Investigations of an electrochemical platform based on the layered MoS2-graphene and horseradish peroxidase nanocomposite for direct electrochemistry and electrocatalysis

The self-assembly of layered molybdenum disulfide–graphene (MoS2–Gr) and horseradish peroxidase (HRP) by electrostatic attraction into a novel hybrid nanomaterial (HRP–MoS2–Gr) is reported. The properties of the MoS2–Gr were characterized by X-ray diffraction (XRD), high-resolution transmission electron microscopy (TEM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). UV–vis and Fourier transform infrared spectroscopy (FT-IR) indicate that the native structure of the HRP is maintained after the assembly, implying good biocompatibility of MoS2–Gr nanocomposite. Furthermore, the HRP–MoS2–Gr composite is utilized as a biosensor, which displays electrocatalytic activity to hydrogen peroxide (H2O2) with high sensitivity (679.7 μA mM−1 cm−2), wide linear range (0.2 μM–1.103 mM), low detection limit (0.049 μM), and fast amperometric response. In addition, the biosensor also exhibits strong anti-interference ability, satisfactory stability and reproducibility. These desirable electrochemical properties are attributed to the good biocompatibility and electron transport efficiency of the MoS2–Gr composite, as well as the high loading of HRP. Therefore, this biosensor is potentially suitable for H2O2 analysis in environmental, pharmaceutical, food or industrial applications.

Multipurpose nanoporous alumina-carbon nanowall bi-dimensional nano-hybrid platform via catalyzed and catalyst-free plasma CVD

Simple, rapid, plasma-assisted synthesis of large-area arrays of vertically-aligned carbon nanowalls on highly-porous, transparent bare and gold-coated alumina membranes with the two pore sizes is reported. It is demonstrated that the complex patterns of vertically aligned nanowalls can nucleate and form different morphologies in the low-temperature plasmas. The process is stable, and the twofold change in the gas flow (10 and 20 sccm) does not noticeably influence the morphology of the nanowall pattern. Application of a thin (5 nm) gold layer to nanoporous membrane prior to the nanowall growth allows controlling the network morphology.

Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging

Here we describe a protocol for advanced CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis). The CUBIC protocol enables simple and efficient organ clearing, rapid imaging by light-sheet microscopy and quantitative imaging analysis of multiple samples. The organ or body is cleared by immersion for 1–14 d, with the exact time required dependent on the sample type and the experimental purposes. A single imaging set can be completed in 30–60 min. Image processing and analysis can take <1 d, but it is dependent on the number of samples in the data set. The CUBIC clearing protocol can process multiple samples simultaneously. We previously used CUBIC to image whole-brain neural activities at single-cell resolution using Arc-dVenus transgenic (Tg) mice. CUBIC informatics calculated the Venus signal subtraction, comparing different brains at a whole-organ scale. These protocols provide a platform for organism-level systems biology by comprehensively detecting cells in a whole organ or body.

Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Functional activation during the Rapid Visual Information Processing task in a middle aged cohort: An fMRI study

The Rapid Visual Information Processing (RVIP) task, a serial discrimination task where task performance believed to reflect sustained attention capabilities, is widely used in behavioural research and increasingly in neuroimaging studies. To date, functional neuroimaging research into the RVIP has been undertaken using block analyses, reflecting the sustained processing involved in the task, but not necessarily the transient processes associated with individual trial performance. Furthermore, this research has been limited to young cohorts. This study assessed the behavioural and functional magnetic resonance imaging (fMRI) outcomes of the RVIP task using both block and event-related analyses in a healthy middle aged cohort (mean age = 53.56 years, n = 16). The results show that the version of the RVIP used here is sensitive to changes in attentional demand processes with participants achieving a 43% accuracy hit rate in the experimental task compared with 96% accuracy in the control task. As shown by previous research, the block analysis revealed an increase in activation in a network of frontal, parietal, occipital and cerebellar regions. The event related analysis showed a similar network of activation, seemingly omitting regions involved in the processing of the task (as shown in the block analysis), such as occipital areas and the thalamus, providing an indication of a network of regions involved in correct trial performance. Frontal (superior and inferior frontal gryi), parietal (precuenus, inferior parietal lobe) and cerebellar regions were shown to be active in both the block and event-related analyses, suggesting their importance in sustained attention/vigilance. These networks and the differences between them are discussed in detail, as well as implications for future research in middle aged cohorts.

The influence of pre-processing on the estimation of readability of web documents

This paper investigates the effect that text pre-processing approaches have on the estimation of the readability of web pages. Readability has been highlighted as an important aspect of web search result personalisation in previous work. The most widely used text readability measures rely on surface level characteristics of text, such as the length of words and sentences. We demonstrate that different tools for extracting text from web pages lead to very different estimations of readability. This has an important implication for search engines because search result personalisation strategies that consider users reading ability may fail if incorrect text readability estimations are computed.

Biofuels from food processing wastes

Food processing industry generates substantial high organic wastes along with high energy uses. The recovery of food processing wastes as renewable energy sources represents a sustainable option for the substitution of fossil energy, contributing to the transition of food sector towards a low-carbon economy. This article reviews the latest research progress on biofuel production using food processing wastes. While extensive work on laboratory and pilot-scale biosystems for energy production has been reported, this work presents a review of advances in metabolic pathways, key technical issues and bioengineering outcomes in biofuel production from food processing wastes. Research challenges and further prospects associated with the knowledge advances and technology development of biofuel production are discussed.

Insights into students' conceptual understanding using textual analysis: A case study in signal processing

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.

Lesbian, gay, bisexual, trans, and queer visibility through selfies: Comparing platform mediators across Ruby Rose’s Instagram and Vine presence

This article investigates the relationship between social media platforms and the production and dissemination of selfies in light of its implications for the visibility of lesbian, gay, bisexual, trans, and queer (LGBTQ) people. Applying an Actor Network Theory lens, two popular visual media apps, Instagram and Vine, are examined through a comparative walkthrough method. This reveals platform elements, or mediators, that can influence the conversational capacity of selfies in terms of the following: range, the variety of discourses addressed within a selfie; reach, circulation within and across publics; and salience, the strength and clarity of discourses communicated through a selfie. These mediators are illustrated through LGBTQ celebrity Ruby Rose’s Instagram selfies and Vine videos. Instagram’s use expectations encourage selfies focused on mainstream discourses of normative beauty and conspicuous consumption with an emphasis on appearance, extending through features constraining selfies’ reach and salience. In contrast, Vine’s broader use expectations enable a variety of discourses to be communicated across publics with an emphasis on creative, first-person sharing. These findings are reflected in Rose’s Instagram selfies, which mute alternative discourses of gender and sexuality through desexualized and aesthetically appealing self-representations, while Vines display her personal side, enabling both LGBTQ and heterosexual, cisgender people to identify with her without minimizing non-normative aspects of her gender and sexuality. These findings demonstrate the relevance of platforms in shaping selfies’ conversational capacity, as mediators can influence whether selfies feature in conversations reinforcing dominant discourses or in counterpublic conversations, contributing to everyday activism that challenges normative gender and sexual discourses.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Appendiceal sonography in children: A retrospective analysis as a platform for potential implementation of diagnostic categories

- Background Sonography is an important diagnostic tool in children with suspected appendicitis. Reported accuracy and appendiceal visualisation rates vary significantly, as does the management of equivocal ultrasound findings. The aim of this study was to audit appendiceal sonography at a tertiary children's hospital, and provide baseline data for a future prospective study. - Summary of work Records of children who underwent ultrasound studies for possible appendicitis between January 2008 and December 2010 were reviewed. Variables included patient demographics, sonographic appendix characteristics, and secondary signs. Descriptive statistics and analysis using ANOVA, Mann-Whitney U test, and ROC curves were performed. Mater Human Research Ethic Committee approval was granted. - Summary of results There were 457 eligible children. Using a dichotomous diagnostic model (including equivocal results), sensitivity was 89.6%, specificity 91.6%, and diagnostic yield of 40.7%. ROC curve analysis of a 6mm diameter cut-off was 0.88 AUC (95% CI 0.80 to 0.95). - Discussion and conclusions Sonography is an accurate test for acute appendicitis in children, with a high sensitivity and negative predictive value. A diameter of 6mm as an absolute cut-off in a binary model can lead to false findings. Results were compared with available literature. Recent publications propose categorising diameter1 and integrating secondary signs2 to improve accuracy and provide more meaningful results to clinicians. This study will be a benchmark for future studies with multiple diagnostic categorisation.