Diffusion-sensitive magnetic resonance spectroscopy and imaging in biomedical sciences

We present a mini-review of the development and contemporary applications of diffusion-sensitive nuclear magnetic resonance (NMR) techniques in biomedical sciences. Molecular diffusion is a fundamental physical phenomenon present in all biological systems. Due to the connection between experimentally measured diffusion metrics and the microscopic environment sensed by the diffusing molecules, diffusion measurements can be used for characterisation of molecular size, molecular binding and association, and the morphology of biological tissues. The emergence of magnetic resonance was instrumental to the development of biomedical applications of diffusion. We discuss the fundamental physical principles of diffusion NMR spectroscopy and diffusion MR imaging. The emphasis is placed on conceptual understanding, historical evolution and practical applications rather than complex technical details. Mathematical description of diffusion is presented to the extent that it is required for the basic understanding of the concepts. We present a wide range of spectroscopic and imaging applications of diffusion magnetic resonance, including colloidal drug delivery vehicles; protein association; characterisation of cell morphology; neural fibre tractography; cardiac imaging; and the imaging of load-bearing connective tissues. This paper is intended as an accessible introduction into the exciting and growing field of diffusion magnetic resonance.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Hydrogels containing silver nanoparticles for burn wounds show antimicrobial activity without cytotoxicity

This research introduces a novel dressing for burn wounds, containing silver nanoparticles in hydrogels for infected burn care. The 2-acrylamido-2-methylpropane sulfonic acid sodium salt hydrogels containing silver nanoparticles have been prepared via ultraviolet radiation. The formation of silver nanoparticles was monitored by surface plasmon bands and transmission electron microscopy. The concentration of silver nitrate loaded in the solutions slightly affected the physical properties and mechanical properties of the neat hydrogel. An indirect cytotoxicity study found that none of the hydrogels were toxic to tested cell lines. The measurement of cumulative release of silver indicated that 70%–82% of silver was released within 72 hr. The antibacterial activities of the hydrogels against common burn pathogens were studied and the results showed that 5 mM silver hydrogel had the greatest inhibitory activity. The results support its use as a potential burn wound dressing.

Structures of μOconotoxins from Conusmarmoreus. Inhibitors of tetrodotoxin (TTX)-sensitive and TTX-resistant sodium channels in mammalian sensory neurons

The μO-conotoxins are an intriguing class of conotoxins targeting various voltage-dependent sodium channels and molluscan calcium channels. In the current study, we have shown MrVIA and MrVIB to be the first known peptidic inhibitors of the transient tetrodotoxin-resistant (TTX-R) Na+ current in rat dorsal root ganglion neurons, in addition to inhibiting tetrodotoxin-sensitive Na+ currents. Human TTX-R sodium channels are a therapeutic target for indications such as pain, highlighting the importance of the μO-conotoxins as potential leads for drug development. Furthermore, we have used NMR spectroscopy to provide the first structural information on this class of conotoxins. MrVIA and MrVIB are hydrophobic peptides that aggregate in aqueous solution but were solubilized in 50% acetonitrile/water. The three-dimensional structure of MrVIB consists of a small β-sheet and a cystine knot arrangement of the three-disulfide bonds. It contains four backbone “loops” between successive cysteine residues that are exposed to the solvent to varying degrees. The largest of these, loop 2, is the most disordered part of the molecule, most likely due to flexibility in solution. This disorder is the most striking difference between the structures of MrVIB and the known δ- and ω-conotoxins, which along with the μO-conotoxins are members of the O superfamily. Loop 2 of ω-conotoxins has previously been shown to contain residues critical for binding to voltage-gated calcium channels, and it is interesting to speculate that the flexibility observed in MrVIB may accommodate binding to both sodium and molluscan calcium channels.

Sensitive methods for studying the environmental performance of protective coatings

The safe working lifetime of a structure in a corrosive or other harsh environment is frequently not limited by the material itself but rather by the integrity of the coating material. Advanced surface coatings are usually crosslinked organic polymers such as epoxies and polyurethanes which must not shrink, crack or degrade when exposed to environmental extremes. While standard test methods for environmental durability of coatings have been devised, the tests are structured more towards determining the end of life rather than in anticipation of degradation. We have been developing prognostic tools to anticipate coating failure by using a fundamental understanding of their degradation behaviour which, depending on the polymer structure, is mediated through hydrolytic or oxidation processes. Fourier transform infrared spectroscopy (FTIR) is a widely-used laboratory technique for the analysis of polymer degradation and with the development of portable FTIR spectrometers, new opportunities have arisen to measure polymer degradation non-destructively in the field. For IR reflectance sampling, both diffuse (scattered) and specular (direct) reflections can occur. The complexity in these spectra has provided interesting opportunities to study surface chemical and physical changes during paint curing, service abrasion and weathering, but has often required the use of advanced statistical analysis methods such as chemometrics to discern these changes. Results from our studies using this and related techniques and the technical challenges that have arisen will be presented.

Processing biases towards gain-framed messages may lead reward sensitive individuals to adopt safer driving behaviours

Using Gray and McNaughton’s revised RST, this study investigated the extent to which the Behavioural Approach System (BAS) and the Fight-Flight-Freeze System (FFFS) influence the processing of gain-framed and loss-framed road safety messages and subsequent message acceptance. It was predicted that stronger BAS sensitivity and FFFS sensitivity would be associated with greater processing and acceptance of the gain-framed messages and loss-framed messages, respectively. Young drivers (N = 80, aged 17–25 years) viewed one of four road safety messages and completed a lexical decision task to assess message processing. Both self-report (e.g., Corr-Cooper RST-PQ) and behavioural measures (i.e., CARROT and Q-Task) were used to assess BAS and FFFS traits. Message acceptance was measured via self-report ratings of message effectiveness, behavioural intentions, attitudes and subsequent driving behaviour. The results are discussed in the context of the effect that differences in reward and punishment sensitivities may have on message processing and message acceptance.

Poly(2-oxazoline) hydrogels as next generation three dimensional cell supports

Synthetic hydrogels selectively decorated with cell adhesion motifs are rapidly emerging as promising substrates for 3D cell culture. When cells are grown in 3D they experience potentially more physiologically relevant cell-cell interactions and physical cues compared with traditional 2D cell culture on stiff surfaces. A newly developed polymer based on poly(2-oxazoline)s has been used for the first time to control attachment of fibroblast cells and is discussed here for its potential use in 3D cell culture with particular focus on cancer cells towards the ultimate aim of high throughput screening of anti-cancer therapies. Advantages and limitations of using poly(2-oxazoline) hydrogels are discussed and compared with more established polymers, especially polyethylene glycol (PEG).

Are in vitro estimates of cell diffusivity and cell proliferation rate sensitive to assay geometry?

Cells respond to various biochemical and physical cues during wound–healing and tumour progression. In vitro assays used to study these processes are typically conducted in one particular geometry and it is unclear how the assay geometry affects the capacity of cell populations to spread, or whether the relevant mechanisms, such as cell motility and cell proliferation, are somehow sensitive to the geometry of the assay. In this work we use a circular barrier assay to characterise the spreading of cell populations in two different geometries. Assay 1 describes a tumour–like geometry where a cell population spreads outwards into an open space. Assay 2 describes a wound–like geometry where a cell population spreads inwards to close a void. We use a combination of discrete and continuum mathematical models and automated image processing methods to obtain independent estimates of the effective cell diffusivity, D, and the effective cell proliferation rate, λ. Using our parameterised mathematical model we confirm that our estimates of D and λ accurately predict the time–evolution of the location of the leading edge and the cell density profiles for both assay 1 and assay 2. Our work suggests that the effective cell diffusivity is up to 50% lower for assay 2 compared to assay 1, whereas the effective cell proliferation rate is up to 30% lower for assay 2 compared to assay 1.

Protocol : A highly sensitive RT-PCR method for detection and quantification of microRNAs

MicroRNAs (miRNAs) are a class of small non-coding RNAs with a critical role in development and environmental responses. Efficient and reliable detection of miRNAs is an essential step towards understanding their roles in specific cells and tissues. However, gel-based assays currently used to detect miRNAs are very limited in terms of throughput, sensitivity and specificity. Here we provide protocols for detection and quantification of miRNAs by RT-PCR. We describe an end-point and real-time looped RT-PCR procedure and demonstrate detection of miRNAs from as little as 20 pg of plant tissue total RNA and from total RNA isolated from as little as 0.1 l of phloem sap. In addition, we have developed an alternative real-time PCR assay that can further improve specificity when detecting low abundant miRNAs. Using this assay, we have demonstrated that miRNAs are differentially expressed in the phloem sap and the surrounding vascular tissue. This method enables fast, sensitive and specific miRNA expression profiling and is suitable for facilitation of high-throughput detection and quantification of miRNA expression.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Highly NO2 sensitive caesium doped graphene oxide conductometric sensors

Here we report on the synthesis of caesium doped graphene oxide (GO-Cs) and its application to the development of a novel NO2 gas sensor. The GO, synthesized by oxidation of graphite through chemical treatment, was doped with Cs by thermal solid-state reaction. The samples, dispersed in DI water by sonication, have been drop-casted on standard interdigitated Pt electrodes. The response of both pristine and Cs doped GO to NO2 at room temperature is studied by varying the gas concentration. The developed GO-Cs sensor shows a higher response to NO2 than the pristine GO based sensor due to the oxygen functional groups. The detection limit measured with GO-Cs sensor is ≈90 ppb.

A control theoretic paradigm for cell signaling networks : a simple complexity for a sensitive robustness

The fields of molecular biology and cell biology are being flooded with complex genomic and proteomic datasets of large dimensions. We now recognize that each molecule in the cell and tissue can no longer be viewed as an isolated entity. Instead, each molecule must be considered as one member of an interacting network. Consequently, there is an urgent need for mathematical models to understand the behavior of cell signaling networks in health and in disease.

Changes in environmentally sensitive productivity and technological modernization in China's iron and steel industry in the 1990s

Technological modernization is widely believed to contribute positively both to economic development and to environmental and resource conservation, through improvements in productivity and strengthening of business competitiveness. However, this may not always be true, particularly in the short term, as it requires substantial investments and may impose financial burdens on firms undertaking such investments. This study empirically examines the effects of technological modernization in China's iron and steel industry in the 1990s on conventional economic productivity (CEP) and environmentally sensitive productivities (ESPs). We employ a directional distance function that can handle multiple inputs and outputs to compute relative production efficiencies. We apply these models to the data covering 27 iron and steel firms in China between 1990 and 1999-a period when the Chinese iron and steel industry modernized rapidly. We find that ESPs have continuously improved, even in the period when the CEP declined.