Persistence of viable but non-culturable bacteria during the production and distribution of drinking water

The direct measurement of in situ respiring bacteria using 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) shows that, especially for Gram-negative bacteria, large numbers of viable but non-culturable (VBNC) bacteria are present in finished water from a conventional water treatment plant, and the regrowth of bacteria along distribution networks can be seen rapidly by using this very sensitive technique. The level of bacterial inactivation with chlorine is much less important than has been previously supposed (based on experiments with non-injured laboratory strains of bacteria and classical culture techniques). Threshold values of VBNC bacteria leaving water treatment plants or regrowing along distribution systems have to be determined for better control of coliform regrowth and health- risks associated with the consumption of drinking water.

In vitro osteoblast response to ferritic stainless steel fiber networks for magneto-active layers on implants

The use of a porous coating on prosthetic components to encourage bone ingrowth is an important way of improving uncemented implant fixation. Enhanced fixation may be achieved by the use of porous magneto-active layers on the surface of prosthetic implants, which would deform elastically on application of a magnetic field, generating internal stresses within the in-growing bone. This approach requires a ferromagnetic material able to support osteoblast attachment, proliferation, differentiation, and mineralization. In this study, the human osteoblast responses to ferromagnetic 444 stainless steel networks were considered alongside those to nonmagnetic 316L (medical grade) stainless steel networks. While both networks had similar porosities, 444 networks were made from coarser fibers, resulting in larger inter-fiber spaces. The networks were analyzed for cell morphology, distribution, proliferation, and differentiation, extracellular matrix production and the formation of mineralized nodules. Cell culture was performed in both the presence of osteogenic supplements, to encourage cell differentiation, and in their absence. It was found that fiber size affected osteoblast morphology, cytoskeleton organization and proliferation at the early stages of culture. The larger inter-fiber spaces in the 444 networks resulted in better spatial distribution of the extracellular matrix. The addition of osteogenic supplements enhanced cell differentiation and reduced cell proliferation thereby preventing the differences in proliferation observed in the absence of osteogenic supplements. The results demonstrated that 444 networks elicited favorable responses from human osteoblasts, and thus show potential for use as magnetically active porous coatings for advanced bone implant applications. © 2012 Wiley Periodicals, Inc.

Single molecule dynamics and statistical fluctuations of gene regulatory networks: A repressilator

The authors developed a time dependent method to study the single molecule dynamics of a simple gene regulatory network: a repressilator with three genes mutually repressing each other. They quantitatively characterize the time evolution dynamics of the repressilator. Furthermore, they study purely dynamical issues such as statistical fluctuations and noise evolution. They illustrated some important features of the biological network such as monostability, spirals, and limit cycle oscillation. Explicit time dependent Fano factors which describe noise evolution and show statistical fluctuations out of equilibrium can be significant and far from the Poisson distribution. They explore the phase space and the interrelationships among fluctuations, order, amplitude, and period of oscillations of the repressilators. The authors found that repressilators follow ordered limit cycle orbits and are more likely to appear in the lower fluctuating regions. The amplitude of the repressilators increases as the suppressing of the genes decreases and production of proteins increases. The oscillation period of the repressilators decreases as the suppressing of the genes decreases and production of proteins increases.

Synthesis using electrospinning and stabilization of single layer macroporous films and fibrous networks of poly(vinyl alcohol)

We demonstrated in this paper an electrospinning technique could be employed to prepare the single layer macroporous films and fibrous networks of poly(vinyl alcohol) (PVA). A crucial element using electrospinning on the development of these electrospun structures was to shorten the distance of from the needle tip to the collector (L), which resulted in the bond of the wet fibers deposited on the collector at the junctions. The morphologies and average pore size of electrospun structures of PVA were mainly predominated by L and the time of collecting wet fibers on the collector. In addition, experimental results showed that an increase of the PVA concentration or a decrease of the applied voltage could also diminish slightly the average pore size of electrospun productions. Furthermore, a 60 degrees C absolute ethanol soak to PVA electrospun production led them to be able to stabilize in water for 1 month against disintegration. Differential scanning calorimetry (DSC) demonstrated that the 60 degrees C ethanol soak enhanced the degree of crystallinity of PVA production. The structural characteristic of macroporous films and networks in combination with their easy processability suggests potential utility in issue engineering applications.

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

Collaborative artistic laboratories. Transfrontier spaces of cultural production

The appearance of the open code paradigm and the demands of social movements have permeated the ways in which today’s cultural institutions are organized. This article analyzes the birth of a new critical and cooperative spatiality and how it is transforming current modes of cultural research and production. It centers on the potential for establishing the new means of cooperation that are being tested in what are defined as collaborative artistic laboratories. These are hybrid spaces of research and creation based on networked and cooperative structures producing a new societal-technical body that forces us to reconsider the traditional organic conditions of the productive scenarios of knowledge and artistic practice.

Tigecycline challenge triggers sRNA production in Salmonella enterica serovar Typhimurium

Background: Bacteria employ complex transcriptional networks involving multiple genes in response to stress, which is not limited to gene and protein networks but now includes small RNAs (sRNAs). These regulatory RNA molecules are increasingly shown to be able to initiate regulatory cascades and modulate the expression of multiple genes that are involved in or required for survival under environmental challenge. Despite mounting evidence for the importance of sRNAs in stress response, their role upon antibiotic exposure remains unknown. In this study, we sought to determine firstly, whether differential expression of sRNAs occurs upon antibiotic exposure and secondly, whether these sRNAs could be attributed to microbial tolerance to antibiotics.

Results: A small scale sRNA cloning strategy of Salmonella enterica serovar Typhimurium SL1344 challenged with half the minimal inhibitory concentration of tigecycline identified four sRNAs (sYJ5, sYJ20, sYJ75 and sYJ118) which were reproducibly upregulated in the presence of either tigecycline or tetracycline. The coding sequences of the four sRNAs were found to be conserved across a number of species. Genome analysis found that sYJ5 and sYJ118 mapped between the 16S and 23S rRNA encoding genes. sYJ20 (also known as SroA) is encoded upstream of the tbpAyabKyabJ operon and is classed as a riboswitch, whilst its role in antibiotic stress-response appears independent of its riboswitch function. sYJ75 is encoded between genes that are involved in enterobactin transport and metabolism. Additionally we find that the genetic deletion of sYJ20 rendered a reduced viability phenotype in the presence of tigecycline, which was recovered when complemented. The upregulation of some of these sRNAs were also observed when S. Typhimurium was challenged by ampicillin (sYJ5, 75 and 118); or when Klebsiella pneumoniae was challenged by tigecycline (sYJ20 and 118).

Conclusions: Small RNAs are overexpressed as a result of antibiotic exposure in S. Typhimurium where the same molecules are upregulated in a related species or after exposure to different antibiotics. sYJ20, a riboswitch, appears to possess a trans-regulatory sRNA role in antibiotic tolerance. These findings imply that the sRNA mediated response is a component of the bacterial response to antibiotic challenge.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.