19683 research outputs found
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Adaptive context-aware access control for IoT environments leveraging fog computing
No full textThe increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.</p
Pollination by multiple species of nectar foraging Hymenoptera in Prasophyllum innubum, a critically endangered orchid of the Australian Alps
No full textContext: Australia has numerous threatened species of terrestrial orchid, with a particularly high incidence of rarity in the genus Prasophyllum R.Br. Although there has been research on mycorrhizal associations and propagation, little is known about the reproductive ecology of threatened Prasophyllum. Understanding which animals are responsible for pollination and the impact of herbivores on reproduction may inform conservation actions. Aims: For the nationally Critically Endangered Prasophyllum innubum, we aimed to determine the pollinator species, test for self-pollination, quantify levels of reproductive success and herbivory, and identify herbivores. Methods: Pollinator observations were undertaken at wild populations of P. innubum, whereas an experiment testing for self-pollination was undertaken in shadehouse conditions. We quantified reproductive success and herbivory at two populations and attempted to identify herbivores using game cameras. Key results: Pollination occurred via three species of bee and a sphecid wasp, all of which attempted feeding on floral nectar. Fruit set averaged 72–84% at wild sites, whereas only 6% of flowers set fruit via self-pollination when insects were excluded. Just 4% of inflorescences were completely consumed by herbivores, and no herbivory was captured on camera. Conclusions: P. innubum has a generalist rewarding pollination system that confers high levels of reproductive success, with herbivory having little impact on reproduction. Implications: Pollinator availability is unlikely to restrict conservation translocation site selection of P. innubum because of a generalist pollination system. If herbivores are a threat for this species, it is likely to be through alteration of habitat rather than direct grazing.</p
Accuracy of Provider Predictions of Viral Suppression Among Adolescents and Young Adults With HIV in an HIV Clinical Program
No full textBackground: Providers caring for adolescents and young adults with HIV (AYA-HIV) mostly base their adherence counseling during clinical encounters on clinical judgment and expectations of patients’ medication adherence. There is currently no data on provider predictions of viral suppression for AYA-HIV. We aimed to assess the accuracy of provider predictions of patients’ viral suppression status compared to viral load results. Methods: Providers caring for AYA-HIV were asked to predict the likelihood of viral suppression of patients before a clinical encounter and give reasons for their predictions. Provider predictions were compared to actual viral load measurements of patients. Patient data were abstracted from electronic health records. The final analysis included 9 providers, 28 patients, and 34 observations of paired provider predictions and viral load results. Results: Provider prediction accuracy of viral suppression was low (59%, Cohen's Kappa = 0.16). Provider predictions of lack of viral suppression were based on nonadherence to medications, new patient status, or structural vulnerabilities (e.g., unstable housing). Anticipated viral suppression was based on medication adherence, history of viral suppression, and the presence of family or other social forms of support. Conclusions: Providers have difficulty accurately predicting viral suppression among AYA-HIV and may base their counseling on incorrect assumptions. Rapid point-of-care viral load testing may provide opportunities to improve counseling provided during the clinical encounter
Improving transformation and regeneration efficiency in medicinal plants: Insights from other recalcitrant species
No full textMedicinal plants are integral to traditional medicine systems worldwide, being pivotal for human health. Harvesting plant material from natural environments, however, has led to species scarcity, prompting action to develop cultivation solutions that also aid conservation efforts. Biotechnological tools, specifically plant tissue culture and genetic transformation, offer solutions for sustainable, large-scale production and enhanced yield of valuable biomolecules. While these techniques are instrumental to the development of the medicinal plant industry, the challenge of inherent regeneration recalcitrance in some species to in vitro cultivation hampers these efforts. This review examines the strategies for overcoming recalcitrance in medicinal plants using a holistic approach, emphasizing the meticulous choice of explants (e.g. embryonic/meristematic tissues), plant growth regulators (e.g. synthetic cytokinins), and use of novel regeneration-enabling methods to deliver morphogenic genes (e.g. GRF/GIF chimeras and nanoparticles), which have been shown to contribute to overcoming recalcitrance barriers in agriculture crops. Furthermore, it highlights the benefit of cost-effective genomic technologies that enable precise genome editing and the value of integrating data-driven models to address genotype-specific challenges in medicinal plant research. These advances mark a progressive step towards a future where medicinal plant cultivation is not only more efficient and predictable but also inherently sustainable, ensuring the continued availability and exploitation of these important plants for current and future generations.</p
The sexual exploitation of pornography, prostitution, and trafficking
No full textNo description supplied</p
Subsidising Extraction: Care at Work in Zambia's Copper Mines
No full textNo description supplied.</p
Characterization of an Estrogen Receptor α-Selective <sup>18</sup>F-Estradiol PET Tracer
No full textObjective: Conventional imaging of cancer with modalities such as computed tomography or magnetic resonance imaging provides little information about the underlying biology of the cancer and consequently little guidance for systemic treatment choices. Accurate identification of aggressive cancers or those that are likely to respond to specific treatment regimens would allow more precisely tailored treatments to be used. The expression of the estrogen receptor α subunit is associated with a more aggressive phenotype, with a greater propensity to metastasize. We aimed to characterize the binding properties of an 18 F-estradiol positron emission tomography (PET) tracer in its ability to bind to the α and β forms of estrogen receptors in vitro and confirmed its binding to estrogen receptor α in vivo. Methods: The 18 F-estradiol PET tracer was synthesized and its quality confirmed by high-performance liquid chromatography. Binding of the tracer was assessed in vitro by saturation and competitive binding studies to HEK293T cells transfected with estrogen receptor α ( ESR1 ) and/or estrogen receptor β ( ESR2 ). Binding of the tracer to estrogen receptor α in vivo was assessed by imaging of uptake of the tracer into MCF7 xenografts in BALB/c nu/nu mice. Results: The 18 F-estradiol PET tracer bound with high affinity (94 nM) to estrogen receptor α, with negligible binding to estrogen receptor β. Uptake of the tracer was observed in MCF7 xenografts, which almost exclusively express estrogen receptor α. Conclusion: 18 F-estradiol PET tracer binds in vitro with high specificity to the estrogen receptor α isoform, with minimal binding to estrogen receptor β. This may help distinguish human cancers with biological dependence on estrogen receptor subtypes.</p
Data set for systematic review and meta analysis entitled: The Effect of Chronic Pain on Memory: A Systematic Review and Meta-Analysis Exploring the Impact of Nociceptive, Neuropathic and Nociplastic Pain
No full textChronic pain is becoming increasingly prevalent in modern society. Much research to date has focussed on the physical symptoms of pain associated with various conditions, yet living with chronic pain is also known to impact an individual’s quality of life, social relationships and cognition. Among cognition, memory is particularly vulnerable to outside factors, yet our understanding of the impact of pain on memory is inconclusive. This systematic review and meta-analysis examined the association between chronic pain type and memory performance. Chronic pain samples were classified as nociceptive, neuropathic or nociplastic and were compared to healthy controls. Studies were sourced from Embase, Web of Science, MEDLINE, PubMed, PsycINFO, Scopus and CINAHL databases between December 2023 and July 2024. A total of 15 studies with 1865 participants were included (106 who experienced chronic nociceptive pain, 315 who experienced chronic neuropathic pain, 589 who experienced chronic nociplastic pain and 855 healthy controls). The studies were assessed for risk of bias and all studies included were considered to be good or strong. Results indicated that individuals with nociceptive and nociplastic pain had impaired memory performance (for short-term, working and long-term memory) compared to healthy controls. The same was not true for individuals with neuropathic pain. This indicates that the type of pain one experiences impacts memory performance. This has profound implications both clinically and with regard to research and offers a new lens for how we can consider chronic pain when trying to understand the impact on cognition.</p
Optimising desired gain indices to maximise selection response
No full textIntroduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where “desired gain selection indices” come in handy, which can yield optimal gains in each trait based on the breeder’s prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option
MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations
No full textMalware attacks continue to evolve, making detection challenging for traditional static and dynamic analysis techniques. On the other hand, memory analysis provides valuable behavioral insights, but prior research lacks temporal evaluations which are critical for robust detection of new malware variants over time. This paper presents MeMalDet, a novel memory analysis-based malware detection technique using deep autoencoders and stacked ensemble learning. We introduce an improved dataset with temporal attributes enabling more realistic evaluations of memory-based malware detection techniques under concept drift (temporal data split). MeMalDet extracts optimal features from memory dumps using deep autoencoders in an unsupervised manner, avoiding manual feature engineering. A stacked ensemble of supervised classifiers then performs highly accurate malware detection. Extensive experiments on our improved large-scale public dataset demonstrate MeMalDet's ability to maintain high performance when detecting obfuscated malware under temporal splits. We achieve up to 98.82% accuracy and 98.72% F1-score in detecting previously unseen advanced obfuscated malware, significantly improving upon state-of-the-art memory analysis-based malware detection techniques. The improved dataset enables temporally robust evaluations, which is a novel contribution. MeMalDet combines the benefits of representation learning and supervised machine learning ensemble classification for effective malware detection over time using memory analysis. This research provides a new capability for identifying evasive modern malware and combating evolving real-world threats