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Local glutamate-glutamine cycling underlies presynaptic ATP homeostasis
Full text linkPresynaptic axon terminals maintain in their cytosol an almost constant level of adenosine triphosphate (ATP) to safeguard neurotransmission during varying workloads. In the present study, it is argued that the vesicular release of neurotransmitter,and the recycling of transmitter via astrocytes, may itself be a mechanism of ATP homeostasis.In a minimal metabolic model of a presynaptic axon bouton, the accumulation of glutamate into vesicles and the activity-dependent supply of its precursor glutamine by astrocytes generated a steady-state level of ATP that was independent of the workload. When the workload increased, an enhanced supply of glutamine raised the rate of ATP production through the conversion of glutamate to the Krebs cycle intermediate alpha-ketoglutarate. The accumulation and release of glutamate, on the other hand, acted as a leak that diminished ATP production when the workload decreased. The fraction of ATP which the axon spent on the release and recycling of glutamate was small (4.7 %), irrespective of the workload. Increasing this fraction enhanced the speed of ATP homeostasis, and reduced the futile production of ATP. The model can be extended to axons releasing other, or coreleasing multiple, transmitters. Hence, the activity-dependent formation and release of neurotransmitter may be a universal mechanism of ATP homeostasis
Variability-selected AGN in dwarf galaxies : the incidence of AGN in dwarf and massive galaxies is similar
Full text linkWe use the VST-COSMOS survey to identify, via their optical broad-band variability, 30 active galactic nuclei (AGN) in nearby (z 10 10 M ⊙) galaxies, we estimate the relative frequency of AGN in these two mass regimes. Our results suggest that the incidence of AGN in dwarfs and massive galaxies is similar (within less than a factor of 2 of each other), with some evidence that the AGN fraction increases with stellar mass in the dwarf population
Training and External validation of Machine Learning Supervised prognostic models of Upper tract urothelial cancer (UTUC) after Nephroureterectomy
Full text linkThe European association of Urology (EAU) suggests a prognostic stratification of Upper Tract Urothelial Cancer (UTUC) based on high and low risk patients, with Radical nephroureterectomy (RNU) and bladder cuff resection being the gold standard for the treatment of non-metastatic High risk UTUC. However, no consensus on post-operative patient management or tools that predict who would benefit the most from a close follow-up rather than adjuvant chemotherapy regimen exist. in Machine Learning (ML) is gaining interest in Urology providing models for prognostic prediction purpose; It’s role in UTUC has not yet been investigated. We aim to develop and validate multiple supervised ML models based on patient- and tumor- related features to predict prognosis in patients with preoperative Histological or Imaging proved UTUC treated with RNU within a multiethnic large cohort. Data from an international multicenter large cohort of histologically proven UTUC patients from Asia and Europe treated with RNU were retrospectively collected. Twenty different ML-supervised predictive models were first trained and then external validate with two separate set. Nomograms were constructed based on 8 independent prognostic factors (age, gender, grading, pT, pN, presence of Carcinoma in Situ (CIS), multifocality and Lymphovascular invasion(LVI)) to predict 6 Outcomes (Overall Survival (OS), Cancer Specific Survival (CSS) and Disease Free Survival (DFS) at 3 and 5 year). Performances were compared using Area-under-curve (AUC) of Receiver-Operating Characteristics (ROC). A total of 3129 patients were enrolled: 637 Asian Patients (training cohort) and 2492 European patients (validation cohort). Upon training assessment, LR models achieved the best results, being the best model for prediction of 4/6 outcomes, with the best result in CSS both at 3 and 5 years (AUC: 0.85, 0.84, 0.81 for CSS-3y, CSS-5y and DFS-3y respectively). Upon external validation, LR(CSL) models achieve the best results, being the number 1 model for prediction of 3/6 outcomes (AUC: 0.84, 0.79, 0.77 for CSS-3y, OS-3y and OS-5y respectively). ML is a promising technology in the field of UTUC. Our model achieve favorable results in terms of prediction of prognosis after RNU, especially in terms of CSS at 3 and 5 years, moreover is the first model of prognosis taking into account the differences in epidemiology existing between European and Asian patients. Further clinical validation and verification of its reliability for the case selection of adjuvant therapy are needed to assess its use in clinical practice linked to clinical decision making. ML is an advancing technology in the field of medicine and urology, which can also be applied to the definition of the prognosis of patients with UTUC undergoing RNU. Our study represents the first experience investigating this potential
From fine to giant : Multi-instrument assessment of the dust particle size distribution at an emission source during the J-WADI field campaign
Full text linkMineral dust particles emitted from dry, uncovered soil can be transported over vast distances, thereby influencing climate and environment. Its impacts are highly size-dependent, yet large particles with diameters dp>10 μm remain understudied due to their low number concentrations and instrumental limitations. Accurately characterizing the particle size distribution (PSD) at emission is crucial for understanding dust transport and climate interactions. Here we characterize the dust PSD at an emission source during the Jordan Wind Erosion and Dust Investigation (J-WADI) campaign, conducted in Wadi Rum, Jordan, in September 2022, focusing on super-coarse (1062.5 μm) particles. This study is the first to continuously cover the full range of diameters from dpCombining double low line0.4 to 200 μm at an emission source by using a suite of aerosol spectrometers with overlapping size ranges. This overlap enabled a systematic intercomparison and validation across instruments, improving PSD reliability. Results show significant PSD variability over the course of the campaign. During periods with friction velocities (u*) above 0.22 ms-1 (or ∼3.3 ms-1 threshold 4 m wind speed), the approximate threshold for local dust emission by saltation, both dust concentrations and the contributions of super-coarse and giant particles typically increased with increasing u∗, especially under neutral to unstable atmospheric stability conditions. These large particles accounted for about 90 % of the total mass concentration during the campaign. A prominent mass concentration peak was observed near dpCombining double low line60 μm in geometric diameter. While particle concentrations for dp<10 μm showed good agreement among most instruments, discrepancies appeared for larger dp due to reduced instrument sensitivity at the size range boundaries and sampling inefficiencies. Despite these challenges, physical samples collected using a flat-plate sampler largely confirmed the PSDs derived from the aerosol spectrometers. These findings help to advance our understanding of the dust PSD and the abundance of super-coarse and giant particle at emission sources
Electron densities from [S ii ] lines significantly overestimate the impact of ionized AGN outflows
Full text linkTo explain the properties of the local galaxy population, theoretical models require active galactic nuclei (AGNs to inject energy into host galaxies, thereby expelling outflows of gas that would otherwise form stars. Observational tests of this scenario rely on determining outflow masses, which requires measuring the electron density () of ionized gas. However, recent studies have argued that the most commonly used diagnostic may underestimate electron densities (and hence overestimate outflow masses) by several orders of magnitude, casting doubt as to whether ionized AGN-driven outflows can provide the impact needed to reconcile observations with theory. Here, we investigate this by applying two different electron–density diagnostics to Sloan Digital Sky Survey (SDSS) spectroscopy of the Quasar Feedback (QSOFEED) sample of 48 nearby type-2 quasars. Accounting for uncertainties, we find that outflow masses implied by the transauroral-line electron-density diagnostic are significantly lower than those produced by the commonly-used ‘strong-line’ [S ii](6717/6731) method, indicating a different origin of these emission lines and suggesting that these doubts are justified. Nevertheless, we show that it is possible to modify the [S ii](6717/6731) electron–density diagnostic for our sample by applying a correction of to account for this, which results in values that are statistically consistent with those produced using the transauroral-line method. The techniques that we present here will be crucial for outflow studies in the upcoming era of large spectroscopic surveys, which will also be able to verify our results and broaden this method to larger samples of AGN of different types
AI decision support for increasing prostate biopsy efficiency: a retrospective multicentre, multiscanner study
Full text linkObjectives To develop and retrospectively validate an artificial intelligence-based decision support system (AI-DSS) for optimising prostate biopsy decisions and improving benefit-to-harm ratios. Materials and methods This retrospective, multicentre, multiscanner study used data from 1022 patients. An AI-DSS integrating PI-RADS scores, automated prostate-specific antigen density (PSAd), and deep-learning imaging risk scores was developed on 770 cases and validated on an independent cohort of 252 men from six UK centres. The AI-DSS performance was benchmarked against the real-world clinical decisions (reference standard) using grade selectivity, biopsy efficiency, and selective biopsy avoidance as outcome measures. Biopsy-proven detection of grade group (GG) ≥ 2 disease was the reference standard. Results In the validation cohort of 252 patients (mean age, 67.3 years), 137 underwent biopsy and 79 (31%) harboured ≥ GG2 disease. Compared to the reference standard, the AI-DSS at the 31% cancer detection rate (CDR) would have avoided 28 biopsies while missing one ≥ GG2 cancer. This corresponded to a 70% increase in grade selectivity (from 4.6 to 7.8), 79% increase in biopsy efficiency (from 1.4 to 2.5), and a 143% increase in selective biopsy avoidance (from 2.8 to 6.8). At the reduced CDR of 30%, grade selectivity, biopsy efficiency, and selective biopsy avoidance increased by 172%, 236%, and 475%, with four ≥ GG2 cancers missed. Conclusion An AI-DSS that integrates clinical and advanced imaging data improves the benefit-to-harm ratio of prostate biopsy decisions in a retrospective setting. Future prospective validation as part of real-world clinical workflow is required to enable clinical implementation. Key Points Question Current prostate cancer diagnostic pathways result in fewer unnecessary biopsies. Can an AI decision support system (AI-DSS) further improve biopsy efficiency for detecting significant cancer
Opportunities to Improve Nutrition for Patients in Hospital After Discharge From an Intensive Care Unit: A Human Factors Analysis
Full text linkBackground: Nutrition during hospitalisation following critical illness is fundamental to rehabilitation, but provision is often poor. Aim: To analyse the process of delivering nutrition to post‐ICU patients on the ward. Study Design: This work forms part of a mixed methods study. In three representative UK hospitals, we conducted: a structured judgement review (SJR) of 300 patients who died following discharge from ICU; in‐depth reviews of 20 survivors and 20 deaths judged to be ‘probably avoidable’ in the SJR; and interviews with 55 patients, family members and staff about their experiences of post‐ICU ward care. We extracted nutrition provision information from the primary data. Using these data and the Functional Resonance Analysis Method (FRAM), we worked with stakeholders to map the process of delivering enteral feed to patients discharged from ICU to hospital wards. Results: The stakeholder meeting included a dietitian and a medical registrar from two of the three primary data collection sites, two researchers with knowledge of the primary data (with nursing and physiotherapy backgrounds) and a human factors facilitator. The FRAM revealed that providing enteral feeding on the ward is not a linear process, with three clusters of functions delivering distinct steps within the wider process: establishing the need for nasogastric feeding, the nasogastric placement cycle and nasogastric feed delivery. There are multiple points in these processes where failures in multi‐professional teamwork result in the absence of the required steps to move through the processes in a timely manner. In particular, the process for confirming nasogastric tube placement risked system‐related delays to feed administration, significantly affecting the volume of feed delivered to patients. Conclusions: The FRAM identified multiple process problems affecting nutritional support that may have led to profound consequences for post‐ICU patients, with multi‐professional collaboration a key factor for effective delivery of timely enteral nutrition. Relevance to Clinical Practice: Improving collaborative working processes and addressing common nutritional support problems after ICU discharge could improve nutritional delivery and expedite recovery from critical illness. Trial Registration: ISRCTN1465805
Cross-Impact Analysis with Crowdsourcing for Constructing Consistent Scenarios
Full text linkCross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions
FOV-RVO: Velocity Obstacle-Based Pedestrian Motion Predictor
Full text linkPredicting pedestrian motion is a crucial part of any safety-first autonomous driving system. We present FOVRVO, a Velocity Obstacle-based motion prediction method that models pedestrian-to-pedestrian and pedestrian-to-scene interactions by integrating the gaze directions of the pedestrians and map information of the environment. The proposed solution is fast, robust, and does not require any prior data. Furthermore, we enhance the method by introducing an auxiliary pre-trained Deep Learning (DL) method and combining predictions for final evaluation to utilize the strengths of both knowledgebased and data-driven motion prediction methods. The combined model is implemented inside the autonomous driving framework - Autoware Mini and tested on data from trips in urban conditions in Tartu, Estonia. The proposed FOV-RVO method outperforms compared state-of-the-art DL methods at number of predicted candidate trajectories K=1 in combined evaluation using minimal Average/Final Displacement Errors (minADE/minFDE), Miss Rate (MR), and non-Drivable Area Compliance (nonDAC). The combined solution at K=2 performs equivalent or better than tested models that output significantly higher predictions (up to K=10). The open-source code with instructions on accessing the dataset is available at https://github.com/dmytrozabolotnii/autoware_mini/tree/FOVRVO
“It doesn’t matter how hard things are, you just keep going” : The experiences of adult women who grew up as carers for parents experiencing psychosis-related difficulties
Full text linkA significant number of children fulfil a young carer role for parents experiencing mental health difficulties. Young carer research highlights children’s experiences of responsibility and role reversal. This study investigated the retrospective experiences of female young carers for a parent with psychosis-related difficulties, which may pose unique and specific challenges. Seven adults who grew up with a parent experiencing psychosis were recruited. Semi-structured interviews were completed and analysed using Interpretative Phenomenological Analysis. Findings are reported in two parts relating firstly to the retrospective accounts of experiences during childhood; and secondly, the current journey of sense-making. Four central themes were constructed, namely: “Caring was lonely and uncertain, but there were some connections”, “Learning how to be the parent while still a child”, “It felt natural, but still difficult to understand” and “Gaining empathy and resilience, while experiencing an ongoing impact”. Clinical implications include the importance of family-focused interventions and an understanding that the impact of offering this support continues into adulthood