A rapid and sensitive UPLC-ESI MS method for analysis of isofraxidin, a natural antistress compound, and its metabolites in rat plasma

Isofraxidin is one of the main bioactive constituents in the root of Acanthopanax senticosus, which has antifatigue, antistress, and immuno-accommondating effects. In this study, an ultraperformance LC (UPLC)-ESI MS method was developed for analyzing isofraxidin and its metabolites in rat plasma. The analysis was performed on a UPLC coupled with ESI MS (quadropole MS tandem TOF MS). The lower LOD (LLOD) for isofraxidin was 0.25 ng/mL, the intraday precision was less than 10%, the interday precision was less than 10%, and the extraction recovery was more than 80%. Isofraxidin and two metabolites (M1 and M2) were detected in rat plasma after oral administration of isofraxidin, and the molecular polarities of M1 and M2 were both increased compared to isofraxidin. The metabolites were identified as 5,6-dihydroxyl-7-methoxycoumarin and 5-hydroxyl-6,7-dimethoxycoumarin when subjected to parent ion spectra, product ion spectra, and extract mass and element composition analyses.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Chemotherapy-induced nausea and vomiting : a review to inform clinical practice

Chemotherapy-induced nausea and vomiting (CINV) is a common sideeffect of cytotoxic treatment and despite the widespread use of anti-emetic medication, it continues to affect a significant proportion of patients with up to 23% and 73% of chemotherapy patients still experiencing vomiting and nausea symptoms, respectively. This is of particular concern in oncology patients as nausea and vomiting may result in malnutrition, decreased quality of life and in extreme cases, treatment stoppage. Therefore, the primary aim of this paper was to inform clinicians on the current literature regarding CINV including its effect on the patient, its pathophysiology, and current treatment options. In addition, this review will also discuss the usage of dietetic interventions as well as less utilised, novel interventions such as oral ginger extracts in the treatment of CINV. In order to address these issues, a systematic literature search was conducted using Pubmed, CINAHL, MEDLINE, Embase, and Health Source (Nursing/Academic Edition). A key finding of this review was that common dietary strategies (e.g. eating slowly, avoiding fatty foods) seem to be solely based on professional opinion as no clinical trials investigating these strategies were identified. In contrast, ginger extracts were found to possess several viable mechanisms that interact with CINV progression including 5-HT3, Substance P and acetylcholine receptor antagonism; anti-inflammatory and antioxidant properties; and gastrointestinal motility and gastric emptying modulation. In conclusion, research investigating dietetic interventions in the management of CINV is sparse and requires further investigation while novel intervention such as ginger, possess multiple mechanisms that may benefit CINV management. This review will discuss the prevalence and significance of CINV, dietetic and novel treatment options, and provide implications for clinical practise and future research.

Multiple types of data are required to identify the mechanisms influencing the spatial expansion of melanoma cell colonies

Background The expansion of cell colonies is driven by a delicate balance of several mechanisms including cell motility, cell-to-cell adhesion and cell proliferation. New approaches that can be used to independently identify and quantify the role of each mechanism will help us understand how each mechanism contributes to the expansion process. Standard mathematical modelling approaches to describe such cell colony expansion typically neglect cell-to-cell adhesion, despite the fact that cell-to-cell adhesion is thought to play an important role. Results We use a combined experimental and mathematical modelling approach to determine the cell diffusivity, D, cell-to-cell adhesion strength, q, and cell proliferation rate, ?, in an expanding colony of MM127 melanoma cells. Using a circular barrier assay, we extract several types of experimental data and use a mathematical model to independently estimate D, q and ?. In our first set of experiments, we suppress cell proliferation and analyse three different types of data to estimate D and q. We find that standard types of data, such as the area enclosed by the leading edge of the expanding colony and more detailed cell density profiles throughout the expanding colony, does not provide sufficient information to uniquely identify D and q. We find that additional data relating to the degree of cell-to-cell clustering is required to provide independent estimates of q, and in turn D. In our second set of experiments, where proliferation is not suppressed, we use data describing temporal changes in cell density to determine the cell proliferation rate. In summary, we find that our experiments are best described using the range D = 161 - 243 ?m2 hour-1, q = 0.3 - 0.5 (low to moderate strength) and ? = 0.0305 - 0.0398 hour-1, and with these parameters we can accurately predict the temporal variations in the spatial extent and cell density profile throughout the expanding melanoma cell colony. Conclusions Our systematic approach to identify the cell diffusivity, cell-to-cell adhesion strength and cell proliferation rate highlights the importance of integrating multiple types of data to accurately quantify the factors influencing the spatial expansion of melanoma cell colonies.

Porohyperelastic analysis of single osteocyte using AFM and inverse FEA

Osteocytes are the mature cells and perform as mechanosensors within the bone. The mechanical property of osteocytes plays an important role to fulfill these functions. However, little researches have been done to investigate the mechanical deformation properties of single osteocytes. Atomic Force Microscopy (AFM) is a state-of-art experimental facility for high resolution imaging of tissues, cells and any surfaces as well as for probing mechanical properties of the samples both qualitatively and quantitatively. In this paper, the experimental study based on AFM is firstly used to obtain forceindentation curves of single round osteocytes. The porohyperelastic (PHE) model of a single osteocyte is then developed by using the inverse finite element analysis (FEA) to identify and extract mechanical properties from the experiment results. It has been found that the PHE model is a good candidature for biomechanics studies of osteocytes.

Synthesis of complementary RNA by RNA-dependent RNA polymerases in plant extracts is independent of an RNA primer

RNA-dependent RNA polymerase (RDR) activities were readily detected in extracts from cauliflower and broccoli florets, Arabidopsis thaliana (L.) Heynh callus tissue and broccoli nuclei. The synthesis of complementary RNA (cRNA) was independent of a RNA primer, whether or not the primer contained a 3′ terminal 2′-O-methyl group or was phosphorylated at the 5′ terminus. cRNA synthesis in plant extracts was not affected by loss-of-function mutations in the DICER-LIKE (DCL) proteins DCL2, DCL3, and DCL4, indicating that RDRs function independently of these DCL proteins. A loss-of-function mutation in RDR1, RDR2 or RDR6 did not significantly reduce the amount of cRNA synthesis. This indicates that these RDRs did not account for the bulk RDR activities in plant extracts, and suggest that either the individual RDRs each contribute a fraction of polymerase activity or another RDR(s) is predominant in the plant extract. © CSIRO 2008.

Rice ragged stunt oryzavirus genome segment S4 could encode an RNA dependent RNA polymerase and a second protein of unknown function

The complete nucleotide sequence of genome segment S4 of rice ragged stunt oryzavirus (RRSV, Thai-isolate) was determined. The 3823 bp sequence contains two large open reading frames (ORFs). ORF1, spanning nucleotides 12 to 3776, is capable of encoding a protein of M(r) 141,380 (P4a). The P4a amino acid sequence predicted from the nucleotide sequence contains sequence motifs conserved in RNA-dependent RNA polymerases (RDRPs). When compared for evolutionary relationships with RDRPs of other reoviruses using the amino acid sequences around the conserved GDD motif, P4a was shown to be more related to Nilaparvata lugens reovirus and reovirus serotype 3 than to rice dwarf phytoreovirus, bovine rotavirus or bluetongue virus. The ORF2, spanning nucleotides 491 to 1468, is out of frame with ORF1 and is capable of encoding a protein of 36, 920 (P4b). Coupled in vitro transcription-translation from cloned ORF2 in wheat germ extract confirmed the existence of ORF2 but in vivo production and possible function of P4b is yet to be determined.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

False-colour spectrograms of long duration acoustic recordings

Acoustic recordings of the environment are an important aid to ecologists monitoring biodiversity and environmental health. However, rapid advances in recording technology, storage and computing make it possible to accumulate thousands of hours of recordings, of which, ecologists can only listen to a small fraction. The big-data challenge is to visualize the content of long-duration audio recordings on multiple scales, from hours, days, months to years. The visualization should facilitate navigation and yield ecologically meaningful information. Our approach is to extract (at one minute resolution) acoustic indices which reflect content of ecological interest. An acoustic index is a statistic that summarizes some aspect of the distribution of acoustic energy in a recording. We combine indices to produce false-colour images that reveal acoustic content and facilitate navigation through recordings that are months or even years in duration.

Damage detection in cable structures using vibration characteristics

Cable structures find many applications such as in power transmission, in anchors and especially in bridges. They serve as major load bearing elements in suspension bridges, which are capable of spanning long distances. All bridges, including suspension bridges, are designed to have long service lives. However, during this long life, they become vulnerable to damage due to changes in loadings, deterioration with age and random action such as impacts. The main cables are more vulnerable to corrosion and fatigue, compared to the other bridge components, and consequently reduces the serviceability and ultimate capacity of the bridge. Detecting and locating such damage at the earliest stage is challenging in the current structural health monitoring (SHM) systems of long span suspension bridges. Damage or deterioration of a structure alters its stiffness, mass and damping properties which in turn modify its vibration characteristics. This phenomenon can therefore be used to detect damage in a structure. The modal flexibility, which depends on the vibration characteristics of a structure, has been identified as a successful damage indicator in beam and plate elements, trusses and simple structures in reinforced concrete and steel. Successful application of the modal flexibility phenomenon to detect and locate the damage in suspension bridge main cables has received limited attention in recent research work. This paper, therefore examines the potential of the modal flexibility based Damage Index (DI) for detecting and locating damage in the main cable of a suspension bridge under four different damage scenarios. Towards this end, a numerical model of a suspension bridge cable was developed to extract the modal parameters at both damaged and undamaged states. Damage scenarios considered in this study with varied location and severity were simulated by changing stiffness at particular locations of the cable model. Results confirm that the DI has the potential to successfully detect and locate damage in suspension bridge main cables. This simple method can therefore enable bridge engineers and managers to detect and locate damage in suspension bridges at an early stage, minimize expensive retrofitting and prevent bridge collapse.

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.

A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.