Mechanisms of Giardia lamblia differentiation into cysts

Microbiologists have long been intrigued by the ability of parasitic organisms to adapt to changes in the environment. Since most parasites occupy several niches during their journey between vectors and hosts, they have developed adaptive responses which allow them to survive under adverse conditions. Therefore, the life cycles of protozoan and helminthic parasites are excellent models with which to study numerous mechanisms involved in cell differentiation, such as the regulation of gene expression, signal transduction pathways, and organelle biogenesis. Unfortunately, many of these studies are very difficult because the conditions needed to elicit developmental changes in parasites remain undetermined in most cases. Recently, several interesting findings were reported on the process of differentiation of Giardia lamblia trophozoites into cysts. G. lamblia is a flagellated protozoan that inhabits the upper small intestine of its vertebrate host and is a major cause of enteric disease worldwide. It belongs to the earliest identified lineage among eukaryotes and therefore offers a unique insight into the progression from primitive to more complex eukaryotic cells. The discovery of a specific stimulus that induces trophozoites to differentiate into cysts, the identification and characterization of encystation-specific molecules, the elucidation of novel biochemical pathways, and the development of useful reagents and techniques have made this parasite an excellent model with which to study differentiation in eukaryotic cells. In this review, we summarize the most recent fundings on several aspects of Giardia differentiation and discuss the significance of these findings within the context of current knowledge in the field.     

ZnO/NiO heterostructure-based microsensors used in formaldehyde detection at room temperature: Influence of the sensor operating voltage

Journal of Materials Science: Materials in Electronics - Recently the emissions of volatile organic compounds (VOCs) in the atmosphere have increased dramatically with rapid development of...     

Fabrication of ferrogels using different magnetic nanoparticles and their performance on protein adsorption

Magnetic biomaterials were prepared using magnetite and chitosan‐coated magnetite nanoparticles (CSNPs) dispersed in poly(vinyl alcohol) gels. Two different methods were developed to obtain ferrogels: in situ co‐precipitation of magnetite (Ferro‐IS) and by adding previously synthesized CSNPs to the neat matrix (Ferro‐CSNPs). In both cases, the crosslinking was carried out by freezing ? thawing (F‐T). The as‐prepared materials as well as precursor CSNPs were characterized by Fourier transform infrared spectroscopy, electronic microscopy (scanning and transmission), X‐ray diffraction, ζ potential, dynamic light scattering, thermogravimetric analysis, differential scanning calorimetry and magnetic properties. The performance of these gels as protein adsorbents was evaluated. Batch adsorption experiments were carried out using bovine serum albumin (BSA) as a model. Substantially different adsorption behaviour was found using Ferro‐IS and Ferro‐CSNPs. This was assigned to dissimilar bonding mechanisms of BSA to the ferrogel matrix. Hence, biomaterials potentially useful in drug delivery as well as in protein purification fields may be prepared by a relatively simple, non‐toxic and low cost method. © 2013 Society of Chemical Industry     

Reduced complexity decoding of convolutional codes based on the M-algorithm and the minimal trellis

In this paper, we propose three new sub-optimum, reduced complexity decoding algorithms for convolutional codes. The algorithms are based on the minimal trellis representation for the convolutional code and on the M-algorithm. Since the minimal trellis has a periodically time-varying state profile, each algorithm has a different strategy to select the number of surviving states in each trellis depth. We analyse both the computational complexity, in terms of arithmetic operations, and the bit error rate performance of the proposed algorithms over the additive white Gaussian noise channel. Results demonstrate that considerable complexity reductions can be obtained at the cost of a small loss in the performance, as compared to the Viterbi decoder.     

Stability of lactobacilli encapsulated in various microbial polymers

Various microbial polymers, namely xanthan gum, gellan gum, pullulan gum and jamilan, were tested as a suitable encapsulating material for Lactobacillus plantarum CRL 1815 and Lactobacillus rhamnosus ATCC 53103. Resulting capsules were also studied for their pH and simulated gastrointestinal conditions tolerance. The morphology of the microcapsules was studied using scanning electron microscopy. pH tolerance was tested at pH 2.0, 3.5, 5.0 and 6.5 over a 6h incubation period. Simulated gastrointestinal conditions were assayed with simulated gastric and pancreatic juices and simulated bile over a 24h incubation period. Suspensions of probiotic organisms were used as a control. The results from encapsulation with microbial polymers indicate that mixtures of 1% xanthan gum with 0.75% gellan gum and 1% jamilan with 1% gellan gum were the most suitable for microencapsulation. Results for the pH tolerance tests showed no improvement in the viability of cells in relation to the control, except for pH 2.0 where lactobacilli encapsulated in xanthan:gellan gum (1%:0.75%) prolonged their viability by 6h exposure. Xanthan:gellan gum (1%:0.75%) was the most effective of the encapsulating materials tested in protecting L. plantarum and L. rhamnosus against simulated bile, improving its viability in 1-2 logCFU when compared with control. The results of this study suggest that microbial polymers are an interesting source of encapsulating material that should be taken into account for prospective studies of probiotic encapsulation for oral delivery applications.     

Modelling mould growth under suboptimal environmental conditions and inoculum size

Predictive models can be a tool to develop strategies to prevent mould development and consequently mycotoxin production. The aims of this work were to assess the impact of a) high/low levels of inoculum and b) optimal/suboptimal environmental conditions on fungal responses based on both kinetic and probabilistic models. Different levels of spore suspensions of Aspergillus carbonarius and Penicillium expansum were prepared and inoculated centrally with a needlepoint load on malt extract agar (MEA) with 50 replicates. While optimum conditions led to a colony diameter increase which followed Baranyi’s function, suboptimal conditions led to different grow functions. In general, growth rate (μ) and lag phase (λ) were normally distributed. Specifically, the growth rate (μ) showed similar distributions under optimal growth conditions, regardless of the inoculum level, while suboptimal a_w and temperature conditions led to higher kurtosis distributions, mainly when the inoculum levels were low. Regarding λ, more skewed distributions were observed, mainly when the inoculum levels were low. Probability models were not much affected by the inoculum size. Lower probabilities of growth were in general predicted under marginal conditions at a given time for both strains. The slopes of the probability curves were smaller under suboptimal growth conditions due to wider distributions. Results showed that a low inoculum level and suboptimal conditions lead to high variability of the estimated growth parameters and growth probability.     

Digital signal processing of in-cylinder pressure for combustion diagnosis of internal combustion engines

In-cylinder pressure analysis is a key tool for engine research and diagnosis; however, it normally requires to process the experimental signal for providing valuable information. Usual four-step data processing consists on level correction, angle referencing, cycle averaging, and filtering. Concerning the last two issues, ad-hoc methods and experience-based algorithms are mostly used, and there is not a consensus in the scientific community about the optimal way to proceed. This paper presents a step-by-step approach to optimise the signal processing both for offline and online applications based on the characteristics of the signal.     

A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal

In-cylinder pressure measurement has historically been used for off-line combustion diagnosis, but online application for real-time combustion control has become of great interest. This work considers low computing-cost methods for analysing the instant variation of the chamber pressure, directly obtained from the electric signal provided by a traditional piezoelectric sensor. Presented methods are based on the detection of sudden changes in the chamber pressure, which are amplified by the pressure derivative, and which are due to thermodynamic phenomena within the cylinder. Signal analysis tools both in time and in time-frequency domains are used for detecting the start of combustion, the end of combustion and the heat release peak. Results are compared with classical thermodynamic analysis and validated in several turbocharged diesel engines.