The adapted Autobiographical interview : A systematic review and proposal for conduct and reporting
- Authors: Miloyan, Beyon , McFarlane, Kimberley , Vasquez-Echeverria, Alejandro
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Behavioural Brain Research Vol. 370, no. (2019), p. 1-6
- Full Text: false
- Reviewed:
- Description: The adapted Autobiographical Interview (AI) is one of the most commonly used and widely cited measures of prospection in adult humans. The use of this instrument requires investigators to make many decisions that can influence the outcomes of studies. Here, we performed a systematic review of studies utilizing the adapted AI. We assessed and characterized the studies on various aspects of methodological quality and reporting. We then investigated and reported on several properties of adapted AI scores that have implications for their interpretation. We conclude by proposing Conduct and Reporting of Autobiographical Interview (CRAI) guidelines to contribute to the improvement of the reporting quality for studies that use the adapted AI, and hope that this will contribute to future efforts to validate this influential measurement instrument of prospection in humans.
Adult deterioration detection system (ADDS) : An evaluation of the impact on met and code blue activations in a regional healthcare service
- Authors: Missen, Karen , Porter, Joanne , Raymond, Anita , de Vent, Kerry , Larkins, Jo-Ann
- Date: 2018
- Type: Text , Journal article
- Relation: Collegian Vol. 25, no. 2 (2018), p. 157-161
- Full Text: false
- Reviewed:
- Description: Aims: To evaluate the impact of Acute Deterioration Detection System (ADDS) charts introduced to a regional healthcare service. Background: To assist health professionals in identifying essential elements for recognizing patient clinical deterioration, a national initiative introduced track and trigger observation charts, to hospitals in Australia. This study investigated whether the introduction of ADDS charts had an impact on the number of Medical Emergency Team (MET) and Code Blue activations at one regional healthcare service, according to their incident recording database. Method: A retrospective study of all Code Blue and MET activations was undertaken at a regional hospital, pre and post the introduction of ADDS charts in a two year period, June 2012 to June 2014. Results: There was a significant increase in MET activations from 5.91 to 11.27 per 1000 admissions (p < 0.01) after the implementation of ADDS charts. There was also an unexplained non-significant increase from 0.50 to 0.88 per 1000 admissions in the activations of Code Blue during this period (p = 0.05). It was also found that ADDS charts did not overly influence the activation criteria for calling a MET/Code Blue, except for an increase in reports of high heart rate and a decrease in the use of the criteria ‘worried’. Conclusion: The introduction of ADDS charts has provided health professionals with a clear track and trigger set of criteria, improving the detection of early signs of deterioration in patients. This study demonstrated an increase in activations as a result of the introduction of ADDS charts in one regional healthcare service.
Securing IoTs in distributed blockchain : analysis, requirements and open issues
- Authors: Moin, Sana , Karim, Ahmad , Safdar, Zanab , Safdar, Kalsoom , Ahmed, Ejaz , Imran, Muhammad
- Date: 2019
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 100, no. (2019), p. 325-343
- Full Text: false
- Reviewed:
- Description: IoTs are integrated, interconnected concepts of things or objects in our surroundings, with an essence of virtualization. The interconnectivity of the business world, health environments, smart home devices, and daily use gadgets takes place through the networks based on cloud infrastructure which is not restricted to jurisdictional, geographic, and national boundaries. However, the light-weight IoT devices come with a limited storage and processing capacity. Due to this limitation, the need for separate data storage arises so that data can be utilized in the future. These third-party storage services are provided at the cost of a user's privacy. Furthermore, the storage relies on a centralized database which is more open to attack due to its single point security breach chances. Furthermore, present IoT data is not trustworthy in the external environment, as data manipulation is lacking when data is shared with other parties. To overcome the above-mentioned limitations of IoT, the emerging secure decentralized storage technology; Blockchain, have begun to abandon the significant impact in the IoT with the enhancement of security and incorporating a large number of devices in the today's ecosystems. In this paper, we have performed a comprehensive literature review to show how well blockchain has transformed the smart environments connected with IoT sensors and the underlying issues for its adaptation. Further, a well-organized taxonomy is presented by highlighting the strengths, weaknesses, opportunities, and threats (SWOT) of blockchain based IoT environment. In addition to that, we have clearly presented the verities of blockchain applications such as bitcoin (earlier cryptocurrency used in blockchain) or ethereum (establish smart contracts) based works and pinpoint the necessities and security challenges. Moreover, we have highlighted the essential implementation requirements of blockchain in the IoTs. This paper is also equipped with a state-of-the-art framework of IoT while adopting security features and decentralized storage requirements of the blockchain. In the end, we have presented insightful challenges need to be addressed to obtain efficient, secure, and effective communication goals and to provide private and secure services for users as per their requirements. © 2019 Elsevier B.V.
Predicting the condensate viscosity near the wellbore by ELM and ANFIS-PSO strategies
- Authors: Mousazadeh, Fatemeh , Naeem, Mohammad , Daneshfar, Reza , Soulgani, Bahram , Naseri, Maryam
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Petroleum Science and Engineering Vol. 204, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software. © 2021 Elsevier B.V.
Direct contact ultrasound for fouling control and flux enhancement in air-gap membrane distillation
- Authors: Naji, Osamah , Al-juboori, Raed , Bowtell, Les , Alpatova, Alla , Ghaffour, Noreddine
- Date: 2020
- Type: Text , Journal article
- Relation: Ultrasonics Sonochemistry Vol. 61, no. (2020), p.
- Full Text: false
- Reviewed:
- Description: Air Gap Membrane distillation (AGMD) is a thermally driven separation process capable of treating challenging water types, but its low productivity is a major drawback. Membrane fouling is a common problem in many membrane treatment systems, which exacerbates AGMD's low overall productivity. In this study, we investigated the direct application of low-power ultrasound (8–23 W), as an in-line cleaning and performance boosting technique for AGMD. Two different highly saline feedwaters, namely natural groundwater (3970 μS/cm) and RO reject stream water (12760 μS/cm) were treated using Polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) membranes. Theoretical calculations and experimental investigations are presented, showing that the applied ultrasonic power range only produced acoustic streaming effects that enhanced cleaning and mass transfer. Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy (ATR FT-IR) analysis showed that ultrasound was capable of effectively removing silica and calcium scaling. Ultrasound application on a fouled membrane resulted in a 100% increase in the permeate flux. Cleaning effects accounted for around 30–50% of this increase and the remainder was attributed to mass transfer improvements. Contaminant rejection percentages were consistently high for all treatments (>99%), indicating that ultrasound did not deteriorate the membrane structure. Scanning Electron Microscopy (SEM) analysis of the membrane surface was used to confirm this observation. The images of the membrane surface demonstrated that ultrasound successfully cleaned the previously fouled membrane, with no signs of structural damage. The results of this study highlight the efficient and effective application of direct low power ultrasound for improving AGMD performance. © 2019 Elsevier B.V.
Transformer based deep intelligent contextual embedding for twitter sentiment analysis
- Authors: Naseem, Usman , Razzak, Imran , Musial, Katarzyna , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 113, no. (2020), p. 58-69
- Full Text: false
- Reviewed:
- Description: Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present DICET, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that DICET considerably outperforms the state of the art in sentiment classification. © 2020 Elsevier B.V.
Real-time localisation system for GPS-denied open areas using smart street furniture
- Authors: Nassar, Mohamed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2021
- Type: Text , Journal article
- Relation: Simulation Modelling Practice and Theory Vol. 112, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Wifi-based localisation systems have gained significant interest with many researchers proposing different localisation techniques using publicly available datasets. However, these datasets are limited because they only contain Wifi fingerprints collected and labelled by users, and they are restricted to indoor locations. We have generated the first Wifi-based localisation datasets for a GPS-denied open area. We selected a busy open area at Murdoch University to generate the datasets using so-called “smart bins”, which are rubbish bins that we enabled to work as access points. The data gathered consists of two different datasets. In the first, four users generated labelled WiFi fingerprints for all available Reference Points using four different smartphones. The second dataset includes 2450865 auto-generated rows received from more than 1000 devices. We have developed a light-weight algorithm to label the second dataset from the first and we proposed a localisation approach that converts the second dataset from asynchronous format to synchronous, applies feature engineering and a deep learning classifier. Finally, we have demonstrated via simulations that by using this approach we achieve higher prediction accuracy, with up to 19% average improvement, compared with using only the fingerprint dataset. © 2021 Elsevier B.V.
A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept
- Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Networks Vol. 205, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.
Differential treatment effects of an integrated motivational interviewing and exercise intervention on depressive symptom profiles and associated factors : a randomised controlled cross-over trial among youth with major depression
- Authors: Nasstasia, Yasmina , Baker, Amanda , Lewin, Terry , Halpin, Sean , Hides, Leanne
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Affective Disorders Vol. 259, no. (2019), p. 413-423
- Full Text: false
- Reviewed:
- Description: Background: Exercise is increasingly recognised as an efficacious intervention for major depressive disorder (MDD) but to our knowledge differential treatment effects on depressive symptom profiles (cognitive, somatic and affective) and associated changes in psychological, physiological and behavioural factors have not been examined among youth with MDD. Methods: Sixty-eight participants (mean age 20.8) meeting DSM-IV diagnostic criteria for MDD were randomised to an Immediate intervention or Control/delayed condition (n = 34 per group). The integrated intervention comprised an initial session of motivational interviewing (MI) followed by a 12-week, multi-modal exercise program. Changes in depressive symptom profiles were assessed with the Beck Depression Inventory-II (BDI-II) total score and factorial symptom subscales. Results: There were significant differential improvements in BDI-II total scores post-treatment among intervention participants, which were also observed across the cognitive and affective subscales. Individual BDI-II items from the cognitive subscale showing significant differential improvement related to negative self-concept, while those from the affective subscale related to interest/activation; the energy item within the somatic subscale also revealed significant differential improvement. Significant differential improvements were also observed in exercise participation, negative automatic thoughts, behavioural activation and bench press repetitions among intervention participants, which correlated significantly with depression improvements. Limitations: The exercise intervention was delivered in a supervised, group format and potential social meditators of change cannot be excluded. Conclusions: Exercise differentially effects depressive symptom profiles with similar antidepressant effects as would be expected from psychological therapies improving negative cognition and emotional health. © 2019. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Yasmina Nasstasia” is provided in this record**
On-farm trial on the effectiveness of the nitrification inhibitor DMPP indicates no benefits under commercial Australian farming practices
- Authors: Nauer, Philipp , Fest, Benedikt , Visser, Luke , Arndt, Stefan
- Date: 2018
- Type: Text , Journal article
- Relation: Agriculture, Ecosystems and Environment Vol. 253, no. (2018), p. 82-89
- Full Text: false
- Reviewed:
- Description: The trend of increasing nitrogen (N) fertilisation in commercial agriculture demands mitigation of negative impacts on the environment, such as emissions of the potent greenhouse gas nitrous oxide (N2O). Laboratory and controlled field experiments have demonstrated that the nitrification inhibitor 3,4-Dimethylpyrazole phosphate (DMPP) has the potential to effectively mitigate N2O emissions from dairy pasture and crop farming, and may increase yields. Yet, this has not been investigated in on-farm research trials under commercial production conditions. During the winter growing seasons 2014–2016 we performed an on-farm trial on five commercial broad-acre cropping and five dairy farms in North-East Victoria, Australia, to compare the performance of DMPP + urea (treatment) against conventional urea (control) fertiliser in mitigating N2O emissions and increasing crop and pasture yields. Application rate was fixed at the regional industry standard of 46 kg N ha‐1, yet timing, number of applications and all other management decisions were left to the judgement of the participating farmers. Emissions of N2O were highly variable over time and between farms. We recorded emission spikes of up to 250 g N2O-N ha
An improved memetic approach for protein structure prediction incorporating maximal hydrophobic core estimation concept
- Authors: Nazmul, Rumana , Chetty, Madhu , Chowdhury, Ahsan
- Date: 2021
- Type: Text , Journal article
- Relation: Knowledge-Based Systems Vol. 219, no. (2021), p. 104395
- Full Text: false
- Reviewed:
- Description: Protein Structure Prediction (PSP) from the primary amino acid sequence, even using a simplified Hydrophobic-Polar (HP) lattice model, continues to be extremely challenging. Finding an optimal conformation, even for a small sequence, by any of the currently known evolutionary approaches is computationally extensive and time consuming. Although Memetic Algorithms (MAs) have shown success in finding the optimal solution for PSP, no significant work on the incorporation of domain or problem specific knowledge into the search process to significantly improve their performance is reported. In this paper, we present an approach to incorporate such knowledge into the initial population to enhance the effectiveness of MA for PSP. The domain knowledge we propose to use is based on the concept of maximal ‘core’ formation by exploiting the fundamental property of the H residues to be at the core of the minimum energy optimal protein structure. A generic technique is proposed for estimating the maximal Hydrophobic core (H-core) in a protein sequence for 2D Square, 3D Cubic and a more complex and realistic 3D FCC (Face Centered Cubic) lattice models. Subsequently, the knowledge of this estimated core is incorporated in an MA. The experiments conducted using HP benchmark sequences for 2D Square, 3D Cubic and 3D FCC lattice models show that the proposed MA with the new core-based population initialization technique has superior performance to the existing methods in terms of convergence speed as well as minimal energy. © 2018 Elsevier B.V.
Multimodal memetic framework for low-resolution protein structure prediction
- Authors: Nazmul, Rumana , Chetty, Madhu , Chowdhury, Ashan
- Date: 2020
- Type: Text , Journal article
- Relation: Swarm and Evolutionary Computation Vol. 52, no. (Feb 2020), p. 14
- Full Text: false
- Reviewed:
- Description: In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the "nearest peak". The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.
Continuity of care experiences : a national cross-sectional survey exploring the views and experiences of Australian students and academics
- Authors: Newton, Michelle , Faulks, Fiona , Bailey, Carolyn , Davis, Jenny , Vermeulen, Monique , Tremayne, Anne , Kruger, Gina
- Date: 2022
- Type: Text , Journal article
- Relation: Women and Birth Vol. 35, no. 3 (2022), p. e253-e262
- Full Text: false
- Reviewed:
- Description: Background: Continuity of Care Experiences (CoCEs) are a component of all entry-to-practice midwifery programs in Australia and facilitate an understanding of the central midwifery philosophy of woman-centred care and continuity of the therapeutic relationship. The aim of this research was to explore how CoCEs are viewed and experienced by students and academics across Australia. Methods: Students enrolled in Australian midwifery programs and academics who teach into these programs were invited to participate in a cross sectional, web-based survey. Data were analysed using descriptive statistics and free text responses were analysed using content analysis. Findings: Four hundred and five students and 61 academics responded to the survey. The CoCE was viewed as a positive and unique learning experience, preparing students to work in midwifery-led continuity models and developing confidence in their midwifery role. Challenges in recruitment, participation in care, and balancing the workload with other course requirements were evident in reports from students, but less understood by academics. Significant personal impact on finances, health and wellbeing of students were also reported. Discussion: The value of CoCEs as an experiential learning opportunity is clear, however, many students report being challenged by elements of the CoCE within current models as they try to maintain study-work-life balance. Conclusion: Innovative course structure that considers and embeds the CoCE requirements within the curricula, in addition to a collective commitment from regulatory bodies, the maternity care sector and Universities to facilitate CoCEs for students may address some of the significant student impacts that are reported by this research. © 2021 Australian College of Midwives
Fracture analysis of cracked magneto-electro-elastic functionally graded materials using scaled boundary finite element method
- Authors: Nguyen, Duc , Javidan, Fatemeh , Attar, Mohammadmahdi , Natarajan, Sundararajan , Yang, Zhenjun , Ooi, Ean Hin , Song, Chongmin , Ooi, Ean Tat
- Date: 2022
- Type: Text , Journal article
- Relation: Theoretical and Applied Fracture Mechanics Vol. 118, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: This paper develops the scaled boundary finite element method to analyse fracture of functionally graded magneto-electro-elastic materials. Polygon meshes are employed to discretize the domain. No asymptotic solution, local mesh refinement or other special treatments around a crack tip are required to calculate the intensity factors. When the material gradients of the coefficients in the constitutive matrix are expressed as a series of power functions of the scaled boundary coordinates, the stiffness matrices can be integrated analytically. The formulation enables the generalized intensity factors of stress, electric displacement and magnetic induction fields along the radial direction to be represented analytically. This permits the calculation of the generalized intensity factors directly from the scaled boundary finite element solution of the singular stress, electric displacement and magnetic induction fields by following the standard stress recovery procedures in the finite element method. Several numerical benchmarks are presented to validate the proposed technique with the results reported in the literature. © 2022 Elsevier Ltd
Least square and Gaussian process for image based microalgal density estimation
- Authors: Nguyen, Linh , Nguyen, Dung , Nghiem, Truong , Nguyen, Thang
- Date: 2022
- Type: Text , Journal article
- Relation: Computers and Electronics in Agriculture Vol. 193, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: Efficiently monitoring microalgal density in real time is critical in closed systems of cultivating algae. In the monitoring methods proposed in the literature, image based techniques present practically potential since they are nondestructive and more biosecured. However, in the existing image analysis methods, parameters of the color-to-grayscale conversion formulae are predefined and only applicable to monitor some specific microalgae strains. Therefore, in this paper we propose a generic approach based on least square to optimize those parameters, which are data-driven and can be used to monitor any type of microalgae. More importantly, apart from the widely used linear regression paradigm, we propose a nonlinear regression model based on Gaussian process to better learn relationship between data representation of measured images and densities of microalgae. The nonlinear regression model is then utilized to efficiently estimate density of algal species. The proposed approach was evaluated in the real-world dataset of Chlorella vulgaris microalgae, where the obtained results as compared with those obtained by some existing techniques demonstrate its effectiveness. © 2022 Elsevier B.V.
The relationship between phobic anxiety and 2-year readmission after Acute Coronary Syndrome : What is the role of heart rate variability?
- Authors: O'Neil, Adrienne , Taylor, Barr , Hare, David , Thomas, Emma , Toukhsati, Samia , Oldroyd, John , Scovelle, Anna , Oldenburg, Brian
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Affective Disorders Vol. 247, no. (2019), p. 73-80
- Full Text: false
- Reviewed:
- Description: Objective: Phobic anxiety is a risk factor for poor prognosis following Acute Coronary Syndrome (ACS). A psychophysiological marker of vagal function, autonomic dysfunction may play a critical role in this relationship. The aim of the study was two-fold: to assess whether phobic anxiety was characterised by autonomic dysfunction (heart rate variability) in the short (1-month) and longer term (12-months) following ACS, and (ii) to quantify the extent to which HRV parameters modified the effect of phobic anxiety on all-cause hospital readmission over 2 years. Methods: The ADVENT study followed 416 ACS patients. At 1-month following discharge (T0), phobic anxiety and autonomic functioning were assessed using the Crown Crisp Index (CCI) and 11 indices of heart rate variability (HRV), respectively. HRV was measured again at 12-months (T1) (n = 359). Hospital readmission (all cause) was derived from an audit of hospital records by two medically trained research fellows. Generalised linear modelling (GLM) was used to first determine the association between CCI score at T0 and HRV parameters at T0 and T1. Binary logistic regression was used to measure the relationship between CCI scores and readmission (yes/no) and the extent to which HRV parameters modified this effect. Results: CCI scores were associated with 7 of the 11 indices of HRV: Average RR (ms), SDRR (ms), RMSSD (ms), SDSD (ms), pRR50 (%), LF Powers (ms2) and HF Powers (ms2) at T0 but not T1. CCI scores at T0 significantly predicted likelihood of readmission to hospital in the subsequent 2 year period. No parameter of HRV at T0 modified this effect. Limitations: We were unable to provide adjudicated major adverse coronary events outcome data, or account for changes in medication adherence, diet or physical activity. Conclusions: While phobic anxiety is associated with both reduced vagal function in the short term after an ACS event and 2 year all cause readmission, HRV does not appear to be the pathway by which phobic anxiety drives this outcome.
Exploring the existence and potential underpinnings of dog-human and horse-human attachment bonds
- Authors: Payne, Elyssa , DeAraugo, Jodi , Bennett, Pauleen , McGreevy, Paul
- Date: 2016
- Type: Text , Journal article , Review
- Relation: Behavioural Processes Vol. 125, no. (2016), p. 114-121
- Full Text: false
- Reviewed:
- Description: This article reviews evidence for the existence of attachment bonds directed toward humans in dog-human and horse-human dyads. It explores each species' alignment with the four features of a typical attachment bond: separation-related distress, safe haven, secure base and proximity seeking. While dog-human dyads show evidence of each of these, there is limited alignment for horse-human dyads. These differences are discussed in the light of the different selection paths of domestic dogs and horses as well as the different contexts in which the two species interact with humans. The role of emotional intelligence in humans as a potential mediator for human-animal relationships, attachment or otherwise, is also examined. Finally, future studies, which may clarify the interplay between attachment, human-animal relationships and emotional intelligence, are proposed. Such avenues of research may help us explore the concepts of trust and bonding that are often said to occur at the dog-human and horse-human interface. © 2015.
Do primary rainforest tree species recruit into passively and actively restored tropical rainforest?
- Authors: Pohlman, Catherine , Tng, David , Florentine, Singarayer
- Date: 2021
- Type: Text , Journal article
- Relation: Forest Ecology and Management Vol. 496, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Restoring tropical rainforests is becoming increasingly urgent. However, in most restoration plantings it is not possible to include the full suite of species found in the original rainforest. Full recovery of species composition thus depends on the dispersal and recruitment of species that are not planted. In many restoration projects, however, recruitment is dominated by a low diversity of regionally-abundant pioneer species and species with small, easily dispersed seeds. These species are characteristic of secondary rainforest and do not include the far more diverse suite of species characteristic of the original, primary rainforest. Such primary rainforest species are usually more vulnerable to the effects of fragmentation than disturbance-adapted pioneers and thus are of greater conservation concern, as well as being required for the full recovery of many important ecosystem functions. As restoring ecosystem processes is one of the central goals of restoration, this raises the question of which, if any, of the available rainforest restoration methods may be used to promote the recruitment of primary rainforest species. We compared the species composition and functional group composition of recruited trees and shrubs in a 25-year-old restoration experiment with those of the originally planted trees, and with nearby primary rainforest and secondary rainforest reference sites in an area of upland rainforest in north-eastern Australia. Our objective was to compare the performance of four commonly-used restoration methods: (i) unassisted (passive) regeneration, (ii) Pioneer Monoculture plantings, (iii) Framework Method plantings, and (iv) Maximum Diversity plantings. The species composition and functional group composition of recruited individuals within all treatments were similar to those of secondary rainforest and highly dissimilar to both primary rainforest and plantations. Pioneer species, species with small, biotically-dispersed diaspores, and canopy trees were over-represented among both recruited individuals and in secondary rainforest. Conversely, climax species, species with large, biotically-dispersed diaspores, species with abiotically-dispersed diaspores, and understorey trees were under-represented among both recruited individuals and secondary rainforest. Restoration treatments had little effect on the species or functional group composition of recruited individuals. Our results indicate that species from nearby primary rainforest almost completely failed to recruit into any of the restoration treatments. We argue that this failure was most likely due to the absence of frugivores able to disperse larger diaspores from both secondary forest and restored forest in our study site. Further direct management intervention will be required to restore primary rainforest plant species to restored forests in this region. © 2021 Elsevier B.V.
Social R&D : does academic freedom contribute to improved societal outcomes?
- Authors: Posso, Alberto , Zhang, Quanda
- Date: 2023
- Type: Text , Journal article
- Relation: Information Economics and Policy Vol. 63, no. (2023), p.
- Full Text: false
- Reviewed:
- Description: The economics literature views R&D as an important conduit for growth because it generates new ideas that can be translated into technological innovations. Some of this R&D occurs in universities, making academic freedom an important part of this process. This literature ignores the potential role that academic research in the social sciences plays toward achieving non-commercial societal outcomes. We bridge this gap by proposing that academia generates social R&D. We posit that greater degrees of academic freedom allow for social R&D to flourish and be transformed into policies that improve societal conditions. We test our hypothesis by studying the relationship between academic freedom and inequality using panel data of 132 countries over the 1967–2018 period. We measure academic freedom using an index developed by the V-Dem Institute. Our econometric analysis suggests that an increase in the index is associated with a decrease in inequality. We employ instrumental variable and interactive fixed effects techniques to try to lend support to the causal relationship between academic freedom and inequality. We argue that this negative relationship can be explained by academia, predominantly the social sciences, exerting pressure on governments to enact policies that redistribute wealth. We find evidence in support of this mechanism using data from other sources. © 2023 Elsevier B.V.
A perspective on genomic-guided anthelmintic discovery and repurposing using Haemonchus contortus
- Authors: Preston, Sarah , Jabbar, Abdul , Gasser, Robin
- Date: 2016
- Type: Text , Journal article
- Relation: Infection, Genetics and Evolution Vol. 40, no. (2016), p. 368-373
- Full Text: false
- Reviewed:
- Description: High-throughput molecular and computer technologies have become instrumental for systems biological explorations of parasites. Investigating the genomes and transcriptomes of different developmental stages of parasitic nematodes can provide insights into gene expression, regulation and function in the parasite, which is a significant step toward understanding their biology as well as host interactions and disease. This article covers aspects of a talk given at the MEEGID XII conference in Thailand in 2014. Here, we refer to recent studies of the genomes and transcriptomes of socioeconomically important parasitic nematodes of animals; provide an account of the barber's pole worm (Haemonchus contortus) and emerging drug resistance problems in this and related worms; we also propose a genomic-guided drug discovery and repurposing approach, involving the prediction of the druggable genome, prioritization of drug targets, screening of compound libraries against H. contortus and, briefly, a hit-to-lead optimization approach. We conclude by indicating prospects that molecular tool kits for nematodes provide to the scientific community for future comparative genomic, genetic, proteomic, metabolomic, evolutionary, biological, ecological and epidemiological investigations, and as a basis for biotechnological outcomes and translation. © 2015 Elsevier B.V.