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Quantifying AI Impact in Post-Compromise Incident Response
Full text linkThis paper presents metrics for measuring the operational impact of artificial intelligence in cybersecurity incidents. It introduces five practical metrics that assess how AI influences the speed, scale, and visibility of post-compromise activity. The metrics are evaluated through two simulated scenarios representing different levels of organisational maturity and security capability. Results show that artificial intelligence can accelerate attacker actions and challenge traditional detection and response workflows. This highlights the need for measurable indicators of AI's impact in cyber-attacks. The metrics support readiness assessments in enterprise environments with increasing integration of AI systems
Future climate change increases the risk of wheat yield loss due to agricultural drought in southeastern Australia
No full textAgricultural drought poses a significant threat to food security and human sustainability by reducing crop yields, and it is projected to intensify in the future due to ongoing global warming and increasing rainfall variability. As a key contributor to the worldwide food supply, the New South Wales (NSW) wheat belt in southeastern Australia is highly exposed to drought-related risks due to its prevailing dry climate and reliance on the rain-fed cropping system. Yet the future impacts of agricultural drought on regional wheat yields remain poorly quantified under climate change. This study aims to evaluate the risk of wheat yield losses induced by agricultural drought under different climate scenarios, focusing on its spatial distribution and temporal evolution across the NSW wheat belt. We integrated a process-based crop simulation model with a probabilistic approach to assess wheat yield loss risk under future climate scenarios. Agricultural Production System sIMulator (APSIM) model was forced with climate data from multiple Global Climate Models (GCMs), enabling the simulation of long-term wheat yield and plant available water (PAW). The simulated PAW values were standardized to derive SPAWI (Standardized Plant Available Water Index), which was used to characterize drought conditions. Copula functions were then utilized to construct the joint probability distribution between wheat yield and SPAWI, allowing the calculation of yield loss probabilities and the identification of drought trigger thresholds. There was a rising trend in agricultural drought frequency across the wheat belt, especially under the Hot/Dry scenarios. Regional results showed elevated wheat yield loss probabilities in the future, approaching 10 % in the drier and warmer areas. Moreover, the drought index thresholds for triggering wheat yield loss were higher over dry areas but lower in the wet region. Uncertainty attribution analysis identified GCM selection as the primary source of yield loss probability change in arid regions, while in wet regions, the choice of copula function played a more critical role. Our findings show a rising risk of wheat yield loss in the NSW wheat belt under future climate scenarios and reveal substantial spatiotemporal heterogeneity in yield impacts. The results offer critical geographic insights for supporting localized adaptation strategies and evidence-based agricultural planning under drought conditions in the future
Zebrafish Models for Drug Discovery and Therapeutic Validation against Non-Tuberculous Mycobacteria.
No full textThe incidence of non-tuberculous mycobacteria (NTM) is increasing globally, often surpassing the incidence of new tuberculosis (TB) cases in developed countries. Most NTM are environmental organisms; however, there are a number of opportunistic and pathogenic species that can cause severe infections in animals and humans. Many NTM are intrinsically resistant to anti-TB therapies and are incredibly difficult to treat, resulting in poor treatment outcomes for these patients. Recent advances in preclinical animal models such as the zebrafish models have led to the discovery of highly active antimicrobial and host-directed therapies (HDTs) targeting NTM infections that can be applied to treat human infections. Here, we summarize recent progress and technological advancements in the discovery and development of antimicrobial drugs and HDTs that have been applied to NTM zebrafish infection models. We highlight the future directions for this increasingly applicable animal model for the discovery of next-generation therapies to treat NTM diseases
Research on climate change and mental health in immigrants is urgently needed: A systematic scoping review
No full textIntroduction: Globally, climate change is an imminent threat to physical and mental health. Climate-related disasters are predicted to increase in frequency, impacting the stability of and access to social systems and public infrastructure, adversely affecting health and well-being. Immigrant populations may be particularly vulnerable to climate change-related mental health impacts. The bidirectional relationship between climate change and migration infers that climate change-related health threats will further influence increasing migration rates. However, there is limited research that explores mental health risk factors and adaptation and mitigation strategies associated with climate change for immigrants. Methods: A scoping review was conducted based on a systematic searching strategy. The study aimed to identify and synthesise existing evidence to better understand the impact of climate change on the mental health of immigrant populations, and provide recommendations for future research and practice. Results: Findings are limited by the quality and depth of existing literature on the topic, as only eight original publications were identified for inclusion in the scoping review, all of which were either qualitative by design or perspective pieces. There is a paucity of evidence on the mental health outcomes of immigrant populations, limiting the recommendations for improving climate-related disaster preparedness and response efforts for immigrants. Conclusion: Future research and the development of data collection systems that capture health indicators of immigrants are needed to assess immigrant vulnerability to climate-related mental health outcomes
Can we prepare young female players for heading in football? The feasibility and acceptability of HeaderPrep.
Full text linkThis study investigated the feasibility and acceptability of a novel programme designed to prepare players to learn heading in football (HeaderPrep). Forty-five players from four different girls' teams (under-11, under-12, under-13, under-15) and five coaches completed the programme over six weeks followed by completion of an evaluation survey. Our findings suggest that the programme enhanced players' confidence in heading the ball. Additionally, both players and coaches observed improvements in heading skill development. Most players (84.4%) also stated they would recommend HeaderPrep to others and found it fun. HeaderPrep may be a feasible introduction prior to starting formal heading training in football
Organ-on-a-chip in the diagnosis and treatment of chronic respiratory disorders and its application to advanced drug delivery systems
No full textThe advent of organ-on-a-chip technology has transformed the landscape of biomedical research, offering great potential for the advancement of diagnostic and therapeutic approaches in the realm of chronic respiratory disorders. Organ-on-a-chip constitutes a sophisticated three-dimensional microfluidic system meticulously replicating the cellular architecture and biological environment of specific organs. Remarkably, this technology has seen substantial development and exploration over the past decade, particularly in simulating various disease states within organs like the lungs. Organ-on-a-chip, as a state-of-the-art method, faithfully emulates the intricate microenvironment of the lung, resulting in more precise disease modeling and drug testing. The integration of artificial intelligence into this system elevates its capabilities, enhancing diagnostic accuracy and streamlining data management. This chapter offers a comprehensive exploration of the applications of organ-on-a-chip platforms in the context of chronic respiratory disorders, with a particular emphasis on their pivotal role in advancing drug delivery systems. Furthermore, the chapter delves into the intricacies of the challenges and opportunities involved in translating organ-on-a-chip research findings into clinical applications for the management of chronic respiratory disorders
Treatment of hoarding disorder in a patient with heart failure: A case report.
No full textHoarding disorder (HD) is characterized by an accumulation of possessions owing to acquisition behaviors or absence of discarding, resulting in clutter severe enough to cause emotional distress, impair functioning, and preclude the use of living spaces for their intended purposes. HD is associated with significant psychiatric and physical health comorbidities. Evidence demonstrates an increased cardiovascular response, high prevalence of heart disease, and sudden cardiac death in patients with HD and yet treatment outcomes for patients with comorbid cardiovascular diseases remain unreported. A psychology referral was made for a patient with heart failure (HF) who underwent a structured clinical interview within their domicile and met criteria for adolescent-onset HD (27-year history). Treatment outcomes for this case are described, as well as the cognitive-behavioral therapy (CBT) modifications required for the patient, living in squalor and facing eviction. Results demonstrated modest improvements in HD symptoms from pretreatment to posttreatment. To ensure HF patients are involved in sorting/discarding tasks during CBT, modifications are necessary to compensate for high fatigability and dizziness to reduce the risk for serious adverse events including syncope and falls
Robust Learning under Hybrid Noise
No full textFeature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called F eature and L abel R ecovery (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises
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