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Plasticity of enteric neurotransmission varies during day-night cycles and with feeding state
No full textThe circadian cycle is a fundamental biological rhythm that governs many physiological functions across nearly all living organisms. In the gastrointestinal tract, activities such as gut motility, hormone synthesis, and communication between the gut, central nervous system, and microbiome all fluctuate in alignment with the circadian cycle. The enteric nervous system (ENS) is critical for coordinating many of these activities; however, how its activity is governed by the circadian cycle remains unknown. In this study, we used live calcium imaging to examine alterations in enteric neurotransmission during the 24-h day/night cycle in mice. In addition, given the role of food timing as a potent circadian entrainer, we also investigated the impact of an acute 13-h fast on ENS activity. Our findings reveal that enteric neuronal activity typically increases during the dark phase but shifts to the light phase following an acute fast. Importantly, these changes in neuronal activity were not accompanied by alterations in the gene expression of associated neurotransmitter receptors.NEW & NOTEWORTHY Neuronal activity in the enteric nervous system changes during the 24-h day/night cycle, with increased neuronal function detected at night when mice are feeding and active. However, following an acute fast, neuronal sensitivity becomes more pronounced during the day. These changes in neuronal function did not correlate with changes in neurotransmitter receptor gene expression levels
Ribosome Biogenesis and Function in Cancer: From Mechanisms to Therapy
No full textRibosome biogenesis is a highly coordinated, multi-step process that assembles the ribosomal machinery responsible for translating mRNAs into proteins. It begins with the rate-limiting step of RNA polymerase I (Pol I) transcription of the 47S ribosomal RNA (rRNA) genes within a specialised nucleolar region in the nucleus, followed by rRNA processing, modification, and assembly with ribosomal proteins and the 5S rRNA produced by Pol III. The ribosomal subunits are then exported to the cytoplasm to form functional ribosomes. This process is tightly regulated by the PI3K/RAS/MYC oncogenic network, which is frequently deregulated in many cancers. As a result, ribosome synthesis, mRNA translation, and protein synthesis rates are increased. Growing evidence supports the notion that dysregulation of ribosome biogenesis and mRNA translation plays a pivotal role in the pathogenesis of cancer, positioning the ribosome as a promising therapeutic target. In this review, we summarise current understanding of dysregulated ribosome biogenesis and function in cancer, evaluate the clinical development of ribosome targeting therapies, and explore emerging targets for therapeutic intervention in this rapidly evolving field
Exploring methodologies for establishing prevalence of deafblindness in children: A scoping review
No full textDeafblindness refers to a functional restriction of both hearing and vision, and presents at all ages. Determining prevalence of deafblindness, particularly in children, is challenging. The aim of this review was to explore and assess methodologies previously used to determine the prevalence of childhood deafblindness in both peer-reviewed and grey literature. Five databases were included in the search – Medline (OVID), PubMed, Scopus, CINAHL, and PsycINFO. Thirteen peer-reviewed articles and 11 documents from the grey literature met inclusion criteria for the review. In exploring the literature on deafblindness in children, it is evident that the characteristics and needs of this population are not well described. Approaches adopted by researchers show inconsistencies in how deafblindness is defined, assessed and diagnosed, making comparison challenging. To understand the needs of this group, it is critical that the childhood deafblind population is accurately described. Recommendations are made for the assessment of the population of children with deafblindness
Learning with place as a catalyst for action
No full textIn response to dominant discourses of quality and an over-reliance on humancentric practice, the Learning with Place framework emerges as an innovative way to rethink practices, structures, and policies within education and beyond. ‘Learning with Place’ views the local Place as agentic, recognising Place as inclusive of local First Nations knowledges and stories, histories and the more-than-human (for example, landforms, waterways, animals, insects, flora, and fauna). Through ‘Learning with Place’, deep relationships with the local Place are generated and these relationships become the catalyst for actions and decision-making regarding caring for/with local Place. This article offers an example of ‘Learning with Place’ in action through an early childhood teacher education program and shares ways in which the framework can be utilised in multiple contexts and disciplines
Average mutual information for random fermionic Gaussian quantum states
No full textStudying the typical entanglement entropy of a bipartite system when averaging over different ensembles of pure quantum states has been instrumental in different areas of physics, ranging from many-body quantum chaos to black hole evaporation. We extend such analysis to open quantum systems and mixed states, where we compute the typical mutual information in a bipartite system averaged over the ensemble of mixed Gaussian states with a fixed spectrum. Tools from random matrix theory and determinantal point processes allow us to compute arbitrary k-point correlation functions of the singular values of the corresponding complex structure in a subsystem for a given spectrum in the full system. In particular, we evaluate the average von Neumann entropy in a subsystem based on the level density and the average mutual information. Those results are given for finite system size as well as in the thermodynamic limit
Breastfeeding and the milk resistome shape the establishment and transmission of antibiotic resistance genes in the infant gut microbiome
No full textThe infant resistome, the collection of antimicrobial resistance genes (ARGs) of newborns, is critical for gut microbiota establishment. Using metagenomic sequencing data, we analyzed various 1-week and 1-month postpartum samples to study infant resistome establishment, ARG transmission, and its impact on functional redundancy of the microbiota. A total of 431 samples were analyzed; infant stools (1-week, n = 119; 1-month, n = 119), maternal stools (1-month postpartum, n = 120), and breastmilk (1-month postpartum, n = 73). Breastfeeding correlated with increased functional redundancy and altered bacterial-ARG co-occurrence networks in the infant resistome. Escherichia coli dominated early resistome dynamics with a higher abundance correlating with reduced functional redundancy. Bifidobacterium longum exhibited a consistent negative association with 21 ARGs at one-month in breastfed infants, while four negative relationships between ARGs and Bifidobacterium bifidum were observed in formula-fed infants. ARG transmission via breastmilk appears to be gene-specific, with the quinolone resistance gene sdrM likely transmitted under maternal antibiotic use. Delivery mode modulated the microbial environment in ways that interact with resistome structure and changing functional redundancy, particularly through genera like Staphylococcus and Streptococcus. These findings highlight the role of early feeding practices in resistome development and propose functional redundancy as a key ecological framework for understanding infant gut resistome dynamics
684. Investigating the role of orexin in stress-induced binge eating in female mice
No full textAbstract
Background
Binge eating is a core feature of both bulimia nervosa and binge eating disorder, which impacts approximately 4-5% of adults, globally. Stress has been identified as a major antecedent to binge eating episodes, particularly in women. However, further research is needed to identify the neurobiological mechanisms driving female, stress-induced binge eating and thus develop effective pharmacological treatments. The orexin neuropeptide system is known to play a critical role in both stress and feeding-related behaviours, hence represents a promising candidate for further research.
Aims & Objectives
We aimed to investigate the role of orexin in female stress-induced binge eating using a mouse model. We hypothesised that orexin neurons would be recruited as a result of this behaviour, and that administration of an orexin-1 receptor antagonist (SB-334867) would reduce stress-induced binge eating.
Method
Our model comprised of repeated cycles of exposure to a mild stressor and intermittent access to palatable food to induce binge-like eating in female mice. Animals were administered either SB-334867 (15 mg/kg, s.c.) or vehicle (5% DMSO in saline) prior to stress binge testing. Immunohistochemistry was performed to assess both orexin levels in the lateral hypothalamus and Fos expression throughout the brain (Fos is a marker of neuronal activity).
Results
Immunohistochemistry revealed activation of lateral hypothalamic orexin neurons in stress binge mice compared to controls and a specific recruitment of orexin immunoreactive cells. Further, systemic antagonism of the orexin-1 receptor with SB-334867 reduced stress-induced binge eating. This effect was associated with reduced activation of the nucleus accumbens core, and increased activation of the bed nucleus of the stria terminalis (BNST) compared to vehicle-treated mice.
Discussion & Conclusions
Our data implicates orexin signaling through the orexin-1 receptor in stress-induced binge eating, likely via a network involving components of the limbic system (nucleus accumbens core and BNST). Further, our results indicate that repeated cycles of stress-induced binge eating recruit the orexin system (i.e. evidencing engagement of the orexin ‘reserve’). Hence, the orexin system may represent an exciting target for pharmacotherapeutic development
Mixed Hodge modules and real groups
No full textLet G be a complex reductive group, θ:G→G an involution, and K=Gθ. In [29], W. Schmid and the second named author proposed a program to study unitary representations of the corresponding real form GR using K-equivariant twisted mixed Hodge modules on the flag variety of G and their polarizations. In this paper, we make the first significant steps towards implementing this program. Our first main result gives an explicit combinatorial formula for the Hodge numbers appearing in the composition series of a standard module in terms of the Lusztig-Vogan polynomials. Our second main result is a polarized version of the Jantzen conjecture, stating that the Jantzen forms on the composition factors are polarizations of the underlying Hodge modules. Our third main result states that, for regular Beilinson-Bernstein data, the minimal K-types of an irreducible Harish-Chandra module lie in the lowest piece of the Hodge filtration of the corresponding Hodge module. An immediate consequence of our results is a Hodge-theoretic proof of the signature multiplicity formula of [2], which was the inspiration for this work
Growing Up Online Through Divorce: Child Influencers and Family Change in Indonesia
No full textDrawing upon divorce cases of Indonesian celebrities with children, I explore the growing online visibility of childhood during and after parental separation. Using a socio-demographic perspective, I examine how rising divorce rates in Indonesia intersect with the increasing portrayals of diverse family structures on social media like Instagram. On one hand, the online visibility of child influencers from divorced celebrity families challenges hegemonic portrayals of the ideal nuclear family, once propagated by the New Order government before its fall in 1998. By amplifying fragments of complex household structures, these child influencers, along with those who have since grown up, have the potential to shape public attitudes and foster greater acceptance of family diversity. On the other hand, it remains uncertain whether this online visibility reduces or inadvertently reinforces the stigma surrounding divorce and growing up through divorce
AI, machine learning and BIM for enhanced property valuation: Integration of cost and market approaches through a hybrid model
No full textAccurate property valuation is essential for real estate market stability, housing affordability and financial decision-making. However, traditional valuation methods face key limitations. The market approach, reliant on comparable sales data, is prone to subjectivity and data availability constraints. The income approach relies on stable rental income streams, which are often unavailable for newly built dwellings in volatile rental markets. And, the cost approach, based on the Depreciated Replacement Cost (DRC) method, neglects broader market influences by focusing solely on property characteristics. Despite advancements in Automated Valuation Models (AVMs) using Machine Learning (ML), these models remain sensitive to market fluctuations and lack integration with 3D property characteristics. To address these challenges, this study proposes a hybrid Artificial intelligence (AI) and Building Information Modeling (BIM)-driven property valuation model, integrating the DRC method with market-based valuation adjustments using ML, Natural Language Processing (NLP) and BIM 3D models. The framework consists of several key stages, including mass land valuation using ML techniques, automated construction cost estimation through BIM-based Quantity Take-Off (QTO) and NLP-based cost-matching, dynamic depreciation assessment via BIM-integrated maintenance management, entitlement calculation using optimization techniques, and market impact assessment through ML-driven modeling. The methodology was tested on a high-rise residential building in Melbourne, Australia, and the results demonstrated high accuracy, with estimated property values closely aligning with recent market transactions. The estimated values for one-bedroom and two-bedroom units were 100 % within the range of recent market transactions, and the estimate for the three-bedroom units showed only a 0.057 % deviation from the actual market value. The study advances the digital transformation of property valuation, showcasing how AI, ML and BIM enhance automation, accuracy and efficiency. These findings hold significant implications for the real estate sector, offering a scalable and adaptable framework for industry adoption