Browse Articles

A topological approach to positron emission particle tracking for finding multiple particles in high noise environments

Abstract Positron emission particle tracking (PEPT) is an advanced imaging technique that accurately tracks the three-dimensional spatial coordinates of a radioactively-labelled particle with sub-millimetre and sub-millisecond precision. By detecting back-to-back 511 keV gamma rays from positron-electron annihilation coincidence events, PEPT can locate particles within highly dense, opaque systems such as fluidised beds, rotating drums, and mills. Despite the progress made in enhancing the precision and accuracy of PEPT, simultaneous multiple particle tracking remains a significant challenge, particularly in high-noise environments. This paper introduces T-PEPT, a novel algorithm that leverages topological data analysis-a relatively new field of applied mathematics that explores the underlying ’shape’ of data through techniques like persistence homology. By creating simplicial complexes and applying persistence homology to PEPT point data, T-PEPT demonstrates highly effective performance in multiple-particle tracking, especially in scenarios with high noise. When benchmarked against existing PEPT algorithms using a widely recognised standard framework, T-PEPT consistently maintains sub-millimetre spatial and sub-millisecond temporal precision in nearly all cases, demonstrating its robustness and accuracy. For Data availability for T-PEPT, please use the GitHub repository: https://github.com/uob-positron-imaging-centre/pept.

The mediating role of distress tolerance in the relationship between childhood maltreatment and anxiety in a sample of Lebanese adults

Trajectory prediction via proposal guided transformer with out way attention

Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection

Modular unit design and energy consumption characterization analysis of a novel cantilever robot

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death

Abstract Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their success rate, a substantial challenge in increasing the number of DCD donations resides in the uncertainty regarding the timing of cardiac death after terminal extubation, impacting the risk of prolonged ischemic organ injury, and negatively affecting post-transplant outcomes. In this study, we trained and externally validated an ODE-RNN model, which combines recurrent neural network with neural ordinary equations and excels in processing irregularly-sampled time series data. The model is designed to predict time-to-death following terminal extubation in the intensive care unit (ICU) using the history of clinical observations. Our model was trained on a cohort of 3,238 patients from Yale New Haven Hospital, and validated on an external cohort of 1,908 patients from six hospitals across Connecticut. The model achieved accuracies of $$95.3~\pm ~1.0\%$$ and $$95.4~\pm ~0.7\%$$ for predicting whether death would occur in the first 30 and 60 minutes, respectively, with a calibration error of $$0.024~\pm ~0.009$$ . Heart rate, respiratory rate, mean arterial blood pressure (MAP), oxygen saturation (SpO2), and Glasgow Coma Scale (GCS) scores were identified as the most important predictors. Surpassing existing clinical scores, our model sets the stage for reduced organ acquisition costs and improved post-transplant outcomes.

A study on the spatiotemporal evolutionary pattern and influencing factors of sports venues in Xi’an

First-principles study of electronic and optical response properties of bimetallic-sulphide heterostructure supported dithiocarbonate complex for organic solar cells

Response of river water quality to landscape features in a subtropical hilly region

Appraising non-HDL-C, systolic pressure, and a nomogram-based diagnostic model as auxiliary biomarkers in confirming acute ischemic stroke and transient ischemic attack

Exposure of broiler chickens to deoxynivalenol and Campylobacter jejuni induces substantial changes in intestinal gene expression

Abstract The mycotoxin deoxynivalenol (DON) is of high importance among feed contaminants because of its frequent occurrence in toxicologically relevant concentrations worldwide. Cereal crops, the main component of chicken diet, are commonly contaminated with DON, resulting in frequent exposure of chickens to DON. Likewise, Campylobacter (C.), a pathogen of major public and animal health concern, is frequently found in chicken flocks and poses a threat to the One Health approach. Campylobacter colonizes the gastrointestinal (GI) tract of poultry with a high bacterial load in the caeca. However, the mechanism of C. jejuni colonization in chickens is still not understood albeit it is well known that C. jejuni resides primarily in the mucosal layer of the chicken intestine. Therefore, in the actual study we focused on the effect of exposure to DON and/or C. jejuni on expression profiles of intestinal mucins (MUC1, MUC2), β-defensins (Gallinacin (GAL) 10, 12), cytokines (Toll-like receptor 2 (TLR2), Interleukin (IL) 6, 8, Interferon-γ (IFN)-γ), inducible nitric oxide synthase 2 (iNOS2), as well as selected tight junction proteins (Claudin 5 (CLDN5), Occludin (OCLN), and zonula occludens-1 (ZO1) via RT-qPCR. For this, a total of 150 one-day-old Ross 308 broiler chickens were randomly allocated to six different groups (n = 25 with 5 replicates/group) and were fed for 5 weeks with either contaminated diets (5 or 10 mg DON/kg feed) or basal diets (control). Following oral infection of birds with C. jejuni NCTC 12744 at 14 days of age, several changes in gene expression patterns were demonstrated. A significant (P ≤ 0.05) downregulation of MUC2 mRNA expression was observed in birds fed DON5 and DON10 diet, as well as in birds co-exposed to DON5 and C. jejuni at 7 dpi. Furthermore, at 14 dpi, MUC2 mRNA expression was significantly (P ≤ 0.05) downregulated in birds fed DON (5 mg and 10 mg/kg diet) with and without C. jejuni and in birds infected solely with C. jejuni. The actual study also demonstrated that co-exposure of broiler chickens to DON and C. jejuni resulted in a decreased barrier function via downregulation of OCLD mRNA expression. In addition, Campylobacter infection induced an increased expression of the antimicrobial peptide GAL12 and the IL8 gene, indicating that C. jejuni can initiate an immune response in the chicken gut in a proinflammatory manner. Similarly, DON with and without C. jejuni induced upregulation of GAL10 and GAL12 mRNA expression at 7 dpi. Moreover, no change in iNOS2 mRNA expression was observed in both the jejunum and the cecum at either 7 dpi or 14 dpi, suggesting unchanged NO production during exposure/infection. In conclusion, we confirmed that DON contamination corresponding to the currently applicable EU guidance value of 5 mg DON/kg feed affects the intestinal gene expression profiles of broilers, mainly in a dose-independent manner. Furthermore, DON exposure interacted synergistically with C. jejuni challenge regarding mucins, innate immunity gene expression in either the jejunum or the cecum, suggesting immunomodulatory activity of both foodborne agents (DON and C. jejuni).

Multi skill project scheduling optimization based on quality transmission and rework network reconstruction

Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities

Abstract Rehabilitation-related diseases have long recovery times, making frequent hospital visits impractical for patients. There is a high demand for online rehabilitation advice, but valuable Q&A information in online health communities remains largely untapped, leading to wasted medical resources. This study developed a BERT-BiGRU-attention model to extract three types of entity relationships: disease symptoms, appropriate rehabilitation measures, and inappropriate rehabilitation measures. This model achieved optimal knowledge extraction results. We then used a clustering analysis model to group disease-related knowledge, helping to uncover useful information for rehabilitation patients, assist in medical diagnosis, and enhance health education.

Drivable area recognition on unstructured roads for autonomous vehicles using an optimized bilateral neural network

Nanoparticles with curcumin and piperine modulate steroid biosynthesis in prostate cancer

Abstract Endogenous androgens are pivotal in the development and progression of prostate cancer (PC). We investigated nanoparticle formulations of curcumin and piperine in modulating steroidogenesis within PC cells. Using multiple PC cell lines (LNCaP, VCaP, DU145 and PC3) we studied the effects of curcumin, piperine, and their nanoparticle formulations—curcumin nanoparticles, piperine nanoparticles, and curcumin–piperine nanoparticles (CPN)—on cell viability, migration, and steroid biosynthesis. Curcumin and its nanoparticle formulations significantly reduced cell viability in PC cells, with curcumin–piperine nanoparticles showing the highest efficacy. These treatments also inhibited cell migration, with CPN exhibiting the most pronounced effect. In assays for steroid biosynthesis, curcumin, and its nanoparticle formulations, as well as piperine and its nanoparticles, selectively inhibited 17α-hydroxylase and 17,20-lyase activities of cytochrome P450 17A1 (CYP17A1). Abiraterone, a CYP17A1 inhibitor, displayed a broader inhibition of steroid metabolism including cytochrome P450 21-hydroxylase activity, whereas curcumin and piperine provided a more targeted inhibition profile. Analysis of steroid metabolites by liquid chromatography-mass spectrometry revealed that CPN caused significant reduction of androstenedione and cortisol, suggesting potential synergistic effects. In conclusion, nanoformulations co-loaded with curcumin and piperine offer an effective approach to targeting steroidogenesis and could be promising candidates for therapies aimed at managing androgen-dependent PC.

Exploration potential sepsis–ferroptosis mechanisms through the use of CETSA technology and network pharmacology

Assessment of disease burden and mortality attributable to air pollutants in northwestern Iran using the AirQ+ software

Toward general object search in open reality

Abstract Real-world scenarios are inherently dynamic and open-ended, necessitating that current deep models adapt to general objects in open realities to be practically useful. In this paper, we extend a valuable computer vision task called General Object Search in Open Reality (GOSO). The main objective of GOSO is to determine whether an object from the open world appears in another gallery image, even when composed of arbitrary entities and backgrounds. However, two significant challenges arise: the high scale variance among different instances of the same entity and the vast openness with an ever-expanding set of unknown categories in the open world. To address these issues, we formalize the GOSO problem and propose a simple yet effective architecture named Siamese Exchanged Attention Network (SEA-Net). Specifically, based on a standard siamese structure, SEA-Net introduces a novel branch that comprises multiple stage-stacked Siamese Exchanged Attention (SEA) layers followed by a Hierarchical Feature Fusion (HFF) module, enabling efficient scale adaptation and the extraction of matching-friendly deep features. Moreover, an Open Score Fusion (OSF) module is integrated into SEA-Net during inference to yield a more robust matching score in open-world scenarios. We construct two new evaluation benchmarks suitable for the GOSO task using the existing COCO and LVIS datasets, and extensive experiments consistently demonstrate the effectiveness of the proposed method.

A brain-inspired sequence learning model based on a logic

Abstract Sequence learning is a crucial aspect of intelligence research, with sequence prediction tasks commonly used to evaluate the performance of sequence learning models. This paper introduces and tests a novel sequence learning model that mimics the structure of neocortical mini-columns and is grounded in Non-Axiomatic Logic, offering interpretability. The model’s learning mechanism encompasses three steps: hypothesizing, revising, and recycling, enabling it to operate effectively under conditions of insufficient knowledge and resources. The model’s performance is assessed using synthetic datasets for sequence prediction. The results demonstrate that the model consistently achieves high accuracy across various levels of difficulty, reaching the theoretical maximum. Furthermore, the model’s concept-centered representation effectively avoids catastrophic forgetting, a finding supported by the experimental results.

Standardization of pharmacognostic characters and phytochemical study of Limeum obovatum vicary