The distribution of enterotoxin and enterotoxin-like genes in Staphylococcus aureus strains isolated from nasal carriers and food samples

Staphylococcus aureus strains were examined for the presence of 18 enterotoxin (se) and enterotoxin-like (sel) genes by PCR and four classic enterotoxins SEA to SED by reverse passive latex agglutination. We screened three groups of isolates: 53 recovered from food samples collected in years 2004-2005, 18--from food samples collected in the 1960s and 1970s and 30--from nasal carrier samples collected in the years 2000-2002. Eighty percent of all tested strains were se and sel positive, but the frequency of these genes was higher among nasal carrier strains (93%) than among food samples isolates (circa 76%). The enterotoxin genes cluster (egc) was the most prevalent among carrier strains (18/30-60%) and the least--among food strains isolated in the 1960s and 1970s (6/18 strains--33%). sea coding enterotoxin A, which was recognised as the major cause of staphylococcal food poisoning so far, was more often found among carrier strains than among the years 2004-2005 food strains (27% versus 11%), but it was the most frequent se/sel gene among food strains isolated in the 1960s and 1970s (10/18 strains). Moreover in our results certain se and sel genes coexisted, which was in accordance with current knowledge about movable genetic elements carrying those genes. The exception was for only one strain, which harboured the sole selr gene on a possibly new genetic element not yet described. As we found both types of egc, as well as seh (especially food samples strains) occurred alone in a majority of positive strains for each of those genes. The analysis of the results obtained by SET-RPLA method for the productivities of classical enterotoxins A-D and the results obtained by PCR for the presence of sea-sed genes revealed the correlation between each other. Only three of all sea-sed positive strains had silent genes--sed.     

Microbes versus microbes: control of pathogens in the food chain

Foodborne illness continues as a considerable threat to public health. Despite improved hygiene management systems and increased regulation, pathogenic bacteria still contaminate food, causing sporadic cases of illness and disease outbreaks worldwide. For many centuries, microbial antagonism has been used in food processing to improve food safety. An understanding of the mode of action of this microbial antagonism has been gained in recent years and potential applications in food and feed safety are now being explored. This review focuses on the potential opportunities presented, and the limitations, of using microbial antagonism as a biocontrol mechanism to reduce contamination along the food chain; including animal feed as its first link. © 2014 Society of Chemical Industry     

Wild Prunus Fruit Species as a Rich Source of Bioactive Compounds

Sugars, organic acids, carotenoids, tocopherols, chlorophylls, and phenolic compounds were quantified in fruit of 4 wild growing Prunus species (wild cherry, bird cherry, blackthorn, and mahaleb cherry) using HPLC‐DAD‐MSn. In wild Prunus, the major sugars were glucose and fructose, whereas malic and citric acids dominated among organic acids. The most abundant classes of phenolic compounds in the analyzed fruit species were anthocyanins, flavonols, derivatives of cinnamic acids, and flavanols. Two major groups of anthocyanins measured in Prunus fruits were cyanidin‐3‐rutinoside and cyanidin‐3‐glucoside. Flavonols were represented by 19 derivatives of quercetin, 10 derivatives of kaempferol, and 2 derivatives of isorhamnetin. The highest total flavonol content was measured in mahaleb cherry and bird cherry, followed by blackthorn and wild cherry fruit. Total phenolic content varied from 2373 (wild cherry) to 11053 mg GAE per kg (bird cherry) and ferric reducing antioxidant power antioxidant activity from 7.26 to 31.54 mM trolox equivalents per kg fruits.     

Friction on a single MoS2 nanotube

Friction was measured on a single molybdenum disulfide (MoS₂) nanotube and on a single MoS₂ nano-onion for the first time. We used atomic force microscopy (AFM) operating in ultra-high vacuum at room temperature. The average coefficient of friction between the AFM tip and MoS₂ nanotubes was found considerably below the corresponding values obtained from an air-cleaved MoS₂ single crystal or graphite. We revealed a nontrivial dependency of friction on interaction strength between the nanotube and the underlying substrate. Friction on detached or weakly supported nanotubes by the substrate was several times smaller (0.023 ± 0.005) than that on well-supported nanotubes (0.08 ± 0.02). We propose an explanation of a quarter of a century old phenomena of higher friction found for intracrystalline (0.06) than for intercrystalline slip (0.025) in MoS₂. Friction test on a single MoS₂ nano-onion revealed a combined gliding-rolling process.PACS, 62.20, 61.46.Fg, 68.37 Ps     

Output-associative RVM regression for dimensional and continuous emotion prediction

Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spatial dependencies between the output vectors, as well as repeating output patterns and input–output associations, that can provide more robust and accurate predictors when modeled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions, and evaluate the proposed framework by focusing on the case of multiple nonverbal cues, namely facial expressions, shoulder movements and audio cues. We demonstrate the advantages of the proposed OA-RVM regression by performing subject-independent evaluation using the SAL database that constitutes naturalistic conversational interactions. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in terms of accuracy of the prediction (evaluated using the Root Mean Squared Error) and structure of the prediction (evaluated using the correlation coefficient), generating more accurate and robust prediction models.     

Evaluation of the quality of collaboration between the client and the therapist in phobia treatments

A growing number of empirical studies evaluate the influence of Mental Health (MH) technology on the clinical effectiveness, the therapeutic relationship (i.e., therapeutic alliance), and usability issues. However, to the authors’ knowledge, no studies have yet been performed regarding the influence of technology on the therapeutic process in terms of collaboration. This study evaluates the quality of collaboration between the client and therapist in Augmented Reality Exposure Therapy (ARET) context and the traditional, In Vivo Exposure Therapy (IVET) context with the Therapeutic Collaborative Scale (TCS). Twenty participants received an intensive session of cognitive behavioral therapy in either a technology-mediated therapeutic context or in a traditional therapeutic context. The results indicate that both therapeutic conditions show high collaboration scores. However, the asymmetry of roles between the therapist and the client under both conditions were detected. Also, a greater level of distraction was observed for therapists in ARET, which affected the quality of the therapists’ involvement in the therapeutic session. The implications of these results are discussed.     

Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues,databases, and tools

While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human‐centred interaction they are bound to experience. This paper surveys the nonverbal behavioural cues that could be present during displays of agreement and disagreement; discusses a number of methods that could be used or adapted to detect these suggested cues; lists some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, it examines the few existing attempts at agreement and disagreement classification, and finally discusses the challenges in automatically detecting agreement and disagreement.