A position paper released by the European Association of Nuclear Medicine emphasised the need for multidisciplinary engagement to establish dosimetry-based personalised treatment in Radionuclide therapy (RNT). The uncertainty analysis results often ignored in routine clinical practice should be incorporated into the dose calculations to improve the efficacy and accuracy of treatment. In this study, patients with haematological malignancies undergoing radioimmunotherapy were evaluated. Our study aimed to calculate the uncertainties associated with each parameter of the single time point (STP) dosimetry chain and compare the with multiple time points (MTP) in the bone marrow and liver results. 28 patients received an intravenous injection of 111In-besilesomab (0.17 ± 0.01GBq) for pre-therapeutic dosimetry and were subsequently treated with 90Y-besilesomab(2.43 ± 0.53GBq). A dosimetry analysis was performed on bone marrow (BM) and liver with MTP and STP. We investigated the uncertainty in population mean effective half-life, volume, recovery coefficient, counts, measured activity, fitting parameters, time-integrated-activity, S-factors, and absorbed dose (AD) for a group of patients. The mean absorbed dose per unit administered activity (DpA) to BM was 5.8 ± 1.7 mGy/MBq with MTP and 5.8 ± 1.6 mGy/MBq with STP, and to the liver was 2.9 ± 1.9 mGy/MBq with MTP and 3.1 ± 2.4 mGy/MBq with STP. The mean fractional uncertainty associated with total absorbed dose to BM was 13.18 ± 3.46% with MTP and 18.75 ± 3.22% with STP, and to liver was 5.77 ± 3.13% with MTP and 49.78 ± 25.36% with STP. A moderate positive relationship (R2 = 0.7) was noted between post-injection acquisition time and AD uncertainty with STP for BM, whereas a strong positive relationship (R2 = 1) was noted for the liver. The absorbed dose uncertainty in STP was significantly higher compared to the MTP. Incorporating the uncertainty analysis for STP dosimetry parameters in routine clinical practice is strongly recommended. The accuracy in the acquisition time, population-based half-life and fitting function for time activity curve is vital for minimising uncertainty in STP dosimetry, which is less time-consuming and easier to implement in clinical practice than MTP.
There are a range of clinical uncertainties for managing mild and unilateral deafness in infants, such as whether hearing interventions are effective or even required. This study aimed to explore parental experiences of decision making for their pre-school aged children with mild or unilateral deafness identified through newborn hearing screening. Grounded theory methodology was used to develop a theory of parental decision making. Semi-structured interviews were carried out with seventeen parents of children with mild and unilateral deafness age 5 months to 4 years old. Parents made decisions about use or non-use of hearing interventions. Negotiating uncertainty was the core category that explained the decision making process. Parental skills and values, trust in professionals, and external constraints enhanced or mitigated uncertainty. The process of negotiating uncertainty involved learning to notice any changes in their child's behaviours which could reinforce or cast doubt on medical advice. Parents engaged in trade-off actions, such as comparing their perceptions of their child's hearing with evidence provided by professionals. These actions led to specific decisions regarding ways of managing their child's deafness, whether they preferred to 'maximise hearing' or 'wait and see'. Parents of infants and young children with mild and unilateral deafness often experience significant uncertainty, particularly in the early stages following diagnosis. The parent-professional relationship is important in supporting families through uncertainty and decision making. Enhancing professionals' ability to communicate effectively about uncertainty could support early parental decision making about their child's deafness.
This study investigates automaticity attribution bias (AAB), whereby consumers attribute autonomous product failures to the algorithmic nature, reducing satisfaction, particularly for high-autonomy products. This effect is amplified in high uncertainty avoidance (UA) cultures. Study 1 (N = 110 after exclusions, M_age = 31.28, 58.2% female) used a scenario-based experiment to test emotional responses and satisfaction across individual uncertainty tolerance and expectation conditions, examining psychological-level mechanisms. Study 2 analysed Amazon reviews (UK, France) of products varying in autonomy to examine cultural-level uncertainty avoidance, confirming AAB as a mediator between negative disconfirmation and outcomes. We acknowledge these operate at distinct analytical levels and avoid cross-level inferences. Findings highlight cultural and psychological factors shaping responses to autonomous product failures, offering insights for tailored marketing strategies in diverse markets. Study 1 employed a scenario-based experiment to explore emotional responses and satisfaction under manipulated individual uncertainty tolerance and expectation levels, generating varying disconfirmation. Study 2 complemented this with naturalistic text analysis of reviews from countries differing in cultural UA, triangulating experimental findings with real-world evidence. These studies emphasize how individual and cultural factors differentially influence consumer evaluations.
We hypothesize that the large uncertainty of slip length in conventional approaches for Newtonian fluids under creeping flow and lubrication approximation conditions arises from the assumed ideal velocity distribution, and an alternative approach that does not require assuming an exact velocity distribution is of great value under the same conditions. In this study, we developed a new methodology to quantitatively and accurately determine slip length by converting the AFM-measured hydrodynamic forces and approach rates into mass flow and velocity-independent resistances and by using the Stribeck curve to identify the appropriate data interval. To prove the concept, we employed high-precision colloidal probe atomic force microscopy (CP-AFM) technology to study water on hydrophilic, less-wetting, and unmodified silicon surfaces across six driving velocities (6.0-36.6 μm/s), and their slip length values were then obtained, compared with conventional methods combined with further discussions. The resulting slip lengths show no dependence on the driving velocity. We further introduce an analysis of extended uncertainty, demonstrating that our method exhibits an extended uncertainty less than one-third of that from the conventional approaches. Subsequently, we quantitatively assessed the contribution of friction resistance (RFriction, directly linked to slip) in the total resistance (RTotal). The results show that, as the separation increases, the contribution of RFriction decreases significantly, which explains why the slip length is so difficult to measure precisely. This study reveals that the large uncertainty of slip length obtained with the conventional methods stems from underestimating the contribution of RFriction, and this work provides a novel and reliable methodology for achieving high-precision slip length by precisely decoupling the viscous and friction flow components.
Adaptive behavior requires organisms to make decisions under uncertainty, balancing the exploitation of known options with exploration as environmental structure changes. Across ecology and neuroscience, this problem has been studied using distinct experimental and theoretical frameworks, including probabilistic choice, reversal learning, foraging tasks, reinforcement learning, and Bayesian inference. Here, we synthesize some of these ideas within a predictive processing perspective, arguing that they address a shared computational challenge: inferring latent environmental structure and adjusting behavior in response to different sources of variability. We distinguish key forms of uncertainty and review evidence that animals can regulate learning rates, persistence, and exploration according to the inferred origin of outcome variability. Laboratory paradigms such as probabilistic reversal learning provide controlled settings to dissociate sensitivity to noise from sensitivity to change, while foraging tasks reveal how local fluctuations are integrated with global estimates of environmental quality. Across species, apparent decision variability often reflects adaptive sampling rather than suboptimal noise. We further review evidence suggesting that cortical and subcortical circuits can encode predictions and environmental statistics, and that neuromodulator systems, including noradrenaline, acetylcholine, dopamine, and serotonin, modulate the influence of new evidence relative to prior beliefs. Together, these findings support a view of adaptive decision-making as hierarchical uncertainty resolution that operates across behavioral timescales and experimental contexts, and provide a framework for linking ecological decision rules, laboratory models, and neural mechanisms.
Population aging poses mental health challenges, particularly regarding depression. This study explores the relationship between transdiagnostic variables (self-esteem, intolerance of uncertainty), self-defining memory characteristics, and depressive symptoms in 225 young (M = 22.8) and 225 older adults (M = 68.4). Older adults reported higher depressive symptoms, self-esteem, and intolerance of uncertainty, as well as more specific, positive, and identity-relevant memories. Young adults' memories showed more guilt/shame and anger. In older adults, lower self-esteem, higher intolerance of anxiety, more memory detail, and external guilt/shame predicted greater depressive symptoms. These results highlight the role of memory and emotional processing in understanding depression in later life.
Quantifying the contributions of different phosphorus (P) sources is critical for controlling eutrophication, yet source discrimination using phosphate oxygen isotopes (δ¹⁸Oₚ) alone often involves significant uncertainty due to overlapping isotopic signatures. To address this, we developed a fluorescence index (FI)-informed Bayesian mixing model for δ¹⁸Oₚ, providing a pragmatic approach for watershed-scale P source discrimination. In this framework, δ¹⁸Oₚ defined the mixing likelihood, while FI supplied auxiliary information to construct period-specific informative Dirichlet priors. Applied to the Yongan River watershed in eastern China, this framework refined source allocation where isotopic discrimination was limited and reduced 90% posterior uncertainty intervals by 11-20% relative to the δ¹⁸Oₚ-only baseline model. Source contributions showed distinct seasonal shifts, with riparian soils and agricultural runoff dominating P inputs during high-flow conditions, whereas groundwater became increasingly important during normal and low-flow periods. These flow-dependent patterns were qualitatively consistent with independent hydrological and hydrochemical evidence under varying flow regimes. A decoupled stress test indicated that the inferred period-specific source structure was generally preserved under plausible non-conservative and asynchronous alteration of δ¹⁸Oₚ and FI. Overall, these findings indicate that FI can serve as a complementary hydro-biogeochemical indicator for improving the precision and interpretation of δ¹⁸Oₚ-based P source apportionment.
As third-year medical students transition into high-stakes, high-stress clinical environments like the emergency department (ED), they may experience significant personal trauma. However, little is known about how this trauma is experienced early in their training - specifically during the transition from preclinical to clinical learning environments. This study addresses that gap by exploring third-year medical students' experiences of trauma during the emergency medicine (EM) clerkship through the lens of Trauma-Informed Care (TIC) and identifies workplace factors and intersectional demographics influencing these experiences. This qualitative study used the critical incident technique to explore emotionally-significant events encountered by third-year medical students immediately after completing the EM clerkship as their first core clerkship at a single academic institution. We conducted a thematic analysis using the Substance Abuse and Mental Health Services Administration's six TIC principles. Data were triangulated with quantitative demographic data, and data saturation was confirmed through constant comparison and reflexive team discussions. Seventeen students participated, describing 19 critical incidents of trauma. The most common trauma types involved lack of peer support and lack of empowerment or voice. Intersectional factors such as race, gender, and age shaped both the type and nature of trauma. Clinical uncertainty, power differentials, and unprofessional behavior emerged as frequent triggers. Applying a trauma-informed framework to medical education reveals how structural and interpersonal factors contribute to student trauma when they transition to the clinical learning environment. These findings highlight opportunities for trauma-informed clerkship design and structured support to create safer, more inclusive learning spaces. Not applicable.
A traditional view of selective attention distinguishes between goal-directed and stimulus-driven mechanisms of attentional control. More recently, a large (and growing) body of research has identified a third class of control system-termed selection history-wherein attentional prioritisation is shaped by our prior experience with stimuli, independently of our goals and the physical salience of those stimuli. This article reviews work within this selection history literature demonstrating that prioritisation is rapidly and automatically modulated by learning about the rewards associated with stimuli, and argues for a framework that distinguishes between history-driven processes implementing attentional exploitation (the drive to leverage reliable information) and attentional exploration (the drive to resolve uncertainty, with the aim of validating potential new sources of information). Findings such as these highlight a fundamental and intricate interaction between learning and attention, wherein our prior experience shapes the way in which we extract information from our environment - with potential consequences for understanding the subsequent decisions that we make and choices that we take.
The allocation of emergency resources is a lifeline in hazard-induced disaster relief, directly determining survival rates, rescue efficiency, and the restoration of social order. Existing allocation models face two limitations: (1) insufficient cross-role integration of funds and resource flows, and (2) limited application of multi-stage robust optimization. To address these issues, this study proposes a tripartite multi-role integrated scheduling framework that jointly considers the objectives and constraints of decision-making (fund allocation), execution (procurement and transportation), and demand roles (affected populations) to optimize economic cost, life-saving utility, and supply-demand balance. We adopt multi-stage adaptive robust optimization to reformulate the allocation model into a feedback framework integrating pre-disaster planning with post-disaster periodic adjustments under demand, procurement, and transportation uncertainties. For model tractability and compatibility with off-the-shelf solvers, we develop a linear reformulation technique that converts the nonlinear robust model into a linear program using duality theory and semi-infinite constraint handling. A case study based on an earthquake scenario shows that multi-stage adjustment and multi-role integration improve allocation optimality, stability, and robustness under multiple uncertainty sources. Sensitivity analyses of key parameters, such as time discretization and historical dependency depth, provide practical guidance for model deployment. Compared with deterministic, static robust, and rolling-horizon methods, the proposed approach shows improvements in allocation safety and overall rescue performance.
Crickets are widely consumed edible insects with high nutritional value, requiring reliable analytical methods for elemental characterization. Their complex matrix can cause matrix effects, making suitable reference materials essential for quality control. Because certified cricket reference materials are scarce, particularly in Brazil, this study aimed to prepare and characterize a national reference material candidate using black cricket (Gryllus assimilis). The production process was adapted to the matrix and included grinding, lyophilization, and packaging, yielding a batch of 25 bottles. The influence of particle size and between-bottle homogeneity was evaluated. Homogeneity was assessed by ANOVA, supported by Levene and Shapiro-Wilk tests, using five randomly selected bottles and three subsamples of 0.250 g per bottle. Elemental characterization was performed by energy-dispersive X-ray fluorescence. Expanded uncertainties were estimated with a coverage factor of k = 2 at approximately 95% confidence. Results showed satisfactory between-bottle homogeneity (p-value > 0.05) and indicated that particle sizes below 500 µm did not significantly affect concentrations. The candidate presented average mass fractions ± expanded uncertainty of Ca (1531.0 ± 176.5 mg kg⁻1), Cl (5559.4 ± 1381.2 mg kg⁻1), Cu (22.9 ± 1.7 mg kg⁻1), Mg (1083.1 ± 295.8 mg kg⁻1), P (10 397.0 ± 1084.6 mg kg⁻1), and S (7608.1 ± 398.0 mg kg⁻1). Coefficients of variation were below 11%, and expanded uncertainties remained under 30%. These results demonstrate adequate precision, reproducibility, and matrix suitability of the proposed material. The study supports continued evaluation of intra-bottle homogeneity, stability, and certification to enable its availability for laboratories performing elemental analysis of cricket-based foods, strengthening analytical reliability and food safety assessment in emerging insect protein chains worldwide. This contribution addresses national needs and supports sustainable nutrition research efforts.
Global climate change is rapidly impacting biodiversity and threatening the sustainable use of medicinal plant species by reducing their availability and increasing harvest uncertainty. Understanding the adaptive genetic variation and genetic vulnerability of medicinal plants under climate change is crucial for effective germplasm management, cultivation, and breeding efforts. In this study, we assessed the genetic differentiation, local adaptation, and genomic vulnerability of the medicinal plant Isodon rubescens (Hemsl.) H. Hara, with the goals of elucidating the impacts of geographic and environmental factors on its genetic structure and identifying at-risk populations for informed conservation and breeding under climate change. We applied restriction site-associated DNA sequencing (RAD-seq) to 17 populations of I. rubescens spanning its central and peripheral ranges, including the Taihang and Qinling-Funiu Mountains. The analysis revealed two distinct genetic groups: one in the Taihang Mountains and the other in the Qinling-Funiu Mountains. Significant patterns of isolation by distance (IBD), environment (IBE), and resistance (IBR) were detected, alongside high niche differentiation. We identified 456 candidate adaptive SNPs, some linked to genes involved in stress responses and biosynthesis. Precipitation was a key environmental driver of local adaptation. Populations in the northern Taihang Mountains and southern Funiu Mountains showed higher genomic vulnerability, indicating a greater risk of maladaptation. Our findings demonstrate that geographic isolation and environmental factors, particularly precipitation, are key drivers of genetic differentiation and local adaptation in I. rubescens. The identified genomic vulnerability pinpoints specific populations at high risk under climate change. These insights provide a crucial genetic basis for formulating targeted conservation strategies and developing climate-resilient breeding programs for this medicinal species.
Early turnover among new graduate nurses is a global concern that threatens the stability and quality of nursing services. Although many studies have examined turnover among new graduate nurses, most have relied on cross-sectional designs, thus providing a limited understanding of how turnover intentions and professional identities evolve. This study aimed to explore the longitudinal trajectories of new graduate nurses employed in large-scale hospitals in South Korea and examine how their experiences, perceptions, and turnover intentions changed during their first 3 years of professional practice. Longitudinal qualitative study. Twenty-nine nurses licensed in 2021 who were recruited through open recruitment processes at large-scale hospitals. Data were collected at two time-points-within the first year and during the third year of practice-through focus group interviews, individual interviews, or written reflections. All interviews were semi-structured and analyzed using traditional inductive content analysis. Rigor was established according to Guba and Lincoln's four criteria of trustworthiness. Five themes were identified: 1) Living in uncertainty while awaiting placement, 2) A merciless honeymoon: Diverging early-career trajectories, 3) Competent nurses who still dream of leaving hospitals, 4) Two distinct paths among those who left hospitals, and 5) What I've come to realize in the third year post-licensure. The trajectory toward becoming a competent nurse is complex and nonlinear. Turnover intention among new graduate nurses evolves, progressively shifting from personal to organizational causes. Multilevel interventions, such as stage-specific organizational support, structured career development systems, diversified undergraduate practicum models, and balanced workforce policies, are required to promote sustainable retention.
While normative theories of choice advocate for deliberative, rational strategies, models of bounded rationality postulate that humans often rely on fast, simplifying heuristics. We hypothesized that the ability to implement deliberative decision strategies depends on the capacity for response inhibition, subserved by the pre-supplementary motor area (preSMA) and the right inferior frontal gyrus (rIFG). We tested this in a study, in which participants performed a multi-attribute probabilistic inference task following a repetitive transcranial magnetic stimulation (TMS). Continuous theta-burst stimulation (cTBS) has been applied to preSMA, rIFG or a control site (right superior parietal lobule) in a within-subject design. The task involved choosing between two options based on six attributes, presented in compensatory and noncompensatory environments varying in attribute weight distribution. Participants' Need for Closure (NFC) was also assessed as a potential moderator, due to its conceptual relevance and earlier findings highlighting its role in decision-making. cTBS to preSMA selectively modulated strategy use: it enhanced the application of a complex strategy in the compensatory condition and increased the use of a heuristic in the noncompensatory condition. These effects were moderated by NFC, with the strongest effect observed in individuals with low and moderate scores on the NFC scale and its facet Closed-mindedness. No effect was observed following cTBS to rIFG. Assuming that cTBS acted in inhibitory manner, these findings show that inhibiting preSMA enhances the adaptivity of choice processes, particularly in individuals more open to uncertainty, and they support the role of preSMA in flexible cognitive control during decision-making.
Parents of undiagnosed children (POUC) experience significant psychosocial challenges, including anxiety, uncertainty, and isolation, that stem from parenting medically complex children while facing obstacles throughout the diagnostic journey. Despite these well-described challenges, a mental health intervention designed to meet the unique needs of POUC, which is necessary to promote the psychological and overall wellbeing of this population, does not exist. Acceptance and Commitment Therapy (ACT) has proven effective in a wide range of populations and shows promise for POUC. With the goal of designing and implementing an ACT-based intervention tailored to POUC, this pre-implementation study aimed to understand their psychosocial needs and prior mental health support experiences, explore their reactions towards ACT, and determine their anticipated barriers, facilitators, and preferences for participating in an ACT skills group, guided by the Consolidated Framework for Implementation Research (CFIR). Semi-structured, individual interviews were conducted with 18 POUC, including an experiential portion that exposed participants to key ACT concepts and exercises. Inductive coding based on participant responses and deductive coding based on the CFIR were employed to code interview transcripts. Reflexive thematic analysis was performed to identify key findings. Isolation was a psychosocial challenge for which all participants desired support. Many participants reported inadequacies in their prior mental health support, primarily due to lack of understanding from therapy providers regarding their unique circumstances. Although most participants indicated that ACT could help them manage difficult thoughts and emotions and act in alignment with their values, they also described achievability, collaboration, and accountability as key elements that could support their uptake. The main barriers, facilitators, and preferences that participants highlighted were related to group design (accessibility, flexibility) as well as their own characteristics as recipients (capability, need, and motivation). This pre-implementation study affirmed the potential value of ACT for POUC and identified key opportunities for tailoring an ACT skills group to meet their needs. Future research, including pilot implementation studies, are needed to evaluate the effectiveness of a tailored ACT skills group and further refine both the intervention and its implementation strategy.
To integrate current evidence and expert consensus on safe, effective exercise prescription in Duchenne and Becker muscular dystrophy (DMD/BMD), translating key pathophysiological principles into practical clinical guidance. Proceedings from the June 2025 Parent Project Muscular Dystrophy meeting, Translating Exercise Research in Dystrophinopathy to the Clinic, were synthesized. Faculty reviewed dystrophinopathy pathology and exercise physiology, analyzed data from clinical trials and pilot studies, summarized outcome-measure selection, and discussed pragmatic solutions to overcome barriers. Through interactive discussion, expert opinion on clinical management was aligned with exercise prescription frameworks (FITT) and clinic resources. Exercise modality and dosing are understudied in dystrophinopathies yet represent critical factors for safe and effective interventions. In DMD, assisted low-intensity cycling can stabilize function and moderate isometric protocols can increase strength without evidence of injury. In BMD, aerobic training and supervised low-intensity resistance generally improve fitness/strength, whereas high-intensity loads may pose risks. Pragmatic assessments support monitoring and anticipatory care. Individualized prescriptions, supervised onboarding, and social engagement may mitigate dosing uncertainty, equipment access, and adherence barriers. The clinical framework should emphasize movement observation, postural strategies, oculomotor and cognitive-motor screening, and documentation for insurance coverage. Safety guidance emphasizes physician clearance, submaximal dosing, fatigue management, and clear "red flag" escalation pathways. Contemporary data and expert consensus support integrating conservative, systematically monitored exercise as an essential adjunct to DMD/BMD care. Available evidence indicates low-to-moderate aerobic activity and moderate-intensity isometric exercise appear feasible and safe when individualized and supervised. Further controlled studies should refine dosing and strengthen disease-stage-specific guidance.
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
The increasing penetration of photovoltaic distributed generation (PV-DG) in Radial Distribution Systems (RDSs) plays a vital role in achieving sustainable energy transition objectives; however, the inherent uncertainty associated with solar irradiance and load demand poses significant challenges to optimal planning and operation. This paper presents a stochastic optimization framework for PV-DG allocation in RDSs using the Barrel Theory-Based Optimizer (BTO). Uncertainties in solar irradiance and load demand are explicitly modeled using appropriate probability density functions and efficiently represented through a higher-order Point Estimate Method (PEM), which captures the essential statistical characteristics with a limited number of representative scenarios. The proposed framework simultaneously optimizes the location and capacity of PV-DG units to minimize real power losses and enhance voltage profile performance while ensuring system operational constraints are satisfied. The effectiveness of the proposed approach is validated on the 85-bus and the IEEE 118-bus RDSs, where the BTO exhibits superior convergence characteristics and enhanced solution robustness when compared with several benchmark optimization techniques, including the well-established Differential Evolution Algorithm (DEA), the recent Crocodile Ambush Optimization (CAO, 2025), and the Schrödinger Optimizer Algorithm (SOA, 2025). For the 85-bus RDS, the impact of integrating different numbers of PV units is systematically investigated. Simulation results confirm that the proposed BTO-based stochastic planning strategy significantly improves energy efficiency, voltage regulation, and loss reduction, thereby enhancing the overall sustainability of the RDS. For the 85-node RDS, the BTO achieves a noticeable reduction in average real power losses, outperforming DEA, CAO, and SOA by 2.55%, 4.10%, and 6.74%, respectively, when three PV units are installed. Additionally, for the case of four PV units, the proposed BTO yields even greater improvements, with loss reductions of 5.12%, 7.50%, and 14.12%, respectively, compared with the same benchmark algorithms. Furthermore, for five PV units, the BTO achieves much greater reduction, outperforming DEA, CAO, and SOA by 13.05%, 6.45%, and 32.31%, respectively, when three PV units are installed.
Positron emission tomography (PET) instrumentation has seen significant advances over the last 20 years. In particular, substantial improvement in electronics and scalability have allowed new clinical scanners to combine high gamma stopping power and patient geometrical coverage in the total body PET paradigm. Similarly, spatial resolution has almost reached its physical limit imposed by the uncertainty caused by the positron range and acollinearity of emitted annihilation gamma rays. Time-of-flight (ToF) offers an improvement to effective sensitivity, proportional to the reverse square of detector timing resolution. In contrast to the aforementioned, this is a development direction that has not yet been sufficiently harnessed, with state of the art remaining far from the physical limit of ToF resolution. Nevertheless, existing scanners offer a glimpse of the advantages of ToF for imaging and diagnosis. In this review, analyse the limiting factors; describe the motivation for enhancing detector ToF capabilities; explore the physical mechanisms related to ToF application; describe the state-of-the-art in clinical, prototyping and laboratory stages; offer insights on emerging approaches and their capabilities to provide scalable, and cost-effective ToF; and finally envision the future of medicine, once ultraToF of the order of 10 ps has become the standard in clinically deployed scanners.
Action anticipation has been primarily attributed to the action observation network (AON) and dual visual streams that process action information, yet this sensorimotor account overlooks the inferential processes required when predicting outcomes from incomplete information. How this sensorimotor system interacts with the medial prefrontal cortex (mPFC), a region supporting inference under uncertainty, remains unclear. We investigated the connectivity architecture between mPFC and sensorimotor systems, and how different representational profiles shape this architecture. Novices received either observation-based or execution-based training on table tennis serve anticipation, alongside an untrained control group. Participants completed anticipation tasks using point-light displays and full-body videos during fMRI scanning, with behavioral modeling, neural activation, and dynamic causal modeling (DCM) analyzed. DCM revealed architectural invariance across all groups: mPFC receives inputs from both AON-integrated action representations and direct feature-level information from dual visual streams, enabling flexible predictive inference. However, representational profiles systematically modulated this shared architecture. Perception-dominant representations prioritized contextual integration with enhanced prefrontal engagement when contextual cues were available, while motor-dominant representations emphasized kinematic features with format-invariant extraction, stronger top-down connectivity, and enhanced mPFC recruitment for impoverished stimuli. These findings demonstrate that action anticipation relies on a universal multi-level connectivity architecture enabling cognitive inference in mPFC, with experiential learning shaping information weighting and modulatory dynamics within this shared system.