Development of in vitro infection model methods for Gram-negative bacteria to assess potential broad-spectrum anti-virulence inhibitors

Bacteria exhibit common virulence targets which can be inhibited for potential broad-spectrum activity across a range of bacterial infections. The macrophage infectivity potentiator (Mip) is a bacterial protein that has peptidylprolyl cis/trans isomerase activity and is part of the FK506-binding protein subgroup, in the superfamily of immunophilins. They have previously been identified to be involved in intracellular virulence of Legionella pneumophila and Burkholderia pseudomallei and inhibition of the protein with pipecolic acid derived small-molecule inhibitors resulted in a decrease in virulence. Due to the highly conserved nature of Mip proteins it is believed that these proteins exist in Klebsiella pneumoniae and Burkholderia cenocepacia; two pathogens contributing to a rise in multi-drug resistant infections. Therefore, this study aims to examine the potential broad-spectrum activity of the Mip inhibitors in K. pneumoniae and B. cenocepacia by developing in vitro model methods for inhibitor evaluation. The presence of putative Mip proteins in K. pneumoniae and B. cenocepacia were confirmed with the use of online bioinformatics tools. The Mip inhibitors were then examined by first developing in vitro cell based methods to identify the magnitude at which K. pneumoniae and B. cenocepacia adhered to macrophages, and internalised, survived and replicated within the macrophages. An optimised adherence assay, and internalisation, survival and replication assay were then used to test the efficacy of the Mip inhibitors in K. pneumoniae and B. cenocepacia. The results obtained demonstrated that K. pneumoniae and B. cenocepacia were able to adhere to the macrophages, however, the concentration at which they adhered varied between strains. Only two strains of K. pneumoniae were internalised into the macrophages and only one strain was able to replicate at low concentrations within the macrophages during the 24 hours post-infection. All strains of B. cenocepacia were internalised into the macrophages, however, only one strain suggested replication within the macrophages during the 24 hours post-infection. The Mip inhibitors had little effect on adherence of K. pneumoniae to macrophages, however, trends in the data suggested that the Mip inhibitors had an effect on the intracellular virulence of K. pneumoniae and B. cenocepacia. Therefore from this preliminary study, it seems that Mip inhibitors have potential broad-spectrum activity in intracellular virulence of these pathogens. These findings provide insight into Mip proteins in K. pneumoniae and B. cenocepacia and the potential they have as common bacterial drug targets

Multi-scale modeling of particle-laden flows

Particle-laden flow occur in a wide range of engineering applications such as combustors, gasifiers, fluidized beds and pollution control systems. Particle-flow interactions are complex, especially in turbulent and confined flows. A proper understanding of these interactions is critical in designing devices with better performance characteristics. In this work, particle-laden flows in channels are numerically investigated with the lattice-Boltzmann method (LBM). A three-dimensional parallelized lattice-Boltzmann method code is developed to carry out these studies. The code resolves the particle surface and the boundary layer surrounding it to gain fundamental insights into particle-flow interactions. The lattice-Boltzmann method is assessed for its accuracy in solving several standard single-phase and multi-phase, laminar and turbulent flows. Direct numerical simulations (DNS) of particle-laden channel flows are then performed. When the particle diameter is smaller than the Kolmogorov length scale, direct numerical simulations (DNS) with the point-particle approximation show that the Stokes number, St, mass loading of particles, i.e. ratio of mass of dispersed to carried phase, and particle diameter, are important parameters that determine the distribution of the particles across the channel cross-section and the impact of the particles on the flow field. When the St is infinitesimally small, the particles are uniformly distributed across the cross-section of the channel. As St is increased, the particle concentration near the wall increases. At even higher St, the particle concentration near the wall decreases, but it increases at the center of the channel. These changes in concentration are attributed to turbophoresis which causes preferential movement of the particles. The impact of the turbophoretic force is affected by St and particle diameter. The parameters that influence the mean flow field of the carrier phase is primarily the mass loading. To further improve the understanding of the physics of the flow, particle-resolved direct numerical simulations (PR-DNS) are carried out. Particle motion in a laminar channel flow is initially studied. The trajectory of a single particle is examined. It is shown that the mean equilibrium position of the particle in the channel depends on the St. Particles with low St reach an equilibrium position that lies between the wall and the center of the channel (Segre-Silberberg effect) while those with high St begin to oscillate about the center of the channel as they are transported by the fluid. The particle location and motion are determined by the interplay of three forces acting on the particle in the wall normal direction: the Saffman lift, Magnus lift and wall repulsion. Saffman lift and Magnus lift act to move the particle towards the wall while wall-repulsion opposes this motion. Direct numerical simulations of turbulent flow past stationary particles in a channel are then carried out. These simulations provide information about particle-flow interactions when the particle is near the wall and at the center. Multiple particles fixed in a cross-sectional plane are also considered. The position of the particles in the channel, the particle size, the Reynolds number and the number of particles are varied. The details of the flow field are analyzed to provide insight into the factors that control the distance of influence of the fixed particle on the flow field. With a single particle case, the effect of the particle is felt for about 20 diameters downstream. When multiple particles are present, interaction between the vortices shed by the particles lengthens the distance to about 40 diameters downstream. The results suggest that in a particle-laden flow, if particles are separated by an average distance greater than 40 diameters, particle-fluid-particle interactions can be neglected. At shorter distances, these interactions become important. Next particle-resolved direct numerical simulations (PR-DNS) in a turbulent channel flow are carried out to study the particle motion when the particle diameter is larger than the Kolmogorov length scale. It is shown that in a turbulent channel flow, the dominant forces are the Saffman lift and the turbophoresis. When the particle is larger than the Kolmogorov length scale, turbophoresis can act in a local sense whereby the more intense exchange of momentum of eddies on the side of the particle with higher turbulent kinetic energy relative to the opposite side move the particle toward the lower turbulent kinetic energy region or in a global sense whereby even when the particles do not directly feel the effect of eddies, particles tend to diffuse down gradients of turbulent kinetic energy. The simulations show that particles with relatively lower St move preferentially toward the wall while those with higher St exhibit a relatively uniform concentration. This is consistent with the conclusion from the point-particle simulations. As particle size is increased, the St at which uniform distribution is reached increases. The likely reason is that the effect of local turbophoresis and Saffman lift increases for larger particles and these forces tend to concentrate particles near the wall. Higher St, i.e. higher inertia, is needed to overcome these forces

PREDICTING RATINGS FOR USER REVIEWS AND OPINION MINING ANALYZE FOR PHYSICIANS AND HOSPITALS

ABSTRACTHealth care is taking its turn in the internet now and online health information consumption is also booming. Users have started generating healthcarereports like online doctor reviews open to all. Hence, online health forums are increasingly popular these days since people can gather their requireddata by just sitting at home and select the best doctor by considering the reviews available online. The patients also browse on their concerneddiseases and use the open forum for discussion on the topics. On an average, these online health-care providers are mainly focusing on reviews aboutthe physicians. The feedback provided by patients is considered and we also analyze the sentiments of the patient to estimate the value of the reviews.The rating for the doctors is divided into various categories such as Staff, Knowledge, and Helpfulness. We propose support vector machine and apriorifor the classification of data and use sentiment based rating prediction to analyze doctor's reviews and opinion mining patterns for online patterns.By providing physician ratings in website, it offers the patients to know about the physician and consider the critique and information to make theirdecision.Keywords: Support vector machine, Apriori, Sentiment classification, Opinion mining

Mother Tongue Influence on English Pronunciation: A Case study in College Students

Mother tongue interference can affect students’ English speaking skill especially in pronouncing the words intrinsically. Considering Tamil language is the dominant mother language used by students at College level, the students as EFL learners often use their mother tongue in daily communication and at campus, and College students pronounce English words in a different way. This study investigated the forms of Tamil language interfering students in pronouncing English words, and how they were interfered. This was a descriptive study in the field of English pronunciation elaborated in a qualitative way. Recorded interviews and pronunciation testing were the main techniques of collecting data. Interview guide, validated pronunciation test, and document analysis were the instruments. In conclusion, the interference was positive since Tamil language’s phonemic sounds have some similarities to those of English, so this condition does not potentially change the meanings of English words

A Study on the Necessity of Blended Learning in Classroom after the Pandemic Period

Rescheduling the usual elements of educational materials predominantly helps the learner to seek knowledge much easier than before. Developing e-sources is the next level of advancement in providing support for all learning categories. This study is an attempt to find out the necessity of blended learning in the classroom after the pandemic period .This study was conducted with a quantitative research design using a survey methodology to gather data about the learners’ interest among the blended learning from the sample of 50 undergraduate students in the state of Tamil Nadu, India. The necessity of updating to the present scenario is challenging the teachers to upgrade themselves and support the students. During the pandemic, the teaching and learning process is quite difficult to acquire education, but the integration of technology balances the mutuality. The core objective of this study is to examine the effects of the online mode of education among students and find out the requirements of blended learning for future education

Selection of crop varieties and yield prediction based on phenotype applying deep learning

In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield

A Conscientious Study of Blended Learning and Cardinal Tools

Evaluating the contexts, activities and relationships of participants in education is a complicated structure that necessitates numerous ideologies and levels of assessment, specifically when it comes to technological developments. Blended learning is a method of instruction that integrates offline and virtual-based education. This article highlights the characteristics of technology and features in blended learning, which enhances the learner engagement in higher education. It showcases some of the digital technologies including video encapsulation and online learning systems that may assist in learning and teaching. This paper delineates the consequences faced by the teachers of higher education while explaining the conceptualization of blended learning. It also suggests the implications that can be practiced for the optimization of blended learning evaluating the learner engagement. It also identifies methods to improve the effectiveness of the teachers in evolving demands in blended learning for higher education

Act of Altruism in Nicola Davies’ Whale Boy

Eco-consciousness deals with the concern of human beings towards nature. Nature protects humans in all aspects unless they harm them. This act of selflessness is referred to as “altruism” in literature. Altruism is the literary term describing humanity or kindness. The kind behaviour of nature is characterised as a non-profitable aid to humans. There won't be any expectations, yet kindness will be expressed in a natural way. According to ethology, altruistic behaviour can be found in humans and animals. It is merely distinguished as a kinship between the two different groups. The novel Whale Boy deals with the behavioural attitudes of a whale and a boy. Nicola Davies, an English zoologist and author, described the kind behaviour of a wild creature towards humans. The novel centers on Michael Fontaine, a young boy who wants to go whale-watching and fishing in the deep ocean off the coast of his island. Nicola Davis focuses on marine wildlife and the issue of whaling and conservation. This article relates to the relationship between Michael and the whale. Thus, through the act of altruism, the article attempts to reflect the kinship between whales and humans

Development of a parallel multiphase Lattice-Boltzmann solver to study Stokes number effects on particle trajectories

Particle-laden flows are important owing to their relevance to many engineering devices such as coal combustors, gasifiers and solar thermochemical reactors. In a recent experimental study by Lau and Nathan [1], it was found that particles in a turbulent pipe flow tend to migrate preferentially depending on their Stokes number (St). Particles with a high St (\u3e10) are concentrated near the axis while those with low St

A Novel Design Approach for Low Speed Recovery of High Performance Fighter Aircraft

In this paper, a novel design approach for low-speed recovery of a high-performance fighter aircraft is presented. It is shown that the phugoid mode has an important bearing on the problem of low-speed departure. Based on the analysis of the phugoid mode trajectories, a novel low-speed protection algorithm is presented in this paper. The proposed low speed recovery is achieved in three phases. The first phase consists of detecting the incipient departure followed in the second phase by the application of suitable recovery controls and finally the third phase ends with the transfer of controls to the pilot. The design of the first and the third phase consist of choosing the correct trigger conditions which ensures safe recovery of the aircraft in all conditions. The proposed Automatic low speed recovery is triggered when the aircraft trajectory crosses a fixed boundary in the region spanned by the dynamic pressure and its rate of decrease. It is observed that this boundary is approximately a straight line, implying that it is equivalent to a forward prediction in time to indicate when the aircraft will reach the lowest controllable airspeed. This Automatic Low Speed Recovery with Forward Prediction (ALSR-FP) algorithm is found to be simpler than other existing design methods and effective in preventing low speed departure for a variety of pilot inputs that result in the aircraft losing airspeed leading to stall. In the second phase control inputs are chosen to align the velocity vector to the direction of local gravity. The recovery phase is considered complete after the aircraft reaches the dynamic pressure which is approximately 10 % higher than the minimum dynamic pressure for control. Performance of the ALSR-FP is demonstrated using the high-performance fighter aircraft ADMIRE model which has a delta wing configuration, canards and multiple redundant controls. It is also shown that the proposed algorithm can be easily implemented on board for any other fighter and civil aircraft