Dual responsive chemosensors for anions : the combination of fluorescent PET (Photoinduced Electron Transfer) and colorimetric chemosensors in a single molecule

The design and synthesis of two novel fluorescent PET anion sensors is described, based on the principle of ‘fluorophore-spacer-(anion)receptor’. The sensors 1 and 2 employ simple diaromatic thioureas as anion receptors, and the fluorophore is a naphthalimide moiety that absorbs in the visible part of the spectrum and emits in the green. Upon recognition of anions such as F⁻ and AcO⁻ in DMSO, the fluorescence emission of 1 and 2 was ‘switched off’, with no significant changes in the UV–vis spectra. This recognition shows a 1:1 binding between the receptor and the anions. In the case of F⁻, further additions of the anion, gave rise to large changes in the UV–vis spectra, where the λ_max at 455 nm was shifted to 550 nm. These changes are thought to be due to the deprotonation of the 4-amino moiety of the naphthalimide fluorophore. This was in fact found to be the case, using simple naphthalimide derivatives such as 6. Sensors 1 and 2 can thus display dual sensing action; where at low concentrations, the fluorescence emission is quenched, and at higher concentrations the absorption spectra are modulated.

A heuristic model selection scheme for representing hot flow data using the hot torsion test results

The problem of "model selection" for expressing a wide range of constitutive behaviour adequately using hot torsion test data was considered here using a heuristic approach. A model library including several nested parametric linear and non-linear models was considered and applied to a set of hot torsion test data for API-X 70 micro-alloyed steel with a range of strain rates and temperatures. A cost function comprising the modelled hot strength data and that of the measured data were utilized in a heuristic model selection scheme to identify the optimum models. It was shown that a non-linear rational model including ten parameters is an optimum model that can accurately express the multiple regimes of hardening and softening for the entire range of the experiment. The parameters for the optimum model were estimated and used for determining variations of hot strength of the samples with deformation.

The effect of humidity and temperature on Wool Comfort Meter assessment of single jersey wool fabrics

The Wool ComfortMeter provides an objective measurement of the fabric-evoked prickle discomfort rating provided by wearers. This work aimed to quantify the sensitivity of the Wool ComfortMeter over a range of different temperature and humidity conditions to determine the recommended test conditions for its operation. The design was: three temperatures (notionally 20, 25 and 30°C) at three relative humidities (RHs, notionally 50, 65 and 80%) each with two replicates, using six different wool single jersey knits (mean fibre diameter 19.5–27.0 µm). As it was difficult to achieve exactly some of the extreme combinations of temperature and RH, some combinations were repeated, providing a total of 23 different assessment conditions. Data were analysed using restricted maximum likelihood mixed model analysis. The best fixed model included RH, RH2, temperature and the interaction of temperature and RH, accounting for 95% of the variation in Wool ComfortMeter readings. Wool ComfortMeter values were almost constant at 55–60% RH. Generally, the Wool ComfortMeter value reduced with increasing RH > 60% at temperatures of 25°C and 28.5°C as the regain of the fabric increased. However, at 20°C little change was detected as RH was increased from 50 to 80% as there were only small changes in fabric regain. The observed effects were in a good agreement with existing knowledge on the effect of regain on the mechanical properties of wool fibre. Wool ComfortMeter is best operated under standard conditions for textile testing of 65% RH and 20°C.

The influence of job, team and organizational level resources on employee well-being, engagement, commitment and extra-role performance : test of a model

Purpose – Worker well-being continues to be fundamental to the study of work and a primary consideration for how organizations can achieve competitive advantage and sustainable and ethical work practices (Cartwright and Holmes; Harter, Schmidt and Keyes; Wright and Cropanzano). The science and practice of employee engagement, a key indicator of employee well-being, continues to evolve with ongoing incremental refinements to existing models and measures. This study aims to elaborate the Job Demands-Resources model of work engagement (Bakker and Demerouti) by examining how organizational, team and job level factors interrelate to influence engagement and well-being and downstream outcome variables such as affective commitment and extra-role behaviour.

Design/methodology/approach – Structural equations modelling of survey data obtained from 3,437 employees of a large multi-national mining company was used to test the important direct and indirect influence of organizational focused resources (a culture of fairness and support), team focused resources (team climate) and job level resources (career development, autonomy, supervisor support, and role clarity) on employee well-being, engagement, extra-role behaviour and organizational commitment.

Findings – The fit of the proposed measurement and structural models met criterion levels and the structural model accounted for sizable proportions of the variance in engagement/wellbeing (66 percent), extra-role-behaviour (52 percent) and commitment (69 percent).

Research limitations/implications – Study limitations (e.g. cross-sectional research design) and future opportunities are outlined.

Originality/value – The study demonstrates important extensions to the Job Demands-Resources model and provides researchers and practitioners with a simple but powerful motivational framework, a suite of measures, and a map of their inter-relationships which can be used to help understand, develop and manage employee well-being and engagement and their outcomes.

A test of core assumptions of the catastrophic misinterpretation model of panic disorder

The catastrophic misinterpretation model of panic disorder proposes that spontaneous panic attacks are the result of misinterpretation of harmless autonomic arousal as precursors to physical (e.g. heart attack) or psychological (e.g. insanity) emergency. Mixed research findings to date have provided equivocal support. A modified form of the Body Sensations Interpretation Questionnaire was used to investigate core assumptions of the model amongst 38 people with panic disorder (PD), 20 with non-clinical panic, 21 with social anxiety disorder, and 34 non-anxious controls. The PD group gave more harm-related interpretations of ambiguous internal stimuli than all other groups only when anxiety-related responses (e.g. “I'm going to panic”) were scored as harm, however there was no evidence that anxiety-related interpretations were masking perceived catastrophic physical or psychological outcomes. Despite this, people with PD rated harm and anxiety outcomes as more negative than non-anxious controls. Results failed to unequivocally support core assumptions of the model.

Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.

Optimizing prediction of binge eating episodes : a comparison approach to test alternative conceptualizations of the affect regulation model

Background : Although a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose–response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).

Methods : A sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.

Results : As hypothesized, the threshold approach was a better predictor than the linear dose–response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more  negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.

Conclusions : Present findings suggest that simple dose–response formulations for the association between  negative mood and onset of binge episodes miss vital aspects of this relationship. Most  notably, the impact of mood on binge eating appears to depend on whether a threshold value  of negative mood has been breached, and elevation in mood beyond this point may be useful  for clinicians and researchers to identify time to onset.

Synthesis of single-crystalline LiFePO4 with rhombus-like morphology

In this investigation, carbon-coated LiFePO4 cathode materials were synthesized with a facile hydrothermal method. The structure and electrochemical properties of the materials were investigated by X-ray diffraction (XRD), Roman, transmission electron microscopy-energy dispersive spectroscopy (TEM-EDS), and electrochemical impedance spectroscopy (EIS). By adjusting the mixing concentration of starting materials, a single-crystalline LiFePO4 with an anisotropic rhombus morphology (Space Group: Pmnb No. 62) were successfully synthesized. In addition, the carbon coated on the surface of LiFePO4 material prepared has a lower ID/IG (0.80), which indicates an optimized carbon structure with an increased amount of sp2-type carbon. Electrochemical performance test shows that the carbon-coated LiFePO4 cathode materials have an initial discharge capacity of 146 mAh g−1 at 0.2C.

Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.

Expected shannon entropy and shannon differentiation between subpopulations for neutral genes under the finite island model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information ("Shannon differentiation") between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Cryo-electron microscopy and single molecule fluorescent microscopy detect CD4 receptor induced HIV size expansion prior to cell entry

Viruses are often thought to have static structure, and they only remodel after the viruses have entered target cells. Here, we detected a size expansion of virus particles prior to viral entry using cryo-electron microscopy (cryo-EM) and single molecule fluorescence imaging. HIV expanded both under cell-free conditions with soluble receptor CD4 (sCD4) targeting the CD4 binding site on the HIV-1 envelope protein (Env) and when HIV binds to receptor on cellular membrane. We have shown that the HIV Env is needed to facilitate receptor induced virus size expansions, showing that the 'lynchpin' for size expansion is highly specific. We demonstrate that the size expansion required maturation of HIV and an internal capsid core with wild type stability, suggesting that different HIV compartments are linked and are involved in remodelling. Our work reveals a previously unknown event in HIV entry, and we propose that this pre-entry priming process enables HIV particles to facilitate the subsequent steps in infection.

Psychometric properties of a tool to measure nurses' attitudes to single checking medications: SCAMS-II

BACKGROUND AND AIMS: Single checking medications has been increasingly adopted over the past decade by nurses in Australian healthcare services. However, attitudes toward the practice of only one nurse checking medications remain unclear. The aim of this article is to report on the development, reliability, and validity of a tool to measure nurses' attitudes to single checking medications in a health service in which single checking has been in place for over a decade. METHODS: In a cross-sectional survey design, the Single Checking and Administration of Medications Scale (SCAMS-II) was used to measure the attitudes of 299 registered nurses (RNs) who were single checking medications in one metropolitan teaching hospital in Australia. Exploratory factor analysis was used to explore the dimensions that best represented the SCAMS-II. Cronbach's α was used to assess internal consistency of the identified subscales. To test the construct validity of the emergent questionnaire, Confirmatory Factor Analysis and Rasch analyses were performed. RESULTS: The psychometric properties of the SCAMS-II revealed 12 items with three reliable subscales: a five-item accountability model; a four-item efficiency model; and a three-item knowledge model. LINKING EVIDENCE TO ACTION: In settings where single checking is current practice, the SCAMS-II is recommended as a reliable tool to measure nurses' attitudes toward the single checking of medications. The findings from this study may assist healthcare organizations in the development of policy and procedure guidelines for the safe administration of medications.

Intellectual property and competition as complementary policies: a test using an ordered probit model

Este Artigo Testa a Proposição da Teoria Econômica de que Propriedade Intelectual e Defesa da Concorrência são Políticas Complementares. um Modelo Probit Ordenado é Utilizado para Estimar os Efeitos Marginais do Uso e Qualidade do Enforcement dos Direitos de Propriedade Intelectual em uma Medida da Gravidade dos Problemas Relacionados À Concorrência. os Resultados Obtidos Reforçam a Noção de que as Políticas de Concorrência e Propriedade Intelectual não são Contraditórias.