as they take less sample points to show out-of-control profile. A case study has been considered to further justify the superiority of Bayesian DEWMA charts over competing charts. 2013 IEEE.The work of S. A. Abbasi was supported by the Qatar University under Project QUST-1-CAS-2018-41.Scopu

Proton conducting ABA triblock copolymers with sulfonated poly(phenylene sulfide sulfone) midblock obtained via copper-free thiol-click chemistry dagger

A series of charged ABA triblock copolymers having sulfonated poly(phenylene sulfide sulfone) (sPSS) as B-block and polystyrene (PS) as A-block have been successfully synthesized using copper-free thiol-click chemistry. One-pot sequential radical addition-fragmentation chain transfer (RAFT) polymerization followed by functionalization with a perfluorinated chain extender (decafluorobiphenyl, DFBP) is used to prepare the PS blocks which are later cliked to the charged sPSS mid-block, synthetized using nucleophilic aromatic substitution polymerization. The proposed synthetic approach ensures good control over the composition of the resulting ABA block copolymers allowing synthesis of block copolymers with well-defined ion exchange capacity (IEC) and nanomorphology. The superstrong segregation regime (chi N >> 100) of these BCPs generates ordered nanostructures, spanning from spherical to lamellar. All the block copolymers are thermally stable up to 300 degrees C and are robust against swelling and wetting due to the dimensional stabilization of the ionic domains provided by the PS matrix. The relationship between proton conductivity and nanomorphology is investigated by electrochemical impedance spectroscopy (EIS), revealing the significant impact of self-assembly on the transport properties, reaching a maximum ion conductivity of 50 mS cm(-1) at 90 degrees C and 95% RH in the through-plane direction

A Generalization Model and Learning in Hardware

We study two problems in the field of machine learning. First, we propose a novel theoretical framework for understanding learning and generalization which we call the bin model. Using the bin model, a closed form is derived for the generalization error that estimates the out-of-sample performance in terms of the in-sample performance. We address the problem of overfitting, and show that using a simple exhaustive learning algorithm it does not arise. This is independent of the target function, input distribution and learning model, and remains true even with noisy data sets. We apply our analysis to both classification and regression problems and give an example of how it may be used efficiently in practice. Second, we investigate the use of learning and evolution in hardware for digital circuit design. Using the reactive tabu search for discrete optimization, we show that we can learn a multiplier circuit from a set of examples. The learned circuit makes less than 2% error and uses fewer chip resources than the standard digital design. We compare use of a genetic algorithm and the reactive tabu search for fitness optimization and show that the reactive tabu search performs significantly better on a 2-bit adder design problem for a similar execution time

Relationship between tyre cavity noise and road surface characteristics on low-noise pavements

Abstract In this work, a protocol to study Tyre Cavity Noise (TCN) was developed. Using this new method, TCN was measured on 24 different road pavements, together tyre noise emission measured with the Close-Proximity (CPX) method and road texture measurements. The results were used to model the relationship between TCN and road surface parameters. The analysis shows that the Standard Reference Test Tyre's (SRTT) TCN is correlated to megatexture at low frequencies and that the correlation between TCN and outside noise emission is significant for frequencies lower than 1 kHz. The use of sensors placed inside the tyre for monitoring the acoustic performance of road pavements presents several advantages compared to the CPX method, such as a more compact design, lower cost and lower hazards both for the instrumentation and for other vehicles

BDD-Driven First-Order Satisfiability Procedures (Extended Version)

Providing a high degree of automation to discharge proof obligations in (fragments of) first-order logic is a crucial activity in many verification efforts. Unfortunately, this is quite a difficult task. On the one hand, reasoning modulo ubiquitous theories (such as lists, arrays, and Presburger arithmetic) is essential. On the other hand, to effectively incorporate this theory specific reasoning in boolean manipulations requires a substantial work. In this paper, we propose a simple technique to cope with such difficult- ies whose aim is to check the validity of universally quantified formulae with arbitrary boolean structure modulo an equational theory. Our approach combines BDDs with refutation theorem proving. The former allows us to compactly represent the boolean structure of formulae, the latter to effectively mechanize the reasoning in equational theories. We report some experimental results on formulae extracted from software verification efforts which confirm both the flexibility and the viability of our approach

Hybridization between Helicoverpa armigera and Helicoverpa assulta (Lepidoptera: Noctuidae): development and morphological characterization of F1 hybrids

Reciprocal hybridizations between Helicoverpa armigera (Hubner) and Helicoverpa assulta (Guenee) were studied. The cross between females of H. armigera and males of H. assulta yielded only fertile males and sterile individuals lacking an aedeagus, valva or ostium bursae. A total of 492 larvae of the F1 generation were obtained and 374 of these completed larval development and pupated. Only 203 pupae were morphologically normal males, the remaining 171 pupae were malformed. Larvae and pupae that gave rise to morphologically abnormal adults exhibited longer development times. Sterility was not only associated with malformed external sex organs, but also a range of abnormalities of the internal reproductive system: (i) loss of internal reproductive organs, (ii) with one to three copies of an undeveloped bursa copulatrix; or (iii) with one or two undeveloped testes. Normal male hybrid adults showed higher flight activity in comparison with males of both species. In contrast, the cross between females of H. assulta and males of H. armigera yielded morphologically normal offspring (80 males and 83 females). The interaction of the Z-chromosome from H. assulta with autosomes from H. armigera might result in morphological abnormalities found in hybrids and backcrosses, and maternal-zygotic incompatibilities might contribute to sex bias attributed to hybrid inviability